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1. Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI -- Integrating Grey and White Matter Information

Author

Samantaray, Tanmayee, Saini, Jitender, Pal, Pramod Kumar, Jaganathan, Bithiah Grace, Saradhi, Vijaya V, and CN, Gupta

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition
Abstract

Thresholding of networks has long posed a challenge in brain connectivity analysis. Weighted networks are typically binarized using threshold measures to facilitate network analysis. Previous studies on MRI-based brain networks have predominantly utilized density or sparsity-based thresholding techniques, optimized within specific ranges derived from network metrics such as path length, clustering coefficient, and small-world index. Thus, determination of a single threshold value for facilitating comparative analysis of networks remains elusive. To address this, our study introduces Mutual K-Nearest Neighbor (MKNN)-based thresholding for brain network analysis. Here, nearest neighbor selection is based on the highest correlation between features of brain regions. Construction of brain networks was accomplished by computing Pearson correlations between grey matter volume and white matter volume for each pair of brain regions. Structural MRI data from 180 Parkinsons patients and 70 controls from the NIMHANS, India were analyzed. Subtypes within Parkinsons disease were identified based on grey and white matter volume atrophy using source-based morphometric decomposition. The loading coefficients were correlated with clinical features to discern clinical relationship with the deciphered subtypes. Our data-mining approach revealed: Subtype A (N = 51, intermediate type), Subtype B (N = 57, mild-severe type with mild motor symptoms), and Subtype AB (N = 36, most-severe type with predominance in motor impairment). Subtype-specific weighted matrices were binarized using MKNN-based thresholding for brain network analysis. Permutation tests on network metrics of resulting bipartite graphs demonstrated significant group differences in betweenness centrality and participation coefficient. The identified hubs were specific to each subtype, with some hubs conserved across different subtypes., Comment: 31 pages, 10 figures, 3 tables

Published
2024

5. Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI

Author

Samantaray, Tanmayee, Gupta, Utsav, Saini, Jitender, and Gupta, Cota Navin

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Neurons and Cognition
Abstract

We propose a novel algorithm called Unique Brain Network Identification Number, UBNIN for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinsons disease PD patients and 70 healthy controls HC from the National Institute of Mental Health and Neurosciences, India. We parcellated each subjects brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node i, weighted by a factor of 2^1-i. The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. This model may be implemented as a neural signature of a persons unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age cohort B and C, C and E, and in HC between E and B, E and C, E and D, and C and D. Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study., Comment: 15 pages, 5 figures,1 algorithm, 1 main table, 1 appendix table

Published
2023
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6. Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5

Author

Kancharla, Vamshi Krishna, Bhattacharya, Debanjali, Sinha, Neelam, Saini, Jitender, Pal, Pramod Kumar, and M, Sandhya

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kinds of inter-hemispheric communication. Thus, subtle changes in CC might be associated with various neurological disorders. The present work proposes the potential of YOLOv5-based CC detection framework to differentiate atypical Parkinsonian disorders (PD) from healthy controls (HC). With 3 rounds of hold-out validation, mean classification accuracy of 92% is obtained using the proposed method on a proprietary dataset consisting of 20 healthy subjects and 20 cases of APDs, with an improvement of 5% over SOTA methods (CC morphometry and visual texture analysis) that used the same dataset. Subsequently, in order to incorporate the explainability of YOLO predictions, Eigen CAM based heatmap is generated for identifying the most important sub-region in CC that leads to the classification. The result of Eigen CAM showed CC mid-body as the most distinguishable sub-region in classifying APDs and HC, which is in-line with SOTA methodologies and the current prevalent understanding in medicine.

Published
2023

8. Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J, Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y, Chang, Ken, Balana, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S, Lombardo, Joseph, Palmer, Joshua D, Flanders, Adam E, Dicker, Adam P, Sair, Haris I, Jones, Craig K, Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y, Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A, Mitchell, J Ross, Farinhas, Joaquim, Maldjian, Joseph A, Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C, Reddy, Divya, Holcomb, James, Wagner, Benjamin C, Ellingson, Benjamin M, Cloughesy, Timothy F, Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B, Teixeira, Bernardo C A, Sprenger, Flávia, Menotti, David, Lucio, Diego R, LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W, McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E, Vadmal, Vachan, Waite, Kristin, Colen, Rivka R, Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V M, Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I, Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M, Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R, Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten MJ, Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W, Dubbink, Hendrikus J, Vincent, Arnaud JPE, Bent, Martin J van den, French, Pim J, Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P, Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B, Mistry, Akshitkumar, Thompson, Reid C, Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C, Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G H, Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M, Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F, Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M, Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A, Ogbole, Godwin, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu'aibu, Mustapha, Dorcas, Adeleye, Soneye, Mayowa, Dako, Farouk, Simpson, Amber L, Hamghalam, Mohammad, Peoples, Jacob J, Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y, Boss, Michael A, Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S, Martin, Jason, and Bakas, Spyridon

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing., Comment: federated learning, deep learning, convolutional neural network, segmentation, brain tumor, glioma, glioblastoma, FeTS, BraTS

Published
2022
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10. Yield of video electro encephalography for phase 1 pre-surgical evaluation of drug resistant epilepsy in 1200 adults: retrospective study from a tertiary center situated in a lower-middle-income country

Author

Shrivastava, Mayank, Asranna, Ajay, Kenchiah, Raghavendra, Mundlamuri, Ravindranath, Viswanathan, Lakshminarayanapuram G., Kulanthaivelu, Karthik, Bharath, Rose Dawn, Saini, Jitender, Sadashiva, Nishanth, Arimappamagan, Arivazhagan, Mahadevan, Anita, Rajeswaran, Jamuna, Malla, Bhaskara Rao, and Sinha, Sanjib

Published
2023
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14. Author Correction: Federated learning enables big data for rare cancer boundary detection

Author

Pati, Sarthak, Baid, Ujjwal, Edwards, Brandon, Sheller, Micah, Wang, Shih-Han, Reina, G. Anthony, Foley, Patrick, Gruzdev, Alexey, Karkada, Deepthi, Davatzikos, Christos, Sako, Chiharu, Ghodasara, Satyam, Bilello, Michel, Mohan, Suyash, Vollmuth, Philipp, Brugnara, Gianluca, Preetha, Chandrakanth J., Sahm, Felix, Maier-Hein, Klaus, Zenk, Maximilian, Bendszus, Martin, Wick, Wolfgang, Calabrese, Evan, Rudie, Jeffrey, Villanueva-Meyer, Javier, Cha, Soonmee, Ingalhalikar, Madhura, Jadhav, Manali, Pandey, Umang, Saini, Jitender, Garrett, John, Larson, Matthew, Jeraj, Robert, Currie, Stuart, Frood, Russell, Fatania, Kavi, Huang, Raymond Y., Chang, Ken, Balaña, Carmen, Capellades, Jaume, Puig, Josep, Trenkler, Johannes, Pichler, Josef, Necker, Georg, Haunschmidt, Andreas, Meckel, Stephan, Shukla, Gaurav, Liem, Spencer, Alexander, Gregory S., Lombardo, Joseph, Palmer, Joshua D., Flanders, Adam E., Dicker, Adam P., Sair, Haris I., Jones, Craig K., Venkataraman, Archana, Jiang, Meirui, So, Tiffany Y., Chen, Cheng, Heng, Pheng Ann, Dou, Qi, Kozubek, Michal, Lux, Filip, Michálek, Jan, Matula, Petr, Keřkovský, Miloš, Kopřivová, Tereza, Dostál, Marek, Vybíhal, Václav, Vogelbaum, Michael A., Mitchell, J. Ross, Farinhas, Joaquim, Maldjian, Joseph A., Yogananda, Chandan Ganesh Bangalore, Pinho, Marco C., Reddy, Divya, Holcomb, James, Wagner, Benjamin C., Ellingson, Benjamin M., Cloughesy, Timothy F., Raymond, Catalina, Oughourlian, Talia, Hagiwara, Akifumi, Wang, Chencai, To, Minh-Son, Bhardwaj, Sargam, Chong, Chee, Agzarian, Marc, Falcão, Alexandre Xavier, Martins, Samuel B., Teixeira, Bernardo C. A., Sprenger, Flávia, Menotti, David, Lucio, Diego R., LaMontagne, Pamela, Marcus, Daniel, Wiestler, Benedikt, Kofler, Florian, Ezhov, Ivan, Metz, Marie, Jain, Rajan, Lee, Matthew, Lui, Yvonne W., McKinley, Richard, Slotboom, Johannes, Radojewski, Piotr, Meier, Raphael, Wiest, Roland, Murcia, Derrick, Fu, Eric, Haas, Rourke, Thompson, John, Ormond, David Ryan, Badve, Chaitra, Sloan, Andrew E., Vadmal, Vachan, Waite, Kristin, Colen, Rivka R., Pei, Linmin, Ak, Murat, Srinivasan, Ashok, Bapuraj, J. Rajiv, Rao, Arvind, Wang, Nicholas, Yoshiaki, Ota, Moritani, Toshio, Turk, Sevcan, Lee, Joonsang, Prabhudesai, Snehal, Morón, Fanny, Mandel, Jacob, Kamnitsas, Konstantinos, Glocker, Ben, Dixon, Luke V. M., Williams, Matthew, Zampakis, Peter, Panagiotopoulos, Vasileios, Tsiganos, Panagiotis, Alexiou, Sotiris, Haliassos, Ilias, Zacharaki, Evangelia I., Moustakas, Konstantinos, Kalogeropoulou, Christina, Kardamakis, Dimitrios M., Choi, Yoon Seong, Lee, Seung-Koo, Chang, Jong Hee, Ahn, Sung Soo, Luo, Bing, Poisson, Laila, Wen, Ning, Tiwari, Pallavi, Verma, Ruchika, Bareja, Rohan, Yadav, Ipsa, Chen, Jonathan, Kumar, Neeraj, Smits, Marion, van der Voort, Sebastian R., Alafandi, Ahmed, Incekara, Fatih, Wijnenga, Maarten M. J., Kapsas, Georgios, Gahrmann, Renske, Schouten, Joost W., Dubbink, Hendrikus J., Vincent, Arnaud J. P. E., van den Bent, Martin J., French, Pim J., Klein, Stefan, Yuan, Yading, Sharma, Sonam, Tseng, Tzu-Chi, Adabi, Saba, Niclou, Simone P., Keunen, Olivier, Hau, Ann-Christin, Vallières, Martin, Fortin, David, Lepage, Martin, Landman, Bennett, Ramadass, Karthik, Xu, Kaiwen, Chotai, Silky, Chambless, Lola B., Mistry, Akshitkumar, Thompson, Reid C., Gusev, Yuriy, Bhuvaneshwar, Krithika, Sayah, Anousheh, Bencheqroun, Camelia, Belouali, Anas, Madhavan, Subha, Booth, Thomas C., Chelliah, Alysha, Modat, Marc, Shuaib, Haris, Dragos, Carmen, Abayazeed, Aly, Kolodziej, Kenneth, Hill, Michael, Abbassy, Ahmed, Gamal, Shady, Mekhaimar, Mahmoud, Qayati, Mohamed, Reyes, Mauricio, Park, Ji Eun, Yun, Jihye, Kim, Ho Sung, Mahajan, Abhishek, Muzi, Mark, Benson, Sean, Beets-Tan, Regina G. H., Teuwen, Jonas, Herrera-Trujillo, Alejandro, Trujillo, Maria, Escobar, William, Abello, Ana, Bernal, Jose, Gómez, Jhon, Choi, Joseph, Baek, Stephen, Kim, Yusung, Ismael, Heba, Allen, Bryan, Buatti, John M., Kotrotsou, Aikaterini, Li, Hongwei, Weiss, Tobias, Weller, Michael, Bink, Andrea, Pouymayou, Bertrand, Shaykh, Hassan F., Saltz, Joel, Prasanna, Prateek, Shrestha, Sampurna, Mani, Kartik M., Payne, David, Kurc, Tahsin, Pelaez, Enrique, Franco-Maldonado, Heydy, Loayza, Francis, Quevedo, Sebastian, Guevara, Pamela, Torche, Esteban, Mendoza, Cristobal, Vera, Franco, Ríos, Elvis, López, Eduardo, Velastin, Sergio A., Ogbole, Godwin, Soneye, Mayowa, Oyekunle, Dotun, Odafe-Oyibotha, Olubunmi, Osobu, Babatunde, Shu’aibu, Mustapha, Dorcas, Adeleye, Dako, Farouk, Simpson, Amber L., Hamghalam, Mohammad, Peoples, Jacob J., Hu, Ricky, Tran, Anh, Cutler, Danielle, Moraes, Fabio Y., Boss, Michael A., Gimpel, James, Veettil, Deepak Kattil, Schmidt, Kendall, Bialecki, Brian, Marella, Sailaja, Price, Cynthia, Cimino, Lisa, Apgar, Charles, Shah, Prashant, Menze, Bjoern, Barnholtz-Sloan, Jill S., Martin, Jason, and Bakas, Spyridon

Published
2023
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15. Role of magnetoencephalography in predicting the epileptogenic zone and post-operative seizure outcome - A retrospective study

Author

Sindhu, Dodmalur Mallikarjuna, Mundlamuri, Ravindranadh Chowdary, Goutham, Bhargava, Narayanan, Mariyappa, Raghavendra, Kenchaiah, Asranna, Ajay, Vishwanathan, Lakshminarayanapuram Gopal, Kulanthaivelu, Karthik, Saini, Jitender, Mangalore, Sandhya, Bharath, Rose Dawn, Sadashiva, Nishanth, Mahadevan, Anita, Jamuna, Rajeswaran, Arivazhagan, Arimappamagan, Rao, Malla Bhaskara, and Sinha, Sanjib

Published
2023
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16. Role of Optical Coherence Tomography in Predicting Visual Outcome after Surgery for Sellar and Supra-Sellar Tumors

Author

Singha, Souvik, Beniwal, Manish, Mailankody, Pooja, Battu, Rajani, Saini, Jitender, Tyagi, Gaurav, and Srinivas, Dwarakanath

Subjects
Tumors -- Prognosis -- Research, Tomography -- Research, Health
Abstract

Byline: Souvik. Singha, Manish. Beniwal, Pooja. Mailankody, Rajani. Battu, Jitender. Saini, Gaurav. Tyagi, Dwarakanath. Srinivas Background: Almost one-fifth of patients undergoing surgery for sellar/supra-sellar tumors do not gain a significant [...]

Published
2024

21. HR-CAM: Precise Localization of Pathology Using Multi-level Learning in CNNs

Author

Shinde, Sumeet, Chougule, Tanay, Saini, Jitender, and Ingalhalikar, Madhura

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a CNN based technique that aggregates feature maps from its multiple layers that can localize abnormalities with greater details as well as predict pathology under consideration. Existing class activation mapping (CAM) techniques extract feature maps from either the final layer or a single intermediate layer to create the discriminative maps and then interpolate to upsample to the original image resolution. In this case, the subject specific localization is coarse and is unable to capture subtle abnormalities. To mitigate this, our method builds a novel CNN based discriminative localization model that we call high resolution CAM (HR-CAM), which accounts for layers from each resolution, therefore facilitating a comprehensive map that can delineate the pathology for each subject by combining low-level, intermediate as well as high-level features from the CNN. Moreover, our model directly provides the discriminative map in the resolution of the original image facilitating finer delineation of abnormalities. We demonstrate the working of our model on a simulated abnormalities data where we illustrate how the model captures finer details in the final discriminative maps as compared to current techniques. We then apply this technique: (1) to classify ependymomas from grade IV glioblastoma on T1-weighted contrast enhanced (T1-CE) MRI and (2) to predict Parkinson's disease from neuromelanin sensitive MRI. In all these cases we demonstrate that our model not only predicts pathologies with high accuracies, but also creates clinically interpretable subject specific high resolution discriminative localizations. Overall, the technique can be generalized to any CNN and carries high relevance in a clinical setting., Comment: Medical Image Computing and Computer Assisted Intervention, 2019

Published
2019

25. Magnetoencephalography imaging of high frequency oscillations strengthens presurgical localization and outcome prediction

Author

Velmurugan, Jayabal, Nagarajan, Srikantan S, Mariyappa, Narayanan, Mundlamuri, Ravindranadh C, Raghavendra, Kenchaiah, Bharath, Rose Dawn, Saini, Jitender, Arivazhagan, Arimappamagan, Rajeswaran, Jamuna, Mahadevan, Anita, Malla, Bhaskara Rao, Satishchandra, Parthasarathy, and Sinha, Sanjib

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Neurosciences, Brain Disorders, Neurodegenerative, Epilepsy, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Adolescent, Adult, Biomarkers, Brain Mapping, Cerebral Cortex, Cohort Studies, Drug Resistant Epilepsy, Female, Humans, Magnetic Resonance Imaging, Magnetoencephalography, Male, Neuroimaging, Neurosurgical Procedures, Predictive Value of Tests, Prospective Studies, ROC Curve, Treatment Outcome, Young Adult, magnetoencephalography, high frequency oscillation, ripple, biomarker, surgical outcome, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery, Biomedical and clinical sciences, Health sciences, Psychology
Abstract

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20-50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80-200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient's surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ2 = 5.22; P = 0.02; φ = -0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient's surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Published
2019

26. Corpus Callosotomy for Non-Localizing Drug Resistant Epilepsy with Drop Attacks

Author

Sadashiva, Nishanth, Kadam, Raju, Arimappamagan, Arivazhagan, Rao, Malla Bhaskara, Mundlamuri, Ravindranadh Chowdary, Raghavendra, Kenchaiah, Asranna, Ajay, Viswanathan, Lakshminarayanapuram Gopal, Mariyappa, Narayanan, Kulanthaivelu, Karthik, Mangalore, Sandhya, Nagaraj, Chandana, Saini, Jitender, Bharath, Rose Dawn, Rajeswaran, Jamuna, Mahadevan, Anita, Satishchandra, Parthasarathy, and Sinha, Sanjib

Published
2023
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32. Assessment of De-noising Filters for Brain MRI T1-Weighted Contrast-Enhanced Images

Author

Sarita, Dass, Rajeshwar, Saini, Jitender, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Marriwala, N., editor, Tripathi, C. C., editor, Jain, Shruti, editor, and Mathapathi, Shivakumar, editor

Published
2022
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34. Longitudinally extensive transverse myelitis with optic neuritis related to profound biotinidase deficiency: NMOSD mimic!

Author

Ravikumar, Nagaraj, Dhar, Debjyoti, Mahale, Rohan, Reddy, Ambati, Shreedevi, Athyadi, Prabhu, Sameetha, Saini, Jitender, Christopher, Rita, Mailankody, Pooja, Mathuranath, P., and Padmanabha, Hansashree

Subjects
Biotin -- Physiological aspects, Health
Abstract

Byline: Nagaraj. Ravikumar, Debjyoti. Dhar, Rohan. Mahale, Ambati. Reddy, Athyadi. Shreedevi, Sameetha. Prabhu, Jitender. Saini, Rita. Christopher, Pooja. Mailankody, P. Mathuranath, Hansashree. Padmanabha Dear Sir, Biotinidase deficiency is an autosomal [...]

Published
2023

35. Cross-sectional area reference values of nerves in the upper and lower extremities using ultrasonography in the Indian population

Author

Sindhu, D., Huddar, Akshata, Saini, Jitender, Vengalil, Seena, Nashi, Saraswati, Bardhan, Mainak, Unnikrishnan, Gopikrishnan, Rajula, Rahul, Kandavel, Thennarasu, Bathala, Lokesh, Visser, Leo, and Nalini, Atchayaram

Subjects
Extremities (Anatomy) -- Medical examination, Nervous system diseases -- Diagnosis, Nerves, Peripheral -- Medical examination, Diagnosis, Ultrasonic -- Methods, Health
Abstract

Byline: D. Sindhu, Akshata. Huddar, Jitender. Saini, Seena. Vengalil, Saraswati. Nashi, Mainak. Bardhan, Gopikrishnan. Unnikrishnan, Rahul. Rajula, Thennarasu. Kandavel, Lokesh. Bathala, Leo. Visser, Atchayaram. Nalini Background and Purpose: Cross-sectional area [...]

Published
2022

36. Sample size requirement for achieving multisite harmonization using structural brain MRI features

Author

Viswanath, Biju, Rao, Naren P., Narayanaswamy, Janardhanan C., Sivakumar, Palanimuthu T., Kandasamy, Arun, Kesavan, Muralidharan, Mehta, Urvakhsh Meherwan, Mukherjee, Odity, Purushottam, Meera, Mehta, Bhupesh, Kandavel, Thennarasu, Binukumar, B., Saini, Jitender, Jayarajan, Deepak, Shyamsundar, A., Moirangthem, Sydney, Vijay Kumar, K.G., Mahadevan, Jayant, Holla, Bharath, Thirthalli, Jagadisha, Gangadhar, Bangalore N., Murthy, Pratima, Panicker, Mitradas M., Bhalla, Upinder S., Chattarji, Sumantra, Benegal, Vivek, Varghese, Mathew, Reddy, Janardhan Y.C., Raghu, Padinjat, Rao, Mahendra, Jain, Sanjeev, Parekh, Pravesh, Vivek Bhalerao, Gaurav, John, John P., and Venkatasubramanian, G.

Published
2022
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41. Coronavirus Disease 2019 (COVID-19)–Associated Rhino-Orbito-Cerebral Mucormycosis: A Multi-Institutional Retrospective Study of Imaging Patterns

Author

Kumar GG, Sharath, Deepalam, Saikant, Siddiqui, Ata, Adiga, Chaitra P., Kumar, Savith, Shivalingappa, Shivakumar Swamy, Acharya, Ullas V., Goolahally, Lakshmikanth N., Sharma, Saksham, Andrew, Dhilip, Hosmani, Pradeep, Nair, Satish, Medikeri, Gaurav, Rao, Ravi Mohan, Agadi, Jagadish B., Kumar, Sujit, Adoor, Gurucharan, Sharma, Suryanarayana, Hegde, Raghuraj, Saini, Jitender, and Kulanthaivelu, Karthik

Published
2022
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45. Automated lateralization of temporal lobe epilepsy with cross frequency coupling using magnetoencephalography

Author

Gautham, Bhargava K., Mukherjee, Joydeep, Narayanan, Mariyappa, Kenchaiah, Raghavendra, Mundlamuri, Ravindranadh C, Asranna, Ajay, Lakshminarayanapuram, Viswanathan G., Bharath, Rose D., Saini, Jitender, Nagaraj, Chandana, Mangalore, Sandhya, Kulanthaivelu, Karthik, Sadashiva, Nishanth, Mahadevan, Anita, Rajan, Jamuna, Kumar, Keshav, Arimappamagan, Arivazaghan, Malla, Bhaskara R., and Sinha, Sanjib

Published
2022
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46. Dysregulated Cerebrospinal Fluid Proteome of Spinocerebellar Ataxia Type 2 and its Clinical Implications.

Author

Stezin, Albert, Sathe, Gajanan J., Gajbhiye, Akshada, Bharadwaj, Sujas, Ghose, Vivek, Bellad, Anikha, Malo, Palash Kumar, Holla, Vikram, Hegde, Shantala, Bharath, Rose Dawn, Saini, Jitender, Jain, Sanjeev, Yadav, Ravi, Pandey, Akhilesh, and Pal, Pramod Kumar

Abstract

Background: Abnormalities in ataxin‐2 associated with spinocerebellar ataxia type 2 (SCA2) may lead to widespread disruptions in the proteome. This study was performed to identify dysregulated proteome in SCA2 and to explore its clinical‐radiological correlations. Methods: Cerebrospinal fluid (CSF) samples from 21 genetically confirmed SCA2 were subjected to shotgun proteome analysis using mass spectrometry (MS) and tandem mass tag (TMT)‐based multiplexing. Proteins with at least 1.5‐fold change in abundance were identified. Their relative abundance was measured using parallel reaction monitoring (PRM) and correlated against disease‐related factors. Results: Eleven proteins were significantly upregulated in SCA2. They belonged to the family of cell adhesion molecules and granins. Their fold changes showed significant clinical, genetic, and radiological correlations. Conclusions: Significant dysregulation of CSF proteome is seen in SCA2. The dysregulated protein may have potential use in clinical evaluation of patients with SCA2. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Radial Cumulative Frequency Distribution: A New Imaging Signature to Detect Chromosomal Arms 1p/19q Co-deletion Status in Glioma

Author

Bhattacharya, Debanjali, Sinha, Neelam, Saini, Jitender, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Satish Kumar, editor, Roy, Partha, editor, Raman, Balasubramanian, editor, and Nagabhushan, P., editor

Published
2021
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