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Comparison of seven modelling algorithms for GABA-edited 1H-MRS [article]

Alexander R. Craven, Pallab K. Bhattacharyya, William T. Clarke, Ulrike Dydak, Richard A. E. Edden, Lars Ersland, Pravat K. Mandal, Mark Mikkelsen, James B. Murdoch, Jamie Near, Reuben Rideaux, Deepika Shukla (+5 others)
2021 bioRxiv   pre-print
Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS,
more » ... AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.
doi:10.1101/2021.11.15.468534 fatcat:kemzc47oifanpbn7vrlsjk2b2i

Comparison of seven modelling algorithms for GABA‐edited 1 H‐MRS

Alexander R. Craven, Pallab K. Bhattacharyya, William T. Clarke, Ulrike Dydak, Richard A. E. Edden, Lars Ersland, Pravat K. Mandal, Mark Mikkelsen, James B. Murdoch, Jamie Near, Reuben Rideaux, Deepika Shukla (+5 others)
2022 NMR in Biomedicine  
Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS,
more » ... AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.
doi:10.1002/nbm.4702 pmid:35078266 pmcid:PMC9203918 fatcat:paik7nzjjbh3ve5spmvk6boyjq

Comparison of Multivendor Single-Voxel MR Spectroscopy Data Acquired in Healthy Brain at 26 Sites

Michal Považan, Mark Mikkelsen, Adam Berrington, Pallab K. Bhattacharyya, Maiken K. Brix, Pieter F. Buur, Kim M. Cecil, Kimberly L. Chan, David Y.T. Chen, Alexander R. Craven, Koen Cuypers, Michael Dacko (+9 others)
2020 Radiology  
Bigley of the University of Sheffield MRI Unit for her assistance with data acquisition.Complete list of authors:Michal Považan, PhD; Mark Mikkelsen, PhD; Adam Berrington, PhD; Pallab K.  ...  Bhattacharyya, PhD; Maiken K. Brix, MD; Pieter F. Buur, PhD; Kim M. Cecil, PhD; Kimberly L. Chan, PhD; David Y.T. Chen, MD; Alexander R. Craven, MS; Koen Cuypers, PhD; Michael Dacko, PhD; Niall W.  ... 
doi:10.1148/radiol.2020191037 pmid:32043950 pmcid:PMC7104702 fatcat:i7rl3z6dfbhrjnu5dc37nmjh3i

In vivo magnetic resonance spectroscopy measurement of gray-matter and white-matter gamma-aminobutyric acid concentration in sensorimotor cortex using a motion-controlled MEGA point-resolved spectroscopy sequence

Pallab K. Bhattacharyya, Micheal D. Phillips, Lael A. Stone, Mark J. Lowe
2011 Magnetic Resonance Imaging  
Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter in the brain. Understanding the GABA concentration, in vivo, is important to understand normal brain function. Using MEGA point resolved spectroscopy (MEGA-PRESS) sequence with interleaved water scans to detect subject motion, GABA level of sensorimotor cortex was measured using a voxel was identified from a functional MRI scan. The GABA level in a 20 × 20 × 20 mm 3 voxel consisting of 37 ± 7% GM, 52 ± 12% WM, and 11 ± 8% CSF
more » ... in the sensorimotor region was measured to be 1.43 ± 0.48 mM. In addition, using linear regression analysis, GABA concentrations within gray and white matter were calculated to be 2.87 ± 0.61 and 0.33 ± 0.11 mM, respectively.
doi:10.1016/j.mri.2010.10.009 pmid:21232891 pmcid:PMC3078577 fatcat:xf5l6qnvxbelbjnwyum2g2voku

Curcumin Enhances the Efficacy of Chemotherapy by Tailoring p65NFκB-p300 Cross-talk in Favor of p53-p300 in Breast Cancer

Gouri Sankar Sen, Suchismita Mohanty, Dewan Md Sakib Hossain, Sankar Bhattacharyya, Shuvomoy Banerjee, Juni Chakraborty, Shilpi Saha, Pallab Ray, Pushpak Bhattacharjee, Debaprasad Mandal, Arindam Bhattacharya, Samit Chattopadhyay (+2 others)
2011 Journal of Biological Chemistry  
Jana S; Patra K; Sarkar S; Jana J; Mukherjee G; Bhattacharjee S*; Mandal DP* (2014).  ...  Bhattacharyya A, Mandal D, Lahiry L, Bhattacharyya S, Chattopadhyay S, Ghosh UK, Sa G, Das T.  ...  INVITED TALK Antitumorigenic potential of linalool: a spice principle with a promise Jana S, Patra K, Sarkar S, Jana  ... 
doi:10.1074/jbc.m111.262295 pmid:22013068 pmcid:PMC3234918 fatcat:l3mz4wcoxfbo5nvycdymk5vo3y

Big GABA: Edited MR spectroscopy at 24 research sites

Mark Mikkelsen, Peter B. Barker, Pallab K. Bhattacharyya, Maiken K. Brix, Pieter F. Buur, Kim M. Cecil, Kimberly L. Chan, David Y.-T. Chen, Alexander R. Craven, Koen Cuypers, Michael Dacko, Niall W. Duncan (+48 others)
2017 NeuroImage  
three-level unconditional linear mixed-effects model to the GABA+ and MM-suppressed GABA data: [1] where y ijk is the observed GABA measurement for participant i at site j on a scanner manufactured by vendor k,  ... 
doi:10.1016/j.neuroimage.2017.07.021 pmid:28716717 pmcid:PMC5700835 fatcat:t5ernqj6s5cdfhq2twugxt5jjm

Big GABA II: Water-referenced edited MR spectroscopy at 25 research sites

Mark Mikkelsen, Daniel L. Rimbault, Peter B. Barker, Pallab K. Bhattacharyya, Maiken K. Brix, Pieter F. Buur, Kim M. Cecil, Kimberly L. Chan, David Y.-T. Chen, Alexander R. Craven, Koen Cuypers, Michael Dacko (+54 others)
2019 NeuroImage  
Barker a,b , Pallab K. Bhattacharyya d,e , Maiken K. Brix f , Pieter F. Buur g , Kim M. Cecil h , Kimberly L. Affiliations a Russell H.  ... 
doi:10.1016/j.neuroimage.2019.02.059 pmid:30840905 pmcid:PMC6818968 fatcat:xgcppuidyrddhjprqf3o7pi5fm

ICCE 2020 List Reviewer Page

2020 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)  
K.  ...  Shyama Prasad Mukherjee University Rajarshi Bhattacharyya, NIT Patna Ranjana Rajnish, Amity University, Lucknow G.  ... 
doi:10.1109/icce50343.2020.9290593 fatcat:t7tvnod5avgrzgsa3nwt4wxnxe

Service Notes

1944 The Indian medical gazette  
Gobinda Pallab Ghosh. Sunilchandra Datta. Sunil Kumar Sen. Amal Kumar Datta. Jagannath Chatterjee. Pratul Chandra Sinha Bhupaty Banerjee. Roy.  ...  Captain K. Krishnaswamy. Dated 18th June, 1943. To be Captains Doraisami Sankaran. Dated 9th August, 1943. Priya Gopal Bhattacharya. Dated 18th August, 1943.  ... 
pmid:29011986 pmcid:PMC5155732 fatcat:p3x5j6z6j5gcnngysmhdwbuzua

Message from General Chairs

2012 2012 International Symposium on Electronic System Design (ISED)  
Bhattacharyya for readily accepting our invitation to deliver keynote/invited talks and for arranging special sessions at the symposium.  ...  We would like to thank Tutorial Chairs -Parthasarathi Dasgupta and Pallab Dasgupta, Workshop Chairs -Santan Chattopadhyay and Suman Chakraborty, Fellowship Chair -P. P.  ... 
doi:10.1109/ised.2012.4 fatcat:u6lkvg3bang5pdtkktpb5a56ra

ICCE 2020 TOC

2020 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)  
Mondal, Dipankar Bose and Dilip K Singh 26-31 7 22 Fundamentals of Electric Resistance Friction Stir Welding of Metals: A Review Kaushik Sengupta, Arpan K Mondal, Dipankar Bose, Dilip K Singh  ...  Biswas, Sushanta Sarkar, Partha Pratim Sarkar 78-82 17 47 Experimental Analysis the Tissue Deformation of Needle Tissue Insertion Process in Tissue Engineering Ranjit Barua, Sudipto Datta, Pallab  ... 
doi:10.1109/icce50343.2020.9290745 fatcat:eln5gj5hr5chddqhecxlm7lpda

Table of Contents

2021 Neuromodulation (Malden, Mass.)  
Beall, PhD; Pallab Bhattacharyya, PhD; Xuemei Huang, MS; Jian Lin, MS; Jacqueline Chen, PhD; Mark J. Lowe, PhD; Donald A. Malone, MD; Andre G.  ...  Kahl, PhD; Joachim K.  ... 
doi:10.1111/ner.13186 fatcat:yv7eayymavbuzbzxuuelruz4gm

Surveillance of Healthcare-Associated Bloodstream and Urinary Tract Infections in a National Level Network of Indian Hospitals

Purva Mathur, Paul Malpiedi, Kamini Walia, Rajesh Malhotra, Padmini Srikantiah, Omika Katoch, Sonal Katyal, Surbhi Khurana, Mahesh Chandra Misra, Sunil Gupta, Subodh Kumar, Sushma Sagar (+71 others)
2020 Infection control and hospital epidemiology  
The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia.  ...  The surveillance platform identified 2 separate hospital-level HAI outbreaks; one caused by colistin-resistant K. pneumoniae and another due to Burkholderia cepacia.  ... 
doi:10.1017/ice.2020.1043 fatcat:auhbrstef5edfclzshadqdnivy

Frequency Drift in MR Spectroscopy at 3T

Steve C.N. Hui, Mark Mikkelsen, Helge J. Zöllner, Vishwadeep Ahluwalia, Sarael Alcauter, Laima Baltusis, Deborah A. Barany, Laura R. Barlow, Robert Becker, Jeffrey I. Berman, Adam Berrington, Pallab K. Bhattacharyya (+121 others)
2021 NeuroImage  
Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major
more » ... dors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.
doi:10.1016/j.neuroimage.2021.118430 pmid:34314848 pmcid:PMC8456751 fatcat:2ckrvz2ncfc4vmu6o5oh7pnedu

Long Term Fertilizer Management Effect on Nutrient Dynamics in Rainfed Rice-lentil System in Transect 4 of IndoGangetic Plain

Jitendra Kumar, Nirmal De, R. S. Meena, Pallab Sharma, A. K. Pradhan, G. Ravindra Chari
2019 International Journal of Plant & Soil Science  
Ghosh BN, Meenab VS, Alama NM, Dograa P, Bhattacharyya R, Sharma NK, Mishra PK.  ...  Bhattacharyya P, Roy KS, Das M, Ray S, Balachandar D, Karthikeyan S, Nayak AK, Mohapatra T.  ... 
doi:10.9734/ijpss/2018/v26i630057 fatcat:72oo4mkhord3tpe22lajsno7q4
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