scholarly journals Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy

Epilepsia ◽  
2021 ◽  
Author(s):  
Alexander C. Whiting ◽  
Marcia Morita‐Sherman ◽  
Manshi Li ◽  
Deborah Vegh ◽  
Brunno Machado de Campos ◽  
...  
2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Emily Iannopollo ◽  
Ryan Plunkett ◽  
Kara Garcia

Background and Hypothesis: Magnetic resonance imaging (MRI) has become a useful tool in monitoring the progression of Alzheimer's disease. Previous surface-based analysis has focused on changes in cortical thickness associated with the disease1. The objective of this study is to analyze MRI-derived cortical reconstructions for patterns of atrophy in terms of both cortical thickness and cortical volume. We hypothesize that Alzheimer’s Disease progression will be associated with a more significant change in volume than thickness. Experimental Design or Project Methods: MRI data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). All subjects with baseline and two-year 3T MRI scans were included. Segmentation of MRIs into gray and white matter was performed with FreeSurfer2,3,4,5. Subjects whose scans did not segment accurately were excluded. Surfaces were then registered to a common atlas with Ciftify6, and anatomically-constrained Multimodal Surface Matching (aMSM) was used to analyze longitudinal changes in each subject7. This produced continuous surface maps showing changes in cortical surface area and thickness. These maps were multiplied to create cortical volume maps8. Permutation Analysis of Linear Models (PALM) was used to perform two-sample t-tests comparing the maps of the Alzheimer’s and control groups9. Results: Preliminary analysis of nine Alzheimer’s subjects and nine control subjects produced surface maps displaying patterns that were expected given previous research findings10,11. There was increased volume and thickness loss in Alzheimer’s subjects relative to controls, with relatively high loss in structures of the medial temporal lobe. Future analysis of a larger sample will determine whether statistically significant differences exist between the Alzheimer’s and control groups in terms of thickness loss and volume loss. Conclusion and Potential Impact: If significant results are found, surface-based analysis of cortical volume may allow for detection of atrophy at an earlier stage in disease progression than would be possible based on cortical thickness.   References 1. Clarkson MJ, Cardoso MJ, Ridgway GR, Modat M, Leung KK, Rohrer JD, Fox NC, Ourselin S. A comparison of voxel and surface based cortical thickness estimation methods. NeuroImage. 2011 Aug 1; 57(3):856-65. 2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179194. 3. Fischl B, Sereno M, Dale A. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207.  4. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33:341-355. 5. Fischl B, Salat DH, van der Kouwe AJ, Makris N, Segonne F, Quinn BT, Dale AM. Sequence-independent segmentation of magnetic resonance images. Neuroimage 2004;23 Suppl 1:S69-84. 6. Glasser MF, Sotiropoulos SN, Wilson JA, Coalson TS, Fischl B, Andersson JL, Xu J, Jbabdi S, Webster M, Polimeni JR, Van Essen DC, Jenkinson M, WU-Minn HCP Consortium. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage. 2013 Oct 15;80:105-24. 7. Robinson EC, Garcia K, Glasser MF, Chen Z, Coalson TS, Makropoulos A, Bozek J, Wright R, Schuh A, Webster M, Hutter J, Price A, Cordero Grande L, Hughes E, Tusor N, Bayly PV, Van Essen DC, Smith SM, Edwards AD, Hajnal J, Jenkinson M, Glocker B, Rueckert D. Multimodal surface matching with higher-order smoothness constraints. Neuroimage. 2018;167:453-65. 8. Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and data mining tools and strategies for the human connectome project. Front Neuroinform 2011;5:4. 9. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. NeuroImage, 2014;92:381-397 10. Matsuda, H. MRI morphometry in Alzheimer’s disease. Ageing Research Reviews. 2016 Sep;30:17-24. 11. Risacher SL, Shen L, West JD, Kim S, McDonald BC, Beckett LA, Harvey DJ, Jack CR Jr, Weiner MW, Saykin AJ. Alzheimer's Disease Neuroimaging Initiative (ADNI). Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort. Neurobiol Aging. 2010 Aug;31(8):1401-18. 


2012 ◽  
Vol 8 (4S_Part_1) ◽  
pp. P33-P33
Author(s):  
Tammie Benzinger ◽  
Gina D'Angelo ◽  
Tyler Blazey ◽  
Gongfu Zhou ◽  
Beau Ances ◽  
...  

Bone ◽  
2018 ◽  
Vol 108 ◽  
pp. 186-192 ◽  
Author(s):  
Elisa A. Marques ◽  
Martine Elbejjani ◽  
Vilmundur Gudnason ◽  
Gunnar Sigurdsson ◽  
Thomas Lang ◽  
...  

2016 ◽  
Vol 92 ◽  
pp. 166-174 ◽  
Author(s):  
Leah H. Rubin ◽  
Vanessa J. Meyer ◽  
Rhoda J. Conant ◽  
Erin E. Sundermann ◽  
Minjie Wu ◽  
...  

2012 ◽  
Vol 8 (4S_Part_1) ◽  
pp. P32-P32
Author(s):  
Sofie Adriaanse ◽  
Koene van Dijk ◽  
Rik Ossenkoppele ◽  
Martin Reuter ◽  
Nelleke Tolboom ◽  
...  

2012 ◽  
Vol 8 (4S_Part_19) ◽  
pp. P702-P702
Author(s):  
Tammie Benzinger ◽  
Gina D'Angelo ◽  
Tyler Blazey ◽  
Gongfu Zhou ◽  
Beau Ances ◽  
...  

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ryan M. Nillo ◽  
Iris J. Broce ◽  
Besim Uzgil ◽  
Nilika S. Singhal ◽  
Christine M. Glastonbury ◽  
...  

Abstract Background Anti-NMDA receptor encephalitis is an immune-mediated disorder characterized by antibodies against the GluN1 subunit of the NMDA receptor that is increasingly recognized as a treatable cause of childhood epileptic encephalopathy. In adults, the disorder has been associated with reversible changes in brain volume over the course of treatment and recovery, but in children, little is known about its time course and associated imaging manifestations. Case presentation A previously healthy 20-month-old boy presented with first-time unprovoked seizures, dysautonomia, and dyskinesia. Paraneoplastic workup was negative, but CSF was positive for anti-NMDAR antibodies. The patient’s clinical condition waxed and waned over a 14-month course of treatment with first- and second-line immunotherapies (including steroids, IVIG, rituximab, and cyclophosphamide). Serial brain MRIs scans obtained at 5 time points spanning this same period showed no abnormal signal or enhancement but were remarkable for cycles of reversible regional cortical volume loss. All scans included identical 1-mm resolution 3D T1-weighted sequences obtained on the same 3 T scanner. Using a novel longitudinal processing stream in FreeSurfer6 (Reuter M, et. al, Neuroimage 61:1402–18, 2012) we quantified the rate of change in cortical volume at each vertex (% volume change per month) between consecutive scans and correlated these changes with the time course of the patient’s treatment and clinical response. We found regionally specific changes in cortical volume (up to 7% per month) that preferentially affected the frontal and occipital lobes and paralleled the patient’s clinical course, with clinical decline associated with volume loss and clinical improvement associated with volume gain. Conclusions Our results suggest that reversible cortical volume loss in anti-NMDA encephalitis has a regional specificity that mirrors many of the clinical symptoms associated with the disorder and tracks the dynamics of disease severity over time. This case illustrates how quantitative morphometric techniques can be applied to clinical imaging data to reveal patterns of brain change that may provide insight into disease pathophysiology. More widespread application of this approach might reveal regional and temporal patterns specific to different types of autoimmune encephalitis, providing a tool for diagnosis and a surrogate marker for monitoring treatment response.


Diabetes Care ◽  
2014 ◽  
Vol 37 (9) ◽  
pp. 2483-2490 ◽  
Author(s):  
Eelco van Duinkerken ◽  
Menno M. Schoonheim ◽  
Martijn D. Steenwijk ◽  
Martin Klein ◽  
Richard G. IJzerman ◽  
...  

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