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The neuroimaging challenges in hemispherectomy patients
Published in Mark J. Ashley, David A. Hovda, Traumatic Brain Injury, 2017
Zachary Jacokes, Avnish Bhattrai, Carinna Torgerson, Andrew Zywiec, Sumiko Abe, Andrei Irimia, Meng Law, Saman Hazany, John Darrell Van Horn
Traditional processing of neuroimaging data requires specific protocols and software programs; consequently, the prospect of processing hemispherectomy data presents novel challenges. Most software designed to process brain images operates under the assumption that the brain in question is intact, resulting in errors when attempting to process a partial brain. For this purpose, the Freesurfer (FS; http://surfer.nmr.mgh.harvard.edu) software package was used and applied via the LONI Pipeline workflow environment (http://pipeline.loni.usc.edu; Figure 12.2). FS, like other processing software, is dependent on the T1 MRI volume data being input to have come from generally healthy, intact brains. In cases in which this is not true, as in the case here, the derived statistics from FS are unlikely to be reliable. For FS to accept and process the image volumes, they need to be manipulated in a manner so that “whole brain” computations can be performed (shown in Figure 12.4), and more reliable results obtained for the intact brain tissue. We now describe this process.
Cortical and cerebellar structural correlates of cognitive-motor integration performance in females with and without persistent concussion symptoms
Published in Brain Injury, 2023
Johanna M. Hurtubise, Diana J. Gorbet, Loriann Hynes, Alison K. Macpherson, Lauren E. Sergio
In addition, the volume and thickness of cortical regions of interest were examined. These regions were determined a-priori and known to be involved in the frontoparietal network for visually guided reaching (28,48,49). Regions in the parietal lobe included the right and left superior parietal lobe (SPL), inferior parietal lobe (IPL), and precuneus. In the frontal lobe, regions of interest included the right and left precentral, superior frontal, rostral middle frontal (rMFG), and caudal middle frontal (cMFG) regions. Finally, the cuneus, which is a region within the occipital lobe, was also investigated. Both the thickness and volume were extracted from each subject using the Desikan-Killiany cortical parcellation atlas (50). The cortical parcellation of the FreeSurfer template was mapped back onto the individual subject and adjusted for small variations. The values of each individual subject’s thickness and volume of the aforementioned regions were then extracted and structural volumes were corrected for TIV using a proportion method.
An insight into diagnosis of depression using machine learning techniques: a systematic review
Published in Current Medical Research and Opinion, 2022
Sweta Bhadra, Chandan Jyoti Kumar
Fifth, it is observed that data pre-processing tools were identical for similar data modalities. In the experiments utilizing MRI and DTI data, the data was processed using the FreeSurfer software. For DTI, two additional tools were employed for pre-processing: FSL topup and eddy. SPM and FSL’s FMRI Expert Analysis Tool were utilized to pre-process data in studies employing fMRI data. Several software’s such as NiftyReg, DPABI, Diffeomorphic Anatomical Registration using the Exponential Lie Algebra (DARTEL) toolbox were used for pre-processing MRI images. EEGLab and NeuroScan were found to be two widely used pre-processing tools for EEG signals. For MEG signal, the use of FieldTrip Toolbox and SPM was observed. We note that there is an exhaustive list of software tools which is used for the pre-processing of neuroimaging data that enables mental health researchers to derive a high level of information useful for a depression diagnosis.
Hippocampal volume, function, and related molecular activity in anorexia nervosa: A scoping review
Published in Expert Review of Clinical Pharmacology, 2020
Johanna Keeler, Olivia Patsalos, Sandrine Thuret, Stefan Ehrlich, Kate Tchanturia, Hubertus Himmerich, Janet Treasure
There are several limitations reflecting the aggregation of data in this review. There was a discrepancy across studies in the strength of the scanner used; whilst the majority of studies used 3 T MRI scanners, a number of studies used 1.5 T scanners that have been found to be less sensitive in detecting hippocampal atrophy [123]. Although we did not distinguish between scanner strength in our findings, we did note the differences in studies using MRI in Tables 2 and 3. Furthermore, there was heterogeneity between whether authors segmented the hippocampus using Freesurfer or voxel-based morphometry techniques. Whilst these techniques can lead to differences in volumetric measurements of the hippocampus and are not directly comparable, this is largely down to differences in anatomical boundary definitions rather than segmentation strategies [124]. Finally, there are a number of fMRI studies that in fact found peak activation in areas close to, but distinct from the hippocampus, but considered the findings of relevance to the hippocampus [80,86]. For example, these findings can be an artifact of smoothing techniques used in fMRI analysis, not reflecting true hippocampus activation but rather reflecting how spatially close the hippocampus is to key areas such as the amygdala. This would be of specific relevance to the studies included that investigated emotional processing [80].