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      Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis


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          • Small sample sizes are a challenge when using deep learning (DL) on fMRI data.

          • We examined effects of a data augmentation procedure called Mixup on fMRI DL.

          • Mixup improved dl-based prediction of clinical outcome in psychosis using fMRI data.


          Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically small due to the considerable time, cost, and resources necessary to obtain them. A recently developed self-supervised learning method called Mixup may help overcome this challenge. In Mixup, the learner combines pairs of training instances to produce a virtual third instance that is a linear combination of the two instances and their labels. This procedure is also well-suited to the coregistered images typically found in fMRI datasets. Here we compared performance of a task fMRI-based deep learner with Mixup vs without Mixup on predicting response to treatment in recent onset psychosis. Whole brain fMRI time series data were extracted from a cognitive control task in 82 patients with recent onset psychosis and used to predict “Improver” ( n = 47) vs “Non-Improver” ( n = 35) status, with Improver defined as showing a 20 % reduction in total Brief Psychiatric Rating Scale score after 1 year of treatment. Mixup significantly improved performance (accuracy without Mixup: 76.5 % [95 % CI: 75.9–77.1 %]; accuracy with Mixup: 80.1 % [95 % CI: 79.4–80.8 %]). Ablation showed the improvement was due to improvement in both Improvers and Non-Improvers. These results suggest that using Mixup may significantly improve performance and reduce overfitting of fMRI-based prognostic deep learners and may also help overcome the small sample size challenge inherent to many neuroimaging datasets.

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          Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

          Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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            Functional network organization of the human brain.

            Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex. Copyright © 2011 Elsevier Inc. All rights reserved.
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              GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification


                Author and article information

                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                29 September 2022
                29 September 2022
                : 36
                [a ]Department of Psychiatry and Behavioral Sciences, University of California, Davis, United States
                [b ]Department of Computer Sciences, University of California, Davis, United States
                Author notes
                [* ]Corresponding author at: Imaging Research Center, University of California, Davis, 4701 X Street, Sacramento, CA 95817, United States. jsmucny@ 123456ucdavis.edu
                S2213-1582(22)00279-0 103214
                © 2022 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                Regular Article

                cognitive control,ensemble learning,fmri,machine learning,schizophrenia,transfer learning


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