Dataset Information

Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia.

ABSTRACT: Magnetic resonance imaging (MRI) has been proposed as a source of information for automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data from different MRI modalities is an active area of research aimed at increasing diagnostic accuracy. Based on a sample of 96 patients with schizophrenia and a matched sample of 115 healthy controls that had undergone a single multimodal MRI session, we generated individual brain maps of gray matter vbm, 1back, and 2back levels of activation (nback fMRI), maps of amplitude of low-frequency fluctuations (resting-state fMRI), and maps of weighted global brain connectivity (resting-state fMRI). Four unimodal classifiers (Ridge, Lasso, Random Forests, and Gradient boosting) were applied to these maps to evaluate their classification accuracies. Based on the assignments made by the algorithms on test individuals, we quantified the amount of predictive information shared between maps (what we call redundancy analysis). Finally, we explored the added accuracy provided by a set of multimodal strategies that included post-classification integration based on probabilities, two-step sequential integration, and voxel-level multimodal integration through one-dimensional-convolutional neural networks (1D-CNNs). All four unimodal classifiers showed the highest test accuracies with the 2back maps (80% on average) achieving a maximum of 84% with the Lasso. Redundancy levels between brain maps were generally low (overall mean redundancy score of 0.14 in a 0-1 range), indicating that each brain map contained differential predictive information. The highest multimodal accuracy was delivered by the two-step Ridge classifier (87%) followed by the Ridge maximum and mean probability classifiers (both with 85% accuracy) and by the 1D-CNN, which achieved the same accuracy as the best unimodal classifier (84%). From these results, we conclude that from all MRI modalities evaluated task-based fMRI may be the best unimodal diagnostic option in schizophrenia. Low redundancy values point to ample potential for accuracy improvements through multimodal integration, with the two-step Ridge emerging as a suitable strategy.

SUBMITTER: Salvador R

PROVIDER: S-EPMC6855131 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia.

Salvador Raymond R Canales-Rodríguez Erick E Guerrero-Pedraza Amalia A Sarró Salvador S Tordesillas-Gutiérrez Diana D Maristany Teresa T Crespo-Facorro Benedicto B McKenna Peter P Pomarol-Clotet Edith E

Frontiers in neuroscience 20191107

Magnetic resonance imaging (MRI) has been proposed as a source of information for automatic prediction of individual diagnosis in schizophrenia. Optimal integration of data from different MRI modalities is an active area of research aimed at increasing diagnostic accuracy. Based on a sample of 96 patients with schizophrenia and a matched sample of 115 healthy controls that had undergone a single multimodal MRI session, we generated individual brain maps of gray matter vbm, 1back, and 2back level ...[more]

PMID: 31787874

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Project description:Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9-10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis.

Project description:Objective/methodsNeuroimaging research has predominantly focused on exploring how cortical or subcortical brain abnormalities are related to language dysfunction in patients with neurological disease through the use of single modality imaging. Still, limited knowledge exists on how various MRI measures relate to each other and to patients' language performance. In this study, we explored the relationship between measures of regional cortical thickness, gray-white matter contrast (GWMC), white matter diffusivity [mean diffusivity (MD) and fractional anisotropy (FA)] and the relative contributions of these MRI measures to predicting language function across patients with temporal lobe epilepsy (TLE) and healthy controls. T1- and diffusion-weighted MRI data were collected from 56 healthy controls and 52 patients with TLE. By focusing on frontotemporal regions implicated in language function, we reduced each domain of MRI data to its principal component (PC) and quantified the correlations among these PCs and the ability of these PCs to explain the variation in vocabulary, naming and fluency. We followed up our significant findings by assessing the predictive power of the implicated PCs with respect to language impairment in our sample.ResultsWe found significant positive associations between PCs representing cortical thickness, GWMC and FA that appeared to be partially mediated by changes in total brain volume. We also found a significant association between reduced FA and increased MD after controlling for confounding factors (e.g., age, field strength, total brain volume). Reduced FA was significantly associated with reductions in visual naming while increased MD was associated with reductions in auditory naming scores, even after controlling for the variability explained by reductions in hippocampal volumes. Inclusion of FA and MD PCs in predictive models of language impairment resulted in significant improvements in sensitivity and specificity of the predictions.ConclusionsQuantitative MRI measures from T1 and diffusion-weighted scans are unlikely to represent perfectly orthogonal vectors of disease in individuals with epilepsy. On the contrary, they exhibit highly intercorrelated PCs in their factor structures, which is consistent with an underlying pathological process that affects both the cortical and the subcortical structures simultaneously. In addition to hippocampal volume, the PCs of diffusion weighted measures (FA and MD) increase the sensitivity and specificity for determining naming impairment in patients with TLE. These findings underline the importance of combining multimodal imaging measures to better predict language performance in TLE that could extend to other patients with prominent language impairments.

Dataset Information

Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia.

Publications

Multimodal Integration of Brain Images for MRI-Based Diagnosis in Schizophrenia.

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