Dataset Information

Integration of multimodal MRI data via PCA to explain language performance.

ABSTRACT:

Objective/methods

Neuroimaging 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.

Results

We 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.

Conclusions

Quantitative 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.

SUBMITTER: Kucukboyaci NE

PROVIDER: S-EPMC4110349 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Integration of multimodal MRI data via PCA to explain language performance.

Kucukboyaci N E NE Kemmotsu N N Leyden K M KM Girard H M HM Tecoma E S ES Iragui V J VJ McDonald C R CR

NeuroImage. Clinical 20140514

<h4>Objective/methods</h4>Neuroimaging 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), ...[more]

PMID: 25068109

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Project description: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.

Dataset Information

Integration of multimodal MRI data via PCA to explain language performance.

Objective/methods

Results

Conclusions

Publications

Integration of multimodal MRI data via PCA to explain language performance.

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