Localizing unique and overlapping lesion locations in apraxia of speech and aphasia

Since Darley’s original description of apraxia of speech (AOS; 1968), controversy has centered around its diagnosis, treatment, and lesion location. Behaviors common to AOS are often shared among other communication disorders, complicating clinical management. The current study sought to identify crucial brain damage that causes apraxic speech, as well as errors common in both AOS and aphasia. Results revealed that damage to premotor and supplementary motor areas is unique to AOS, while involvement of temporal lobe areas predicts behaviors attributable to aphasia. These findings contribute to research regarding the neuroanatomical mechanism of AOS, and may ultimately improve differential diagnostic procedures

Using neuroimaging to classify aphasia

The proper classification of aphasia based on clinical symptoms has been debated for well over a century. Much of the early debates centered on relating localized brain damage to a constellation of speech and language impairments. The premise behind much of this work was based on the notion that lesion-symptom mapping could reveal how language was organized in the brain (Broca, 1861, 1865; Dejarine, 1906; Marie, 1906). Although the principle for classifying aphasia based on specific symptoms has been fervently challenged (e.g. Head, 1926) it is still customary to report aphasia types in clinical studies of aphasia. Similar symptoms in sub-groups of patients suggests a similar pattern of brain damage. Nevertheless, it remains unclear if specific aphasia types can be diagnosed simply based on the location of cortical damage. One way to examine this issue would be to relate lesion patterns to aphasia types using multivariate pattern analysis (MVPA). MVPA of neuroimaging data has been successfully used to diagnose diseases such as dementia, schizophrenia, and Parkinson's disease (Orru et al., 2012). In the present study, we demonstrate how MVPA can be used to predict aphasia type in persons with chronic stroke. Unlike previous studies that perform the analysis on voxels (using MRI scans), we trained a classifier on the proportional damage to brain areas (defined with a brain atlas). In addition, we computed the loadings that reflect the contribution of each brain area to classification

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

A variety of preprocessing techniques are available to correct subject-dependant artifacts in fMRI, caused by head motion and physiological noise. Although it has been established that the chosen preprocessing steps (or “pipeline”) may significantly affect fMRI results, it is not well understood how preprocessing choices interact with other parts of the fMRI experimental design. In this study, we examine how two experimental factors interact with preprocessing: between-subject heterogeneity, and strength of task contrast. Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults. The importance of standard preprocessing with motion correction, physiological noise correction, motion parameter regression and temporal detrending were examined for the two task contrasts. We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA). Results were obtained for Penalized Discriminant Analysis, and model performance quantified with reproducibility (R) and prediction metrics (P). Simulation methods were also used to test for potential biases from individual-subject optimization. Our results demonstrate that (1) individual pipeline optimization is not significantly more biased than fixed preprocessing. In addition, (2) when applying a fixed pipeline across all subjects, the task contrast significantly affects pipeline performance; in particular, the effects of PCA and ICA models vary with contrast, and are not by themselves optimal preprocessing steps. Also, (3) selecting the optimal pipeline for each subject improves within-subject (P,R) and between-subject overlap, with the weaker cognitive contrast being more sensitive to pipeline optimization. These results demonstrate that sensitivity of fMRI results is influenced not only by preprocessing choices, but also by interactions with other experimental design factors. This paper outlines a quantitative procedure to denoise data that would otherwise be discarded due to artifact; this is particularly relevant for weak signal contrasts in single-subject, small-sample and clinical datasets

Predictive Gaussian Classification of Functional MRI Data

This thesis presents an evaluation of algorithms for classification of functional MRI data. We evaluated the performance of probabilistic classifiers that use a Gaussian model against a popular non-probabilistic classifier (support vector machine, SVM). A pool of classifiers consisting of linear and quadratic discriminants, linear and non-linear Gaussian Naive Bayes (GNB) classifiers, and linear SVM, was evaluated on several sets of real and simulated fMRI data. Performance was measured using two complimentary metrics: accuracy of classification of fMRI volumes within a subject, and reproducibility of within-subject spatial maps; both metrics were computed using split-half resampling. Regularization parameters of multivariate methods were tuned to optimize the out-of-sample classification and/or within-subject map reproducibility. SVM showed no advantage in classification accuracy over Gaussian classifiers. Performance of SVM was matched by linear discriminant, and at times outperformed by quadratic discriminant or nonlinear GNB. Among all tested methods, linear and quadratic discriminants regularized with principal components analysis (PCA) produced spatial maps with highest within-subject reproducibility. We also demonstrated that the number of principal components that optimizes the performance of linear / quadratic discriminants is sensitive to the mean magnitude, variability and connectivity of simulated active signal. In real fMRI data, this number is correlated with behavioural measures of post-stroke recovery , and, in a separate study, with behavioural measures of self-control. Using the data from a study of cognitive aspects of aging, we accurately predicted the age group of the subject from within-subject spatial maps created by our pool of classifiers. We examined the cortical areas that showed difference in recruitment in young versus older subjects; this difference was demonstrated to be primarily driven by more prominent recruitment of task-positive network in older subjects. We conclude that linear and quadratic discriminants with PCA regularization are well-suited for fMRI data classification, particularly for within-subject analysis.Ph

Classifying Mental States From Eye Movements During Scene Viewing

How eye movements reflect underlying cognitive processes during scene viewing has been a topic of considerable theoretical interest. In this study, we used eye-movement features and their distributions over time to successfully classify mental states as indexed by the behavioral task performed by participants. We recorded eye movements from 72 participants performing 3 scene-viewing tasks: visual search, scene memorization, and aesthetic preference. To classify these tasks, we used statistical features (mean, standard deviation, and skewness) of fixation durations and saccade amplitudes, as well as the total number of fixations. The same set of visual stimuli was used in all tasks to exclude the possibility that different salient scene features influenced eye movements across tasks. All of the tested classification algorithms were successful in predicting the task within a single participant. The linear discriminant algorithm was also successful in predicting the task for each participant when the training data came from other participants, suggesting some generalizability across participants. The number of fixations contributed most to task classification; however, the remaining features and, in particular, their covariance provided important task-specific information. These results provide evidence on how participants perform different visual tasks. In the visual search task, for example, participants exhibited more variance and skewness in fixation durations and saccade amplitudes, but also showed heightened correlation between fixation durations and the variance in fixation durations. In summary, these results point to the possibility that eye-movement features and their distributional properties can be used to classify mental states both within and across individuals

Chronic post-stroke aphasia severity is determined by fragmentation of residual white matter networks

Many stroke survivors with aphasia in the acute period experience spontaneous recovery within the first six months after the stroke. However, approximately 30-40% sustain permanent aphasia and the factors determining incomplete recovery are unclear. Suboptimal recovery may be influenced by disruption of areas seemingly spared by the stroke due to loss of white matter connectivity and network integrity. We reconstructed individual anatomical whole-brain connectomes from 90 left hemisphere stroke survivors using diffusion MR images. We measured the modularity of the residual white matter network organization, the probability of brain regions clustering together, and the degree of fragmentation of left hemisphere networks. Greater post-stroke left hemisphere network fragmentation and higher modularity index were associated with more severe chronic aphasia, controlling for the size of the stroke lesion. Even when the left hemisphere was relatively spared, subjects with disorganized community structure had significantly worse aphasia, particularly when key temporal lobe regions were isolated into segregated modules. These results suggest that white matter integrity and disorganization of neuronal networks could be important determinants of chronic aphasia severity. Connectome white matter organization measured through modularity and other topological features could be used as a personalized variable for clinical staging and aphasia treatment planning