Drew Morris

Converging Evidence for the Advantage of Dynamic Facial Expressions

Neuroimaging evidence suggests that dynamic facial expressions elicit greater activity than static face stimuli in brain structures associated with social cognition, interpreted as greater ecological validity. However, a quantitative meta-analysis of brain activity associated with dynamic facial expressions is lacking. The current study investigated, using three fMRI experiments, activity elicited by (a) dynamic and static happy faces, (b) dynamic and static happy and angry faces, and (c) dynamic faces and dynamic flowers. In addition, using activation likelihood estimate (ALE) meta-analysis, we determined areas concordant across published studies that (a) used dynamic faces and (b) specifically compared dynamic and static emotional faces. The middle temporal gyri (Experiment 1) and superior temporal sulci (STS; Experiment 1 and 2) were more active for dynamic than static faces. In contrasts with the baseline the amygdalae were more active for dynamic faces (Experiment 1 and 2) and the fusiform gyri were active for all conditions (all Experiments). The ALE meta-analyses revealed concordant activation in all of these regions as well as in areas associated with cognitive manipulations (inferior frontal gyri). Converging data from the experiments and the meta-analyses suggest that dynamic facial stimuli elicit increased activity in regions associated with interpretation of social signals and emotional processing.

Quantitative MRI in the very preterm brain: Assessing tissue organization and myelination using magnetization transfer, diffusion tensor and T1 imaging

Magnetization transfer ratio (MTR), diffusion tensor imaging (DTI) parameters and T(1) relaxometry values were used to create parametric maps characterizing the tissue microstructure of the neonatal brain in infants born very premature (24-32 gestational weeks) and scanned at preterm and term equivalent age. Group-wise image registration was used to determine anatomical correspondence between individual scans and the pooled parametric data at the preterm and term ages. These parametric maps showed distinct contrasts whose interrelations varied across brain regions and between the preterm and term period. Discrete patterns of regional variation were observed for the different quantitative parameters, providing evidence that MRI is sensitive to multiple independent aspects of brain maturation. MTR values showed a marked change in the pattern of regional variation at term equivalent age compared to the preterm period such that the ordinal ranking of regions by signal contrast changed. This was unlike all other parameters where the regional ranking was preserved at the two time points. Interpreting the data in terms of myelination and structural organization, we report on the concordance with available histological data and demonstrate the value of quantitative MRI for tracking brain maturation over the neonatal period.

Sub-patterns of language network reorganization in pediatric localization related epilepsy: A multisite study

To study the neural networks reorganization in pediatric epilepsy, a consortium of imaging centers was established to collect functional imaging data. Common paradigms and similar acquisition parameters were used. We studied 122 children (64 control and 58 LRE patients) across five sites using EPI BOLD fMRI and an auditory description decision task. After normalization to the MNI atlas, activation maps generated by FSL were separated into three sub‐groups using a distance method in the principal component analysis (PCA)‐based decisional space. Three activation patterns were identified: (1) the typical distributed network expected for task in left inferior frontal gyrus (Brocas) and along left superior temporal gyrus (Wernickes) (60 controls, 35 patients); (2) a variant left dominant pattern with greater activation in IFG, mesial left frontal lobe, and right cerebellum (three controls, 15 patients); and (3) activation in the right counterparts of the first pattern in Brocas area (one control, eight patients). Patients were over represented in Groups 2 and 3 (P < 0.0004). There were no scanner (P = 0.4) or site effects (P = 0.6). Our data‐driven method for fMRI activation pattern separation is independent of a priori notions and bias inherent in region of interest and visual analyses. In addition to the anticipated atypical right dominant activation pattern, a sub‐pattern was identified that involved intensity and extent differences of activation within the distributed left hemisphere language processing network. These findings suggest a different, perhaps less efficient, cognitive strategy for LRE group to perform the task. Hum Brain Mapp, 2011.

A balancing act of the brain: activations and deactivations driven by cognitive load

The majority of neuroimaging studies focus on brain activity during performance of cognitive tasks; however, some studies focus on brain areas that activate in the absence of a task. Despite the surge of research comparing these contrasted areas of brain function, their interrelation is not well understood. We systematically manipulated cognitive load in a working memory task to examine concurrently the relation between activity elicited by the task versus activity during control conditions. We presented adults with six levels of task demand, and compared those with three conditions without a task. Using whole‐brain analysis, we found positive linear relations between cortical activity and task difficulty in areas including middle frontal gyrus and dorsal cingulate; negative linear relations were found in medial frontal gyrus and posterior cingulate. These findings demonstrated balancing of activation patterns between two mental processes, which were both modulated by task difficulty. Frontal areas followed a graded pattern more closely than other regions. These data also showed that working memory has limited capacity in adults: an upper bound of seven items and a lower bound of four items. Overall, working memory and default‐mode processes, when studied concurrently, reveal mutually competing activation patterns.

White matter and development in children with an autism spectrum disorder

Recent research suggests that brain development follows an abnormal trajectory in children with autism spectrum disorders (ASD). The current study examined changes in diffusivity with age within defined white matter tracts in a group of typically developing children and a group of children with an ASD, aged 6 to 14 years. Age by group interactions were observed for frontal, long distant, interhemispheric and posterior tracts, for longitudinal, radial and mean diffusivity, but not for fractional anisotropy. In all cases, these measures of diffusivity decreased with age in the typically developing group, but showed little or no change in the ASD group. This supports the hypothesis of an abnormal developmental trajectory of white matter in this population, which could have profound effects on the development of neural connectivity and contribute to atypical cognitive development in children with ASD.

Language dominance in children with epilepsy: Concordance of fMRI with intracarotid amytal testing and cortical stimulation

Accurate localization of language function is critical in children undergoing epilepsy surgery. Functional magnetic resonance imaging (fMRI) is a noninvasive mapping method that has begun to replace electrocortical stimulation mapping (ESM) and the intracarotid amytal test (IAT). We used both quantitative and qualitative methods to evaluate the concordance of fMRI with ESM and IAT in 20 children using a panel of language tasks. In no cases did fMRI assessment of language hemisphere dominance identify the opposite hemisphere from assessment by IAT or ESM. Concordance with IAT and ESM was higher using fMRI visual inspection than an fMRI laterality index, which failed to lateralize language in a number of the subjects. We have demonstrated that fMRI has good concordance with more traditional methods of language mapping. When fMRI demonstrates bilateral language activations, however, we continue to recommend confirmatory testing by either IAT or ESM prior to resection in classic language regions.

The optimal linear transformation-based fMRI feature space analysis

This paper proposes a method of extending the optimal linear transformation (OLT), an image analysis technique of feature space, from magnetic resonance imaging (MRI) to functional magnetic resonance imaging (fMRI) so as to improve the activation detection performance over conventional approaches of fMRI analysis. The method was: (1) ideal hemodynamic responses for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing, (2) considering the ideal hemodynamic responses as hypothetical signature vectors for different activity patterns of interest, OLT was used to extract the features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging, (3) using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed to validate the proposed method. The simulation and the experiment results indicated the proposed method was capable of improving some conventional methods to be more sensitive to activations, having strong contrast between activations and inactivations, and being more valid for complex activity patterns.

Feature-Space-Based fMRI Analysis Using the Optimal Linear Transformation

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.