Tracing the structural origins of atypical language representation: consequences of prenatal mirror-imaged brain asymmetries in a dizygotic twin couple

The impact of ROI extraction method for MEG connectivity estimation: practical recommendations for the study of resting state data

Magnetoencephalography and electroencephalography (M/EEG) seed-based connectivity analysis requires the extraction of measures from regions of interest (ROI). M/EEG ROI-derived source activity can be treated in different ways. It is possible, for instance, to average each ROI's time series prior to calculating connectivity measures. Alternatively, one can compute connectivity maps for each element of the ROI prior to dimensionality reduction to obtain a single map. The impact of these different strategies on connectivity results is still unclear. Here, we address this question within a large MEG resting state cohort (N=113) and within simulated data. We consider 68 ROIs (Desikan-Kiliany atlas), two measures of connectivity (phase locking value-PLV, and its imaginary counterpart- ciPLV), and three frequency bands (theta 4-8 Hz, alpha 9-12 Hz, beta 15-30 Hz). We compare four extraction methods: (i) mean, or (ii) PCA of the activity within the seed or ROI before computing connectivity, map of the (iii) average, or (iv) maximum connectivity after computing connectivity for each element of the seed. Hierarchical clustering is then applied to compare connectivity outputs across multiple strategies, followed by direct contrasts across extraction methods. Finally, the results are validated by using a set of realistic simulations. We show that ROI-based connectivity maps vary remarkably across strategies in terms of connectivity magnitude and spatial distribution. Dimensionality reduction procedures conducted after computing connectivity are more similar to each-other, while PCA before approach is the most dissimilar to other approaches. Although differences across methods are consistent across frequency bands, they are influenced by the connectivity metric and ROI size. Greater differences were observed for ciPLV than PLV, and in larger ROIs. Realistic simulations confirmed that after aggregation procedures are generally more accurate but have lower specificity (higher rate of false positive connections). Though computationally demanding, after dimensionality reduction strategies should be preferred when higher sensitivity is desired. Given the remarkable differences across aggregation procedures, caution is warranted in comparing results across studies applying different methods.

Automated Brain Masking of Fetal Functional MRI with Open Data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

Associations between Cortical Asymmetry and Domain Specific Cognitive Functions in Healthy Children

Cortical asymmetry and functional lateralization form intriguing and fundamental features of human brain organization, and is complicated by individual differences and evolvement with age. While many studies have investigated neuroanatomical differences between hemispheres as well as functional lateralization of the brain for different age groups, few have looked into the associations between cortical asymmetry and development of cognitive functions in children. In this study, we aimed to identify relationships between hemispheric asymmetry in brain cortex measured by MRI and cognitive development in healthy young children evaluated by a comprehensive battery of neuropsychological tests. Structural MRI data were obtained from 71 children in the age range of 7.5 to 8.5 years. Structural lateralization index (SLI), a reflection of the brain asymmetry, was computed for each of the 3 cortical morphometry measurements: cortical thickness, surface area and gray matter volume. A total of 34 bilateral regions were studied for the whole brain cortex as defined by the Desikan atlas. Region-wise SLI was correlated with domain specific cognitive scores using partial correlation analysis controlled for the potential confounding effects of age and sex. Significant correlations were identified between test scores of multiple cognitive domains and SLI of several cortical regions. Specifically, SLI of total surface area of precuneus and insula significantly correlated with measures of executive function behavior; significant relationships were also found between SLI of mean cortical thickness of superior parietal cortex and memory and language tests scores; in addition, SLI of parahippocampal gyrus also showed significant correlations with language test scores for all 3 morphometry features. These findings revealed regional hemispheric asymmetries that may be linked to specific cognitive abilities in children.Clinical relevance- This study shows associations between structural lateralization in different brain cortical regions and variations in specific cognitive functions in healthy children.

Exploratory factor analysis with structured residuals for brain network data

Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high-dimensional data to a small number of underlying variables. Techniques such as exploratory factor analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA by using structured residuals (EFAST), and (c) apply this technique to three large and varied brain-imaging network datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.