X-Chromosome Insufficiency Alters the Structure and Function of the Human Visual Cortex

Post by Lincoln Tracy

What's the science?

Turner syndrome is a genetic condition in females caused by the absence of a part of, or an entire, X chromosome. Women with Turner syndrome have specific cognitive deficits relating to the ability to perceive spatial relationships between objects and the ability to select and monitor goal-related behaviors. Previous research suggests that women and girls with Turner syndrome have structural changes in the parieto-occipital cortex (the part of the brain responsible for the integration of sensory information and the processing of visual information) when compared to typically developing controls. Specifically, females with Turner syndrome are reported to have a smaller volume of parieto-occipital cortex with a thicker surface. Despite knowing that these structural changes exist, it is not known whether abnormal processing of visual information occurs in females with Turner syndrome. This week in The Journal of Neuroscience, Green and colleagues used functional magnetic resonance imaging (fMRI) and population receptive field (pRF) mapping to investigate receptive field processing in Turner syndrome.

How did they do it?

The authors recruited 24 girls (aged 7-14 years) with Turner syndrome and 28 typically developing girls of the same age to act as controls. All 52 participants had an MRI scan where they watched a screen that showed a flickering black and white checkerboard pattern in either expanding rings or rotating wedges to obtain and assess data for the pRFs. pRFs can be used to generate topographic maps of the visual field (i.e. the total area an individual can see) using representations of the polar angle (the angle from the horizontal axis) and eccentricity (the distance from fixation). Data from the MRI scans were used to determine the volume, surface area, and cortical thickness of the early visual areas V1-V3. Visual field coverage (the locations within a visual field that evoke a response from voxels within a brain map) in these areas were also drawn from the MRI data. Outside of the MRI scanner the girls completed the Picture Puzzles test, one of the tests from the NEPSY-II battery, which tests visuospatial processing. The relationship between performance on the Picture Puzzles test and pRF properties was compared.

What did they find?

First, the authors found that girls with Turner syndrome and typically developing girls showed a similar organization of polar angle and eccentricity maps. These maps were then used to define visual field maps for V1-V3. Using these maps, the authors then showed that while there were no differences in total brain volume between girls with Turner syndrome and typically developing girls, the cortical volume and surface area of the V1-V3 visual field maps were smaller in girls with Turner syndrome. There were no differences in V1-V3 cortical thickness between girls with Turner syndrome and typically developing girls. Second, they found that despite Turner syndrome and typically developing participants having similar pRF size for V1-V3, participants with Turner syndrome had less pRF eccentricity in visual areas V1-V3 (meaning that their pRF was closer to the center of their gaze). Third, visual field coverage was determined by comparing how the pRFs from V1-V3 fit together in the visual field. There were no differences between participants with Turner syndrome and typically developing girls in the visual field coverage of V1-V3, nor were there differences in the polar angle and eccentricity maps. Fourth, they found that participants with Turner syndrome performed worse in the Picture Puzzles task compared to the typically developing group. Performance in the Picture Puzzles task was found to be negatively correlated with pRF size and eccentricity in girls with Turner syndrome. There was no correlation between performance in the Picture Puzzles task and the cortical volume of V1-V3 in either the participants with Turner syndrome or typically developing girls.

What's the impact?

The authors have demonstrated that Turner syndrome may negatively affect the functional properties of brain regions responsible for visual processing, which may in turn influence behavior. The findings of this study may serve as a novel approach for investigating how other regions of the brain are affected by Turner syndrome. Furthermore, the authors provide a target for interventions to improve the visuospatial deficits observed in Turner syndrome. Taken together, these results suggest a fundamental change in our understanding of how variations in sex chromosome pairings affect visuospatial development in humans.

Green et al. X-Chromosome Insufficiency Alters Receptive Fields across the Human Early Visual Cortex. Journal of Neuroscience (2019).Access the original scientific publication here.

Uncovering Neuronal Circuitry Using Computational Modelling

Post by Amanda McFarlan

What's the science?

With the advancement of technology, it is now possible to acquire recordings from large populations of neurons in anaesthetized or awake, behaving animals. These recordings generate large data sets that may be useful for understanding how neuronal activity is related to behaviour. However, interpreting and analyzing the results from a large data set can be very challenging and requires sophisticated computational methods. This week in Nature Communications, Kobayashi and colleagues investigated the efficacy of their computational model in identifying neural connectivity in both synthetic simulations and biological data sets.  

How did they do it?

The authors estimated neuronal connectivity using a computational model by cross-correlating the spike (or action potential)  times for each neuron pair. Then, they applied a generalized linear model to identify pairs of neurons with small (millisecond) differences in spike timing to determine the pairs that were likely monosynaptically connected. To determine the level of conservatism that optimally balanced the rate of detected false positives and false negatives, the authors applied their model to spike train data obtained from a synthetic neuronal network of 1000 neurons (800 excitatory, 200 inhibitory) for which the connectivity was already known. Next, the authors sampled neurons from the total population to evaluate the efficacy of their model at predicting neural connections. They generated a connection matrix with four quadrants to represent the different combinations of neural connections: inhibitory-excitatory, excitatory-excitatory, excitatory-inhibitory and inhibitory-inhibitory. Then, they applied their model to spike train data from hippocampal neurons in the CA1 region of a rat to assess its efficacy in estimating neuronal connectivity when applied to a real, biological data set. Finally, the authors established the validity of their model by comparing it to two other commonly used methods, the cross-correlation method and the jittering method, while using both synthetic and biological data.

What did they find?

The authors showed that their simulation of a neuronal network produced results that were consistent with balanced state network models. They determined that balancing the rate of false positives and false negatives was optimally achieved using a significance level of α=0.001 to determine neuronal connections. Compared to excitatory neurons, inhibitory neurons tended to have higher firing rates and exhibit more regular spiking. Additionally, the likelihood of identifying neuronal connections was positively correlated with the duration of the recording, suggesting that it may be possible to correctly identify a larger number of connections between excitatory and inhibitory neurons by extending the length of a recording. Next, the authors showed that applying their computational model to biological data in the rat hippocampus yielded similar results as applying it to synthetic data, suggesting that their model is useful for estimating neuronal connections in real-life examples. Finally, they revealed that their computational model, with only 13 false connections, was superior to the cross-correlation and jittering methods which had 88 and 27 false connections, respectively.

What's the impact?

This is the first study to show a new computational model that combines the generalized linear model and cross-correlations to reliably identify neuronal connections in complex neural networks. The authors have demonstrated that their model is superior to other commonly used methods and is also effective when applied to both synthetic and biological data. This computational model may be instrumental for mapping neural connections in large data sets from extracellular recordings in animal subjects and may provide insight into patterns of neuronal connectivity throughout the brain.  

Kobayashi et al. Reconstructing neuronal circuitry from parallel spike trains. Nature Communications (2019). Access the original scientific publication here.

Illustration by Kai Shinomoto

The Global Brain Signal Varies Across The Cortex and Carries Information about Behavior

Post by Stephanie Williams

What's the science?

In some types of neuroimaging analyses, a brain-wide signal called the ‘global signal’ can be extracted from functional magnetic resonance (fMRI) scans, a type of scan that measures brain activity indirectly by measuring blood oxygenation/flow. Usually, this signal is identified by averaging the signals from each cortical region or location, at each timepoint in the scan. The information contained in this signal is not completely understood, but different physiological fluctuations contribute to the signal; for example, the signal can be related to heartbeat or to a person moving their body while in the scanner, suggesting some of the signal might represent noise and not true brain activity. Because of this, sometimes scientists have previously removed this signal from their data before continuing with further analysis. However, due to recent findings regarding the composition of the global signal, the practice of removing the signal from data is now less common in neuroimaging research. This week in Nature Scientific Reports , Li and colleagues show that the global signal is related to the cortex in a topographically-specific manner and that it contains information related to trait-level cognition and behavior.

How did they do it?

The authors were interested in understanding two broad aspects of the global signal: 1) How individual differences in the global signal are related cognition and behavior and 2) How local activity fluctuations (in one brain region) are correlated with global signal fluctuations, and how this relationship varies across different cortical regions ( i.e. the “topography” of the global signal). The authors used a cohort (N=1094) of healthy young adults (ages 22-37) from the HCP S1200 dataset. Each participant had several 14.4-minute fMRI scans. During the scans, subjects were not asked to perform any specific task — they were simply resting in the scanner. To extract the global signal for each participant, the authors averaged the signals across all grey matter cortical vertices (known as voxels). To create global signal maps, the authors regressed the global signal across each voxel’s signal fluctuations. This step produced a map of beta coefficients across the cortex, with each beta coefficient representing how similar that local time series was to the averaged time series of the whole brain (a time-dependent correlation).

To extract a general global signal map for all subjects, the authors grouped subjects and averaged across each subject’s average signal map. They also looked at differences between individual subjects (standard deviation of the general global signal map) and looked at how these differences varied across the brain. Next, they analyzed the relationship between the global signal and a series of behavioral measures. They extracted 100 principal components from a pool of behavioral measures and 100 principle components from the global signal for each subject. They then used a machine learning algorithm called canonical correlation analysis to estimate linear combinations of the global signal and behavioral measure components. They used several outputs from this analysis to extract significant brain-brain-behavior associations. They also confirmed that their findings were not confounded by subject motion while in the scanner. 

What did they find?

The authors found that 1) global signal is related to local cortical fluctuations in a reliable manner. The topographic maps of the global signal showed strong mapping (high correlations between the local and global time series) in the medial posterior occipital lobes, posterior insula, and central sulcus across subjects. The authors report that the greatest variation in the global signal beta maps arose in visual cortex and retrosplenial cortex. The results of the principal component analysis showed that the first 3 principal components of the global signal were similar to 1) frontoparietal control network 2) default and dorsal attention networks and 3) sensorimotor and visual networks. The spatial patterns of the 3 first principal components were similar to the typical pattern of each of the three networks. The results of the canonical correlation analysis suggest that several significant brain-behavior relationships ( “canonical-variate pairs”) existed and could be inferred from the global signal. The authors found that greater weights for the first global signal principal component--which the authors interpret as the frontoparietal control network-- were related to higher scores in picture vocabulary, temporal discounting, life satisfaction, and lower aggressive antisocial behavior. The authors interpret their findings, supported by previous literature, as showing that brain-behavior relationships can be derived from information contained in the global signal alone.

What's the impact?

The authors have provided evidence for the importance of the global signal, which will affect how individual researchers decide to preprocess their fMRI data. They show that the global signal contains both noise and behaviorally-relevant signals, and should not be removed prior to data analysis.

Li et al. Topography and behavioral relevance of the global signal in the human brain. Scientific Reports (2019). Access the original scientific publication here.