Shared functional connections within and between cortical networks predict individual cognitive abilities in adult males and females
A thorough understanding of sex-independent and sex-specific neurobiological features that underlie cognitive abilities in healthy individuals is essential for the study of neurological illnesses in which males and females differentially experience and exhibit cognitive impairment. Here, we evaluate sex-independent and sex-specific relationships between functional connectivity and individual cognitive abilities in 392 healthy young adults (196 males) from the Human Connectome Project. First, we establish that sex-independent models comparably predict crystallised abilities in males and females, but more accurately predict fluid abilities in males. Second, we demonstrate sex-specific models comparably predict crystallised abilities within and between sexes, and generally fail to predict fluid abilities in either sex. Third, we reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and temporal parietal networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and temporal parietal networks are associated with poorer performance. Together, our findings suggest that shared neurobiological features of the functional connectome underlie crystallised and fluid abilities across the sexes.
Dhamala, E., Jamison, K. W., Jaywant, A., & Kuceyeski, A. (2021). Shared functional connections within and between cortical networks predict individual cognitive abilities in males and females. bioRxiv.
Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults
White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease-related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.
Dhamala, E., Jamison, K. W., Jaywant, A., Dennis, S., & Kuceyeski, A. (2021). Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults. Human Brain Mapping.
Sex classification using long‐range temporal dependence of resting‐state fMRI time series
A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional temporal dependence of resting‐state brain activity in 195 adult male–female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies up to 81%. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume‐matching when studying brain‐based sex differences. Differential patterns in regional temporal dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.
Dhamala, E., Jamison, K. W., Sabuncu, M. R., & Kuceyeski, A. (2020). Sex classification using long‐range temporal dependence of resting‐state functional MRI time series. Human brain mapping, 41(13), 3567-3579.
Validation of in vivo MRS measures of metabolite concentrations in the human brain
In vivo magnetic resonance spectroscopy (MRS) is the only technique capable of non‐invasively assessing metabolite concentrations in the brain. The lack of alternative methods makes validation of MRS measures challenging. The aim of this study is to assess the validity of MRS measures of human brain metabolite concentrations by comparing multiple MRS measures acquired using different MRS acquisition sequences. Single‐voxel SPECIAL and MEGA‐PRESS MR spectra were acquired from both the dorsolateral prefrontal cortex and primary motor cortices in 15 healthy subjects. The SPECIAL spectrum, as well as both the edit‐off and difference spectra of MEGA‐PRESS were each analyzed in LCModel to obtain estimates of the absolute concentrations of total choline (TCh; glycerophosphocholine + phosphocholine), total creatine (TCr; creatine + phosphocreatine), N‐acetylaspartate (NAA), N‐acetylaspartylglutamate (NAAG), NAA + NAAG, glutamate (Glu), glutamine (Gln), Glu + Gln, scyllo‐inositol (Scyllo), myo‐inositol (Ins), glutathione (GSH), γ‐aminobutyric acid (GABA), lactate (Lac) and aspartate (Asp). Then, having obtained up to three independent measures of each metabolite per brain region per subject, correlations between the different measures were assessed. As expected, metabolites with the most prominent spectral peaks had the most well‐correlated measures between methods, while metabolites with less prominent spectral peaks tended to have poorly‐correlated measures between methods. Given that the ground truth for in vivo MRS measures is never known, the method proposed here provides a promising means to assess the validity of in vivo MRS measures, which has not yet been explored widely.
Dhamala, E., Abdelkefi, I., Nguyen, M., Hennessy, T. J., Nadeau, H., & Near, J. (2019). Validation of in vivo MRS measures of metabolite concentrations in the human brain. NMR in Biomedicine, 32(3), e4058.