Integrating multimodal connectivity improves prediction of individual cognitive abilities
How white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information – at both a model input or output level – significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly better than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities.
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.