Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations
Individual differences in brain anatomy can predict variability in cognitive function. Most studies have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) differences using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. Here, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across neuroanatomical properties in healthy young adults and typically developing children. We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalise across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalisable without ICV correction, while models based on cortical thickness are more generalisable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranium volumes from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.
Dhamala E, Ooi LQR, Chen J, Kong R, Anderson KM, Yeo BTT, Holmes AJ. (2022). Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations. BioRxiv
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 KW, Jaywant A, Kuceyeski A. (2022). Shared functional connections within and between cortical networks predict individual cognitive abilities in males and females. Human Brain Mapping
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.
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.
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.