Hemodynamic latency is associated with reduced intelligence across the lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and cognitive ability

Toward precision brain health: accurate prediction of a cognitive index trajectory using neuroimaging metrics

The goal of precision brain health is to accurately predict individuals' longitudinal patterns of brain change. We trained a machine learning model to predict changes in a cognitive index of brain health from neurophysiologic metrics. A total of 48 participants (ages 21-65) completed a sensorimotor task during 2 functional magnetic resonance imaging sessions 6 mo apart. Hemodynamic response functions (HRFs) were parameterized using traditional (amplitude, dispersion, latency) and novel (curvature, canonicality) metrics, serving as inputs to a neural network model that predicted gain on indices of brain health (cognitive factor scores) for each participant. The optimal neural network model successfully predicted substantial gain on the cognitive index of brain health with 90% accuracy (determined by 5-fold cross-validation) from 3 HRF parameters: amplitude change, dispersion change, and similarity to a canonical HRF shape at baseline. For individuals with canonical baseline HRFs, substantial gain in the index is overwhelmingly predicted by decreases in HRF amplitude. For individuals with non-canonical baseline HRFs, substantial gain in the index is predicted by congruent changes in both HRF amplitude and dispersion. Our results illustrate that neuroimaging measures can track cognitive indices in healthy states, and that machine learning approaches using novel metrics take important steps toward precision brain health.

The confound of hemodynamic response function variability in human resting-state functional MRI studies

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences

Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or neural networks models designed to leverage the properties of graph representations, have recently shown promise in task fMRI decoding studies. Here, we propose an end-to-end graph convolutional network (GCN) framework with three convolutional layers to classify task fMRI data from the Human Connectome Project dataset. We compared the predictive performance of our GCN model across four of the most widely used node embedding algorithms—NetMF, RandNE, Node2Vec, and Walklets—to automatically extract the structural properties of the nodes in the functional graph. The empirical results indicated that our GCN framework accurately predicted individual differences (0.978 and 0.976) with the NetMF and RandNE embedding methods, respectively. Furthermore, to assess the effects of individual differences, we tested the classification performance of the model on sub-datasets divided according to gender and fluid intelligence. Experimental results indicated significant differences in the classification predictions of gender, but not high/low fluid intelligence fMRI data. Our experiments yielded promising results and demonstrated the superior ability of our GCN in modeling task fMRI data.

DCE-MRI of Brain Fluid Barriers: In Vivo Water Cycling at the Human Choroid Plexus

Metabolic deficits at brain-fluid barriers are an increasingly recognized feature of cognitive decline in older adults. At the blood-cerebrospinal fluid barrier, water is transported across the choroid plexus (CP) epithelium against large osmotic gradients via processes tightly coupled to activity of the sodium/potassium pump. Here, we quantify CP homeostatic water exchange using dynamic contrast-enhanced MRI and investigate the association of the water efflux rate constant (k_co) with cognitive dysfunction in older individuals. Temporal changes in the longitudinal relaxation rate constant (R₁) after contrast agent bolus injection were measured in a CP region of interest in 11 participants with mild cognitive dysfunction [CI; 73 ± 6 years] and 28 healthy controls [CN; 72 ± 7 years]. k_co was determined from a modified two-site pharmacokinetic exchange analysis of the R₁ time-course. K^trans, a measure of contrast agent extravasation to the interstitial space was also determined. Cognitive function was assessed by neuropsychological test performance. k_co averages 5.8 ± 2.7 s^-1 in CN individuals and is reduced by 2.4 s^-1 [ca. 40%] in CI subjects. Significant associations of k_co with global cognition and multiple cognitive domains are observed. K^trans averages 0.13 ± 0.07 min^-1 and declines with age [-0.006 ± 0.002 min^-1 yr^-1], but shows no difference between CI and CN individuals or association with cognitive performance. Our findings suggest that the CP water efflux rate constant is associated with cognitive dysfunction and shows an age-related decline in later life, consistent with the metabolic disturbances that characterize brain aging.