Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects

Abstract

Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.

Keywords: fluid intelligence; healthy aging; linked independent component analysis; multimodal fusion; neuroimaging.

FIGURE 1

Summary of processing and analysis of the imaging modalities, comprising functional, cerebrovascular, and structural measurements. ASL, arterial spin labeling; CBF, cerebral blood flow; DMN, default mode network; FPN, frontoparietal network; GMV, grey matter volume; ICA, independent component analysis; RSFA, resting state fluctuation amplitude; rsfMRI, resting‐state functional magnetic resonance imaging; SN, salience network; T1w, T1‐weighted

FIGURE 2

Scatter plots showing the correlation between age and fluid intelligence measured by Cattell test score in the CC280 main sample (n = 215) and CC420 validation sample (n = 433)

FIGURE 3

(a) The group‐average spatial maps associated with the default mode network, the salience network, and the lateralized frontoparietal networks, generated from group‐level independent component analysis of 648 subjects from Cam‐CAN cohort stage 2. (b) the group‐average spatial maps of cerebrovascular and structural neuroimaging modality inputs to linked ICA, including resting state fluctuation amplitude and cerebral blood flow as cerebrovascular measurements, and grey matter volume as a structural measurement

FIGURE 4

The relative weight of modalities in each component generated from linked independent component analysis (ICA) and the percentage of variance explained of each component of the CC280 main analysis (n = 215). Note that most components are dominated by one modality. Two columns on the right show the spatial correlation coefficients between each of linked ICA components of the CC280 main sample and split‐sample validation sub‐sample 1 (split‐sample 1, n = 108), and main sample and split‐sample validation sub‐sample 2 (split‐sample 2, n = 107). CBF, cerebral blood flow; DMN, default mode network; FPN, frontoparietal network; GMV, grey matter volume; SN, salience network; RSFA, resting‐state fluctuation amplitude

FIGURE 5

Degree of fusion between the seven neuroimaging modalities included in linked independent component analysis (ICA) CC280 (n = 215) with 30, 40, and 50 components, respectively. Greater number (i.e., darker color) in the matrix represents more fusion found between the two modalities in linked ICA output components. CBF, cerebral blood flow; DMN, default mode network; FPNr, right frontoparietal network; FPNl, left frontoparietal network; GMV, grey matter volume; SN, salience network; RSFA, resting‐state fluctuation amplitude

FIGURE 6

Linked ICA weighted spatial maps for five components showing strong age effects on subject loadings in CC280 main analysis (n = 215). Warm and cold color scheme indicate positive and negative association with linked ICA subject loadings, respectively. For example, an individual with a high loading value on IC1, that is, young adult, had (i) high whole brain GMV, (ii) high perfusion in visual cortex and posterior cingulate cortex (PCC) coupled with low perfusion in middle temporal gyrus and (iii) high RSFA in dorsolateral prefrontal cortex and PCC, coupled with low RSFA values in areas proximal to vascular and cerebrospinal fluid territories (venous sinuses and middle cerebral arteries). The brain figures depict the weighted spatial maps and the accompanying scatter plots show models of age plotted against IC subject loadings. Nonlinear changes in relation to age were observed in IC4, IC7, and IC22 (refer to Table 2 for regression results). For visualization the spatial map threshold is set to 3 < |Z| < 10. CBF, cerebral blood flow; GMV, grey matter volume; RSFA, resting‐state fluctuation amplitude

FIGURE 7

Linked ICA weighted spatial maps for three components showing unique associations with Cattell test score in CC280 main analysis (n = 215). Warm and cold color scheme indicate positive and negative association with linked ICA subject loadings, respectively. The accompanying scatter plots show models of Cattell test score plotted against IC subject loadings. One component reflects signals from structural and cerebrovascular measurements: IC1 that reflects grey matter volume (GMV), cerebral blood flow (CBF) and resting state fluctuation amplitude (RSFA) (see Figure 6 and main text for further interpretation). Two components reflect signals from functional networks: IC16 that reflects the right frontoparietal network (FPN) and IC17 which reflects the left FPN. For visualization the spatial map threshold is set to 3 < |Z| < 10. Similar components of IC1 and IC16 were found in CC420 out‐of‐sample validation analysis (supplementary materials S1) to be associated with fluid intelligence