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 agerelated 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.

Frontoparietal network integrity supports cognitive function despite atrophy and hypoperfusion in pre-symptomatic frontotemporal dementia: multimodal analysis of brain function, structure and perfusion

“…The decreases in CBF and default mode network activity are consistent with the commonly observed changes in perfusion [52, 53] and default mode network [54] in normal aging. We also demonstrated a significant decrease with age in global GMV, consistent with previous multimodal neuroimaging fusion studies [55–57] and normal aging pattern of the brain [58]. More importantly, we illustrated the age- and cognition-relevant divergence of frontoparietal network integrity between pre-symptomatic genetic mutation carriers and non-carriers.…”

“…The stability of the estimated ICs was evaluated across 100 ICASSO iterations [41]. Functional networks were identified from components by visualization and validated by spatially matching the components to pre-existing templates [42], in accordance with the previous methodology used to identify networks from ICs [43, 44]. The dorsal and ventral default mode network, the salience network, and the left and right frontoparietal network were selected, which are higher-order functional networks known to be associated with age- and FTD-related cognitive change [45–47].…”

Cerebrovascular reactivity impairment in genetic frontotemporal dementia

“…It is also plausible that the RSFA signal in ICA-identified regions captures multiple sources with different aetiology, particularly at boundaries of large vessels and adjacent perivascular space, that may exhibit different spontaneous brain activity at rest [67]. The latter illustrates the challenge of dissociating spatially overlapping sources of signal using univariate methods and motivate the use of data-driven and multimodal approaches [68], as underscored by our findings.…”

“…This null result may be caused by small and unbalanced sub-groups per mutated gene but may also imply true commonalities in the vascular pathology downstream of the mutations’ molecular pathology. Previous neuroimaging studies have revealed gene mutation-specific brain changes in FTD [56, 69]. The CVR changes in frontal regions may reflect distinct mechanisms from the atrophy and perfusion alterations in temporal areas discovered in earlier FTD investigations.…”

“…Thus, future investigations in the GENFI sample should clarify which vascular factors drive the RSFA changes reported here by adopting other means to quantify cerebrovascular function, such as resting arterial-spin labelling (ASL)-CBF and WMH burden on MRI. Another avenue for future efforts is to complement current analyses with estimates of functional and WM integrity, as well as CSF and blood markers in relation to cognitive decline [29] in a multi-modal manner [68,75]. On a clinical level, using integrative approaches to uncover protective factors in prodromal stages of disease may improve prognosis and inform stratification procedures, future triallists, patients, and carers.…”