Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

Abstract

Using group‐level functional parcellations and constant‐length sliding window analysis, dynamic functional connectivity studies have revealed network‐specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual‐level ageing‐related brain dynamics are uncertain. Here, we performed individual parcellation and individual‐length sliding window clustering to characterize ageing‐related dynamic network changes. Healthy participants ( n = 637, 18–88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven‐network parcellation, varied from group‐level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual‐length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U‐shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network‐specific and individual‐level targeting during neuromodulation in ageing and ageing‐related diseases.

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“…The individual functional brain network mapping process was performed using the HFR_ai toolkit [31]. The preprocessed rs-fMRI time series were iteratively clustered and matched to the group-level 18-network parcellation [47].…”

“…The data preprocessing pipeline has been described in our previous study (32). T1 image preprocessing was performed using FreeSurfer software (version 6.0.0), and functional image preprocessing was performed using the FSL toolkit (version 5.0.2).…”

“…That is, functional networks are not in the same anatomical positions across individuals. Previous studies have shown that individual functional mapping can better capture brainbehavior associations than group-level functional mapping [31], characterize individualized brain dynamics in healthy aging [32], and provide candidate neuroimaging markers for clinical psychiatric symptoms across different mental disorders [33]. Although individual mapping has been emphasized in transcranial magnetic stimulation (TMS) studies [34], individual differences in the same tACS montages are unclear.…”