Age-related reduction in anxiety and neural encoding of negative emotional memory

Introduction

Older adults experience less anxiety. We examined how memory of negative emotional images varied with age and may reflect age-related differences in anxiety.

Methods

Fifty-one adults, age 22-80 years, underwent imaging with a memory task where negative and neutral images were displayed pseudo-randomly. They were queried post-scan about the images inter-mixed with an equal number of images never displayed. Sensitivity (d') and reporting bias (Z-score of false alarm rate; Z[FAR]) were quantified with signal detection theory.

Results

Age was negatively correlated with both Spielberg State Trait Anxiety Inventory (STAI) state score and d' (negative - neutral) and positively with Z[FAR] (negative - neutral). However, STAI score and d' or Z[FAR] (negative - neutral) were not significantly correlated. In whole-brain regression, STAI score was correlated with higher activity of the right middle/superior temporal gyri/temporal parietal junction (MTG/STG/TPJ) for "negative correct - incorrect" - "neutral correct - incorrect" trials. Further, the MTG/STG/TPJ activity (β) was also negatively correlated with age. Mediation analyses supported a complete mediation model of age → less anxiety → less MTG/STG/TPJ β.

Discussion

Together, the findings demonstrated age-related changes in negative emotional memory and how age-related reduction in anxiety is reflected in diminished temporoparietal cortical activities during encoding of negative emotional memory.

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.

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

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.