Alteration of the Intra- and Inter-Lobe Connectivity of the Brain Structural Network in Normal Aging

Enhanced reciprocal connections of the prefrontoparietal-thalamo-hippocampal circuit in older adults

Cognitive functions rely on specific brain circuits involving cortical and subcortical regions. However, how age-related changes in effective connectivity within the prefrontoparietal-thalamo-hippocampal circuit affect cognition remains unclear. This study included 143 healthy older adults (60 to 88 yrs) and 124 young adults (18 to 44 yrs), using regression dynamic causal modeling to analyze resting-state functional magnetic resonance imaging data. Compared to young adults, older adults showed increased reciprocal effective connectivity within the ventromedial prefrontal cortex-lateral thalamic nuclei-right caudal hippocampus circuit. Enhanced bidirectional connectivity was also observed within the frontoparietal network, between the pregenual cingulate gyrus and superior frontal gyrus, and between lateral thalamic nuclei (LTN) and frontal pole. Additionally, heightened connectivity between thalamus and hippocampus negatively correlated with cognitive performance. Our findings reveal significant age-related increases in effective connectivity within the prefrontoparietal-thalamo-hippocampal circuit, linked to cognitive performance levels. Increased connectivity may indicate compensatory mechanisms helping preserve cognitive function in healthy aging. These results advance our understanding of neural dynamics underlying cognitive aging and potential adaptive mechanisms in older adults.

Dissociation of focal and large-scale inhibitory functions in the older adults: A multimodal MRI study

BACKGROUND

The decline of inhibitory in cognitive aging is linked to reduced cognitive and mental capacities in older adults. However, this decline often shows inconsistent clinical presentations, suggesting varied impacts on different inhibition-related tasks. Inhibitory control, a multifaceted construct, involves various types of inhibition. Understanding these components is crucial for comprehending how aging affects inhibitory functions. Our research investigates the influences of aging on large-scale and focal-scale inhibitory and examines the relationship with brain markers.

METHODS

We examined the impact of aging on inhibitory in 18 younger (20-35 years) and 17 older adults (65-85 years) using focal and large-scale inhibition tasks. The Gabor task assessed focal-scale inhibition, while the Stop Signal Task (SST) evaluated large-scale inhibition. Participants underwent neuropsychological assessments and MRI scans, including magnetic resonance spectroscopy (MRS) and structural and resting fMRI.

RESULTS

Older adults exhibited a marked decline in inhibitory function, with slower SST responses indicating compromised large-scale inhibition. Conversely, the Gabor task showed no significant age-related changes. MRS findings revealed decreased levels of GABA, glutamate, glutamine, and NAA in the pre-SMA, correlating with observed large-scale inhibition in older adults. Additionally, pre-SMA seed-based functional connectivity analysis showed reduced brain network connections in older adults, potentially contributing to inhibitory control deficits.

CONCLUSIONS

Our study elucidates the differential effects of aging on inhibitory functions. While large-scale inhibition is more vulnerable to aging, focal-scale inhibition is relatively preserved. These findings highlight the importance of targeted cognitive interventions and underscore the necessity of a multifaceted approach in aging research.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Brain structure and working memory adaptations associated with maturation and aging in mice

Introduction

As the population skews toward older age, elucidating mechanisms underlying human brain aging becomes imperative. Structural MRI has facilitated non-invasive investigation of lifespan brain morphology changes, yet this domain remains uncharacterized in rodents despite increasing use as models of disordered human brain aging.

Methods

Young (2m, n = 10), middle-age (10m, n = 10) and old (22m, n = 9) mice were utilized for maturational (young vs. middle-age) and aging-related (middle-age vs. old mice) comparisons. Regional brain volume was averaged across hemispheres and reduced to 32 brain regions. Pairwise group differences in regional volume were tested using general linear models, with total brain volume as a covariate. Sample-wide associations between regional brain volume and Y-maze performance were assessed using logistic regression, residualized for total brain volume. Both analyses corrected for multiple comparisons. Structural covariance networks were generated using the R package "igraph." Group differences in network centrality (degree), integration (mean distance), and segregation (transitivity, modularity) were tested across network densities (5-40%), using 5,000 (1,000 for degree) permutations with significance criteria of p < 0.05 at ≥5 consecutive density thresholds.

Results

Widespread significant maturational changes in volume occurred in 18 brain regions, including considerable loss in isocortex regions and increases in brainstem regions and white matter tracts. The aging-related comparison yielded 6 significant changes in brain volume, including further loss in isocortex regions and increases in white matter tracts. No significant volume changes were observed across either comparison for subcortical regions. Additionally, smaller volume of the anterior cingulate area (χ² = 2.325, p_BH = 0.044) and larger volume of the hippocampal formation (χ² = -2.180, p_BH = 0.044) were associated with poorer cognitive performance. Maturational network comparisons yielded significant degree changes in 9 regions, but no aging-related changes, aligning with network stabilization trends in humans. Maturational decline in modularity occurred (24-29% density), mirroring human trends of decreased segregation in young adulthood, while mean distance and transitivity remained stable.

Conclusion/Implications

These findings offer a foundational account of age effects on brain volume, structural brain networks, and working memory in mice, informing future work in facilitating translation between rodent models and human brain aging.

Age-Related Differences of Cortical Topology Across the Adult Lifespan: Evidence From a Multisite MRI Study With 1427 Individuals

BACKGROUND

Healthy aging is usually accompanied by alterations in brain network architecture, influencing information processing and cognitive performance. However, age-associated coordination patterns of morphological networks and cognitive variation are not well understood.

PURPOSE

To investigate the age-related differences of cortical topology in morphological brain networks from multiple perspectives.

STUDY TYPE

Prospective, observational multisite study.

POPULATION

A total of 1427 healthy participants (59.1% female, 51.75 ± 19.82 years old) from public datasets.

FIELD STRENGTH/SEQUENCE

1.5 T/3 T, T1-weighted magnetization prepared rapid gradient echo (MP-RAGE) sequence.

ASSESSMENT

The multimodal parcellation atlas was used to define regions of interest (ROIs). The Jensen-Shannon divergence-based individual morphological networks were constructed by estimating the interregional similarity of cortical thickness distribution. Graph-theory based global network properties were then calculated, followed by ROI analysis (including global/nodal topological analysis and hub analysis) with statistical tests.

STATISTICAL TESTS

Chi-square test, Jensen-Shannon divergence-based similarity measurement, general linear model with false discovery rate correction. Significance was set at P < 0.05.

RESULTS

The clustering coefficient (q = 0.016), global efficiency (q = 0.007), and small-worldness (q = 0.006) were significantly negatively quadratic correlated with age. The group-level hubs of seven age groups were found mainly distributed in default mode network, visual network, salient network, and somatosensory motor network (the sum of these hubs' distribution in each group exceeds 55%). Further ROI-wise analysis showed significant nodal trajectories of intramodular connectivities.

DATA CONCLUSION

These results demonstrated the age-associated reconfiguration of morphological networks. Specifically, network segregation/integration had an inverted U-shaped relationship with age, which indicated age-related differences in transmission efficiency.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.

Entropy in Brain Networks

A thorough and comprehensive understanding of the human brain ultimately depends on knowledge of large-scale brain organization[...].