Amyloid and tau accumulate across distinct spatial networks and are differentially associated with brain connectivity

The abnormal accumulation of amyloid-β and tau targets specific spatial networks in Alzheimer's disease. However, the relationship between these networks across different disease stages and their association with brain connectivity has not been explored. In this study, we applied a joint independent component analysis to ¹⁸F- Flutemetamol (amyloid-β) and ¹⁸F-Flortaucipir (tau) PET images to identify amyloid-β and tau networks across different stages of Alzheimer's disease. We then assessed whether these patterns were associated with resting-state functional networks and white matter tracts. Our analyses revealed nine patterns that were linked across tau and amyloid-β data. The amyloid-β and tau patterns showed a fair to moderate overlap with distinct functional networks but only tau was associated with white matter integrity loss and multiple cognitive functions. These findings show that amyloid-β and tau have different spatial affinities, which can be used to understand how they accumulate in the brain and potentially damage the brain's connections.

Shared and unique lifetime stressor characteristics and network connectivity predict adolescent anxiety and depression

Background

Exposure to major life stressors and aberrant functional connectivity have been linked to anxiety and depression, especially during adolescence. However, whether specific characteristics of life stressors and functional network connectivity act together to differentially predict anxiety and depression symptoms remains unclear.

Methods

We utilized baseline lifetime stressor exposure and resting-state functional magnetic resonance imaging data in a longitudinal sample of 107 adolescents enriched for anxiety and depressive disorders. We examined five stressor characteristics: physical danger, interpersonal loss, humiliation, entrapment, and role change/disruption. Anxiety and depression symptoms were assessed at baseline, 6-month and 12-month follow-ups. Linear mixed-effect models tested if lifetime severity of these stressor characteristics, functional connectivity within and between frontoparietal, default, and ventral attention networks, and their interactions differentially predicted anxiety and depression symptoms at two 6-month follow-ups.

Results

Greater lifetime severity of physical danger and humiliation predicted higher anxiety symptoms. Greater lifetime entrapment severity predicted higher anxiety and depression symptoms. After including within- and between-network functional connectivity and other predictive characteristics, only the effects of lifetime entrapment severity remained significant. Lifetime entrapment severity more strongly predicted anxiety symptoms in youth with higher default network connectivity. Greater functional connectivity between frontoparietal and default networks predicted increased depression symptoms.

Conclusions

Our study is the first to use lifetime severity of distinct stressor characteristics and resting-state functional connectivity jointly to predict adolescent anxiety and depression symptoms. These results imply certain stressor characteristics and functional connectivity metrics as specific predictors of anxiety or depression and highlight entrapment as a shared predictor for anxiety and depression.

Disrupted Functional Brain Network Architecture in Sufferers with Boxing-Related Repeated Mild Traumatic Brain Injury: A Resting-State EEG Study

BACKGROUND

Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG).

METHODS

Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach.

RESULTS

Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers.

CONCLUSIONS

This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

Resting-state network alterations in depression: a comprehensive meta-analysis of functional connectivity

BACKGROUND

Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.

METHODS

A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.

RESULTS

A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN-FPN and limbic network (LN)-DMN connectivity, decreased DMN-somatomotor network and LN-FPN connectivity, and varied ventral attention network (VAN)-dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.

CONCLUSIONS

These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.