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Background The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. Methods We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case–control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. Results In UKB, reductions in network efficiency were observed in MDD cases globally (d = −0.076, pFDR = 0.033), across all tiers (d = −0.069 to −0.079, pFDR = 0.020), and in hubs (d = −0.080 to −0.113, pFDR = 0.013–0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. Conclusion Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls. [ABSTRACT FROM AUTHOR]

There is increasing interest in using data‐driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K‐nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case–control status in either cohort (GS subsample: pFDR =.2239–.6585; UKB: pFDR =.2003–.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529–.3490, pFDR <.005; UKB: d = 0.0868–0.1070, pFDR <.005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case–control status. [ABSTRACT FROM AUTHOR]

The Restore Ministries case presents the story of a social entrepreneur, Scott Reall, who successfully created Restore, a behavioral healthcare venture, within the YMCA. Despite the initial collaborative success, Restore was at a crossroad. Growth following a "ministry mindset," a mindset in which the goal was to meet everyone's needs regardless of their ability to pay, had produced an inconsistent fee structure, confusion among stakeholders, and branding questions. It also created concerns over business model scalability and sustainability. A key question was whether Restore and the Y were adequately prepared to undertake Reall's vision to the next stage of growth? [ABSTRACT FROM AUTHOR]

The article presents a study which looks into the population status of protected seahorses that has been threatened by unsustainable collection of swimming net enclosure which led to habitat degradation and incidental bycatch in New South Wales, Australia. It reveals a significant decline of the species after the swimming net was replaced. It notes the clear relationship between the abundant population of Hippocampus whitei and epibiotic growth on a swimming net.

Reciprocating gait orthoses (RGOs) have long been used for orthotic management in patients with higher level spinal cord injuries, with one primary limitation in ambulation being attributable to the absence of knee flexion in swing phase. As a result, an individual is forced to use compensatory upper body motions to advance the legs. The authors report the use of stance-control knee-ankle-foot orthoses in combination with an isocentric RGO in a patient with a T10 spinal cord injury. A comparison is made between the scenarios of having the knees locked during the entire gait cycle to that of allowing the knees to flex freely during the swing phase, yet still be locked for stability during stance. Qualitative observation and kinematic three-dimensional gait data demonstrate that this subject ambulated with a faster, more efficient gait pattern with the stance-control feature activated. Despite having no voluntary control of his knees, this orthotic option afforded him the ability to walk safely and smoothly with both knees flexing during swing and with less upper body compensation. [ABSTRACT FROM AUTHOR]

Seahorses and their syngnathid relatives have provided a focus for efforts to ensure sustainable use of marine resources, with new international trade controls (CITES Appendix II) implemented in May 2004. We demonstrate how a study of international trade can be used to assess relative levels of threat and set domestic research and conservation priorities. Australia has remarkably high syngnathid biodiversity with at least 14 seahorse species, two endemic sea dragon species, and 90 species of pipefishes and pipehorses found in its territorial waters. Our objectives were to quantify species, trade routes, volumes, values and temporal trends in syngnathid trade to and from Australia. We found that Australia is probably the major global supplier of dried pipehorses Solegnathus spp.. These fishes, including at least one endemic species, are sourced from trawl bycatch and comprise Australia's largest syngnathid export, by both volume and value. Research is urgently needed to evaluate the impacts and sustainability of trawling on pipehorse populations. Australia is also the sole supplier of two sea dragon species, Phycodurus eques and Phyllopteryx taeniolatus, for the live aquarium trade. Although lucrative, the number of wild-caught individuals involved in this trade was relatively low and probably of low conservation risk relative to habitat loss. Exports of seahorses and other pipefish species, and imports of all syngnathid species, are minor on a global scale, although the burgeoning aquaculture industry for seahorses requires careful evaluation for its potential impacts on wild populations. [ABSTRACT FROM AUTHOR]

A counseling program for severely disabled elderly persons was developed consisting of cognitive group therapy by telephone. The intent of the program was to solve problems related to feeling dissatisfied, lonely, or being too inactive to remain healthy. A majority of the treated group members reported fewer feelings of loneliness after treatment. High goal attainment observed in the treated groups suggests that research should explore the potential of treating affective problems with scheduled phone contact. [ABSTRACT FROM PUBLISHER]

Measuring transient functional connectivity is an important challenge in Electroencephalogram (EEG) research. Here, the rich potential for insightful, discriminative information of brain activity offered by high temporal resolution is confounded by the inherent noise of the medium and the spurious nature of correlations computed over short temporal windows. We propose a novel methodology to overcome these problems called Filter Average Short-Term (FAST) functional connectivity. First, long-term, stable, functional connectivity is averaged across an entire study cohort for a given pair of Visual Short Term Memory (VSTM) tasks. The resulting average connectivity matrix, containing information on the strongest general connections for the tasks, is used as a filter to analyse the transient high temporal resolution functional connectivity of individual subjects. In simulations, we show that this method accurately discriminates differences in noisy Event-Related Potentials (ERPs) between two conditions where standard connectivity and other comparable methods fail. We then apply this to analyse activity related to visual short-term memory binding deficits in two cohorts of familial and sporadic Alzheimer's disease. Reproducible significant differences were found in the binding task with no significant difference in the shape task in the P300 ERP range. This allows new sensitive measurements of transient functional connectivity, which can be implemented to obtain results of clinical significance.

Alzheimer's Disease (AD) shows both complex alterations of functional dependencies between brain regions and a decreased ability to perform Visual Short-Term Memory Binding (VSTMB) tasks. Recent advances in network neuroscience toward understanding the complexity of hierarchical brain function here enables us to establish a link between these two phenomena. Here, we study data on two types of dementia at Mild Cognitive Impairment (MCI) stage-familial AD patients (E280A mutation of the presenilin-1 gene) and elderly MCI patients at high risk of sporadic AD, both with age-matched controls. We analyzed Electroencephalogram (EEG) signals recorded during the performance of Visual Short-Term Memory (VSTM) tasks by these participants. Functional connectivity was computed using the phase-lag index in Alpha and Beta; and network analysis was employed using network indices of hierarchical spread (degree variance) and complexity. Hierarchical characteristics of EEG functional connectivity networks revealed abnormal patterns in familial MCI VSTMB function and sporadic MCI VSTMB function. The middle-aged familial MCI binding network displayed a larger degree variance in lower Beta compared to healthy controls ( p = 0.0051 , Cohen's d = 1.0124), while the elderly sporadic MCI binding network displayed greater hierarchical complexity in Alpha ( p = 0.0140 , Cohen's d = 1.1627). Characteristics in healthy aging were not shown to differ. These results indicate that activity in MCI exhibits cross-frequency network reorganization characterized by increased heterogeneity of node roles in the functional hierarchy. Aging itself is not found to cause VSTM functional hierarchy differences., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Smith, Starr, Escudero, Ibañez and Parra.)

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm., (© The Author(s) 2020. Published by Oxford University Press.)

With several initiatives well underway towards amassing large and high-quality population-based neuroimaging datasets, deep learning is set to push the boundaries of what is possible in classification and prediction in neuroimaging studies. This includes those that derive increasingly popular structural connectomes, which map out the connections (and their relative strengths) between brain regions. Here, we test different Convolutional Neural Network (CNN) models in a benchmark sex prediction task in a large sample of N=3,152 structural connectomes acquired from the UK Biobank, and compare results across different connectome processing choices. The best results (76.5% test accuracy) were achieved using Fractional Anisotropy (FA) weighted connectomes, without sparsification, and with a simple weight normalisation through division by the maximum FA value. We also confirm that for structural connectomes, a Graph CNN approach, the recently proposed BrainNetCNN, outperforms an image-based CNN.