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In recent years, research on normatively positive social tipping dynamics in response to the climate crisis has produced invaluable insights. In contrast, relatively little attention has been given to the potentially negative social tipping processes that might unfold due to an increasingly destabilized Earth system and to how they might in turn reinforce social and ecological destabilization dynamics and/or impede positive social change. In this paper, we discuss selected potential negative social tipping processes (anomie, radicalization and polarization, displacement, conflict, and financial destabilization) linked to Earth system destabilization. We draw on related research to understand the drivers and likelihood of these negative tipping dynamics, their potential effects on human societies and the Earth system, and the potential for cascading interactions (e.g. food insecurity and displacement) contributing to systemic risks. This first attempt to provide an explorative conceptualization and empirical account of potential negative social tipping dynamics linked to Earth system destabilization is intended to motivate further research into an under-studied area that is nonetheless crucial for our ability to respond to the climate crisis and for ensuring that positive social tipping dynamics are not averted by negative ones.

Abstract Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed designed to simulate an urban environment. Participants were alerted to an incoming braking event via an audio countdown to elicit anticipatory potentials that were measured using an EEG. The CSNN’s performance was compared to a standard CNN, EEGNet and three graph neural networks via 10-fold cross-validation. The CSNN outperformed all the other neural networks, and had a predictive accuracy of 99.06% with a true positive rate of 98.50%, a true negative rate of 99.20% and an F1-score of 0.98. Performance of the CSNN was comparable to the CNN in an ablation study using a subset of EEG channels that localized SCPs. Classification performance of the CSNN degraded only slightly when the floating-point EEG data were converted into spike trains via delta modulation to mimic synaptic connections.

Abstract Expanded intronic G4C2 repeats in the C9ORF72 gene cause amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). These intronic repeats are translated through a non-AUG-dependent mechanism into five different dipeptide repeat proteins (DPRs), including poly-glycine-arginine (GR), which is aggregation-prone and neurotoxic. Here, we report that Kapβ2 and GR interact, co-aggregating, in cultured neurons in-vitro and CNS tissue in-vivo. Importantly, this interaction significantly decreased the risk of death of cultured GR-expressing neurons. Downregulation of Kapβ2 is detrimental to their survival, whereas increased Kapβ2 levels mitigated GR-mediated neurotoxicity. As expected, GR-expressing neurons displayed TDP-43 nuclear loss. Raising Kapβ2 levels did not restore TDP-43 into the nucleus, nor did alter the dynamic properties of GR aggregates. Overall, our findings support the design of therapeutic strategies aimed at up-regulating Kapβ2 expression levels as a potential new avenue for contrasting neurodegeneration in C9orf72-ALS/FTD.

Summary: The ALS/FTD-linked intronic hexanucleotide repeat expansion in the C9orf72 gene is aberrantly translated in the sense and antisense directions into dipeptide repeat proteins, among which poly proline-arginine (PR) displays the most aggressive neurotoxicity in-vitro and in-vivo. PR partitions to the nucleus when heterologously expressed in neurons and other cell types. We show that by lessening the nuclear accumulation of PR, we can drastically reduce its neurotoxicity. PR strongly accumulates in the nucleolus, a nuclear structure critical in regulating the cell stress response. We determined that, in neurons, PR caused nucleolar stress and increased levels of the transcription factor p53. Downregulating p53 levels also prevented PR-mediated neurotoxicity both in in-vitro and in-vivo models. We investigated if PR could induce the senescence phenotype in neurons. However, we did not observe any indications of such an effect. Instead, we found evidence for the induction of programmed cell death via caspase-3 activation.

Oppositional defiant disorder and conduct disorder, collectively referred to as disruptive behavior disorders (DBDs), are prevalent psychiatric disorders in children. Early diagnosis of DBDs is crucial because they can increase the risks of other mental health and substance use disorders without appropriate psychosocial interventions and treatment. However, diagnosing DBDs is challenging as they are often comorbid with other disorders, such as attention-deficit/hyperactivity disorder, anxiety, and depression. In this study, a multimodal ensemble three-dimensional convolutional neural network (3D CNN) deep learning model was used to classify children with DBDs and typically developing children. The study participants included 419 females and 681 males, aged 108–131 months who were enrolled in the Adolescent Brain Cognitive Development Study. Children were grouped based on the presence of DBDs (n = 550) and typically developing (n = 550); assessments were based on the scores from the Child Behavior Checklist and on the Schedule for Affective Disorders and Schizophrenia for School-age Children-Present and Lifetime version for DSM-5. The diffusion, structural, and resting-state functional magnetic resonance imaging (rs-fMRI) data were used as input data to the 3D CNN. The model achieved 72% accuracy in classifying children with DBDs with 70% sensitivity, 72% specificity, and an F1-score of 70. In addition, the discriminative power of the classifier was investigated by identifying the cortical and subcortical regions primarily involved in the prediction of DBDs using a gradient-weighted class activation mapping method. The classification results were compared with those obtained using the three neuroimaging modalities individually, and a connectome-based graph CNN and a multi-scale recurrent neural network using only the rs-fMRI data.

Abstract Functional magnetic resonance imaging has revealed correlated activities in brain regions even in the absence of a task. Initial studies assumed this resting-state functional connectivity (FC) to be stationary in nature, but recent studies have modeled these activities as a dynamic network. Dynamic spatiotemporal models better model the brain activities, but are computationally more involved. A comparison of static and dynamic FCs was made to quantitatively study their efficacies in identifying intrinsic individual connectivity patterns using data from the Human Connectome Project. Results show that the intrinsic individual brain connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and is more accurately captured with partial correlation and assuming static FC. It was also seen that the intrinsic individual brain connectivity patterns were invariant over a few months. Additionally, biological sex identification was successfully performed using the intrinsic individual connectivity patterns, and group averages of male and female FC matrices. Edge consistency, edge variability and differential power measures were used to identify the major resting-state networks involved in identifying subjects and their sex.

Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain’s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.