Sunday, April 7th, 2024

Time	Event
2:19a	Functional divergence between the two cerebral hemispheres contributes to human fluid intelligence Hemispheric lateralization is linked to potential cognitive advantages. It is considered a driving force behind the generation of human intelligence. However, establishing quantitative links between the degree of lateralization and intelligence in humans remains elusive. In this study, we propose a novel framework that utilizes the hyperaligned multidimensional representation space derived from hemispheric functional gradients to compute between-hemisphere distances within this space. Our analysis improves the functional alignment between the hemispheres, making it possible to delineate aspects of human brain lateralization that relate to individual differences in cognitive ability more precisely. Applying this framework to the Human Connectome Project (HCP) large cohort (N = 777) identified the highest functional divergence within the frontoparietal control network across the two hemispheres. We found that spatial variability in between-hemisphere functional divergence aligned with the lateralized response patterns across multiple tasks, cortical myelination and evolutionary expansion. Furthermore, both global divergence between the cerebral hemispheres and regional divergence within the multiple demand network were positively associated with fluid composite score and partially mediated the influence of brain size on individual differences in fluid intelligence. Together, these findings illuminate the profound significance of brain lateralization as a fundamental organizational principle of the human brain, providing direct evidence that hemispheric lateralization supports human fluid intelligence. Significance StatementA novel framework is developed to estimate between-hemisphere distance in a functional representation space derived from connectivity gradients. This framework is used in a large sample to delineate functional lateralization in humans. Our findings offer direct proof that a larger functional difference between the two hemispheres is associated with better fluid intelligence, indicating that functional lateralization is the optimal organization for cognitive processing. Importantly, our findings reveal that the functional distance between the left and right hemispheres partially mediates the impact of brain size on fluid intelligence. (Comment on this)
2:19a	Interleukin-6 induces nascent protein synthesis in human DRG nociceptors via MNK-eIF4E signaling Plasticity of dorsal root ganglion (DRG) nociceptors in the peripheral nervous system requires new protein synthesis. This plasticity is believed to be responsible for the physiological changes seen in DRG nociceptors in animal models of chronic pain. Experiments in human DRG (hDRG) neurons also support this hypothesis, but a direct observation of nascent protein synthesis in response to a pain promoting substance, like interleukin-6 (IL-6), has not been measured in these neurons. To fill this gap in knowledge, we used acutely prepared human DRG explants from organ donors. These explants provide a physiologically relevant microenvironment, closer to in vivo conditions, allowing for the examination of functional alterations in DRG neurons reflective of human neuropathophysiology. Using this newly developed assay, we demonstrate upregulation of the target of the MNK1/2 kinases, phosphorylated eIF4E (p-eIF4E), and nascently synthesized proteins in a substantial subset of hDRG neurons following exposure to IL-6. To pinpoint the specific molecular mechanisms driving this IL-6- driven increase in nascent proteins, we used the specific MNK1/2 inhibitor eFT508. Treatment with eFT508 resulted in the inhibition of IL-6-induced increases in p-eIF4E and nascent proteins. Additionally, using TRPV1 as a marker for nociceptors, we found that these effects occurred in a large number of human nociceptors. Our findings provide clear evidence that IL-6 drives nascent protein synthesis in human TRPV1+ nociceptors via MNK1/2-eIF4E signaling. The work links animal findings to human nociception, creates a framework for additional hDRG signaling experiments, and substantiates the continued development of MNK inhibitors for pain. (Comment on this)
2:20a	Microgliosis driven by palmitate exposure alters energy metabolism and extracellular vesicles release that impact behavior and systemic metabolism Dietary patterns that include an excess of foods rich in saturated fat are associated with brain dysfunction. Although microgliosis has been proposed to play a key role in the development of brain dysfunction in diet-induced obesity (DIO), neuroinflammation with cytokine over-expression is often not always observed. Thus, mechanisms by which microglia contribute to brain impairment in DIO are uncertain. Using the BV2 cell model, we investigated the gliosis profile of microglia exposed to palmitate (200 {micro}mol/L), a saturated fatty acid abundant in high-fat diet and in the brain of obese individuals. We observed that microglia respond to a 24-hour palmitate exposure with increased proliferation, and with a metabolic network rearrangement that favors energy production from glycolysis rather than oxidative metabolism, despite stimulated mitochondria biogenesis. In addition, while palmitate did not induce increased cytokine expression, it modified the protein cargo of released extracellular vesicles (EVs). When administered intra-cerebroventricularly to mice, EVs from palmitate-exposed microglia in vitro led to memory impairment, depression-like behavior, and glucose intolerance, when compared to mice receiving EVs from vehicle-treated microglia. We conclude that microglia exposed to palmitate can mediate brain dysfunction through the cargo of shed EVs. (Comment on this)
3:31a	Electromagnetic Modeling within a Microscopically Realistic Brain - Implications for Brain Stimulation Across all electrical stimulation (neuromodulation) domains, conventional analysis of cell polarization involves two discrete steps: i) prediction of tissue electric field ignoring presence of cells and; ii) prediction of cell polarization from tissue electric fields. The first step assumes that electric current flow is not distorted by the dense tortuous network of cell structures. The deficiencies of this assumption have long been recognized, but - except for trivial geometries - ignored, because it presented intractable computation hurdles. We leverage recent electron microscopic images of the brain that have made it possible to reconstruct microscopic brain networks over relatively large volumes and a charge-based formulation of boundary element fast multipole method (BEM-FMM) for the first stimulations of realistic neuronal polarization by electrical stimulation that consider field distortions by a cellular network. The dataset under study is a 250x140x90 m section of the L2/L3 mouse visual cortex with 396 tightly spaced neurite cells and 34 microcapillaries. We report that brain microstructure significantly distorts the primary macroscopic electric field. Although being very local such distortions constructively accumulate along the neuronal arbor and reduce neuronal activating thresholds by 0.55-0.85-fold as compared to conventional theory. Significance statementThis study introduces a novel method for modeling perturbations of impressed electric fields within a microscopically realistic brain volume, with densely populated neuronal cells and blood microcapillaries. It addresses the limitation of present macroscopic-level electromagnetic models in brain stimulation, which predict 2.5-3 times higher activating thresholds than those observed in experiments. Despite the challenge posed by the small size of the brain sample, our model predicted a consistent average threshold reduction factor of 0.85-0.55 when compared to the macroscopic approach. The present study begins to bridge a critical gap in our understanding of neural activation and arguably creates a new standard for future research in neuronal network modeling in brain stimulation and electrophysiology. (Comment on this)
12:30p	Activity-dependent Sonic hedgehog signaling promotes astrocyte modulation of synaptic plasticity The influence of neural activity on astrocytes and their reciprocal interactions with neurons has emerged as an important modulator of synapse function. Activity regulates gene expression in astrocytes, yet the molecular mechanisms by which such activity is translated into functional changes in gene expression have remained largely unknown. The molecular signaling pathway, Sonic hedgehog (Shh), mediates neuron-astrocyte communication and regulates the organization of cortical synapses. Here, we demonstrate that sensory experience stimulates SHH signaling in cortical astrocytes. Whisker stimulation and chemogenetic activation both increase SHH activity in deep layers of the somatosensory cortex, where neuron-astrocyte SHH signaling is predominantly found. Selective loss of SHH signaling in astrocytes occludes experience-dependent structural plasticity of synapses. We further demonstrate that sensory experience promotes expression of the synapse modifying molecules, Hevin and SPARC, in a SHH-dependent manner. Taken together, these data identify SHH signaling as an activity-dependent, neuronal derived cue that stimulates astrocyte interactions with synapses and promotes synaptic plasticity. (Comment on this)
1:45p	Brain dynamics and spatiotemporal trajectories during threat processing In the past decades, functional MRI research has investigated mental states and their brain bases in largely static fashion based on evoked responses during blocked and event-related designs. Despite some progress in naturalistic designs, our understanding of threat processing remains largely limited to those obtained with standard paradigms. In the present paper, we applied Switching Linear Dynamical Systems to uncover the dynamics of threat processing during a continuous threat-of-shock paradigm. Importantly, unlike studies in systems neuroscience that frequently assume that systems are decoupled from external inputs, we characterized both endogenous and exogenous contributions to dynamics. First, we demonstrated that the SLDS model learned the regularities of the experimental paradigm, such that states and state transitions estimated from fMRI time series data from 85 ROIs reflected both the proximity of the circles and their direction (approach vs. retreat). After establishing that the model captured key properties of threat-related processing, we characterized the dynamics of the states and their transitions. The results revealed that threat processing can profitably be viewed in terms of dynamic multivariate patterns whose trajectories are a combination of intrinsic and extrinsic factors that jointly determine how the brain temporally evolves during dynamic threat. We propose that viewing threat processing through the lens of dynamical systems offers important avenues to uncover properties of the dynamics of threat that are not unveiled with standard experimental designs and analyses. (Comment on this)
4:33p	A robust brain network for sustained attention from adolescence to adulthood that predicts later substance use Substance use, including cigarettes and cannabis, is associated with poorer sustained attention in late adolescence and early adulthood. Previous studies were predominantly cross-sectional or under-powered and could not indicate if impairment in sustained attention was a consequence of substance-use or a marker of the inclination to engage in such behaviour. This study explored the relationship between sustained attention and substance use across a longitudinal span from ages 14 to 23 in over 1,000 participants. Behaviours and brain connectivity associated with diminished sustained attention at age 14 predicted subsequent increases in cannabis and cigarette smoking, establishing sustained attention as a robust biomarker for vulnerability to substance use. Individual differences in network strength relevant to sustained attention were preserved across developmental stages and sustained attention networks generalized to participants in an external dataset. In summary, brain networks of sustained attention are robust, consistent, and able to predict aspects of later substance use. (Comment on this)

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