bioRxiv Subject Collection: Neuroscience's Journal
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Saturday, November 16th, 2024
| Time |
Event |
| 12:33a |
High-resolution imaging atlas reveals context-dependent role of pancreatic sympathetic innervation in diabetic mice
Gaining a better understanding of how sympathetic nerves impact pancreatic function is helpful for understanding diabetes. However, there is still uncertainty and controversy surrounding the roles of sympathetic nerves within the pancreas. To address this, we utilize high-resolution imaging and advanced three-dimensional (3D) reconstruction techniques to study the patterns of sympathetic innervation and morphology in islets of adult WT and diabetic mice. Our data shows that more than ~30% alpha/beta-cells are innervated by sympathetic nerves in both WT and diabetic mice. Also, sympathetic innervated alpha/beta-cells are reduced in DIO mice, whereas sympathetic innervated beta-cells are increased in db/db mice. Besides, in situ chemical pancreatic sympathetic denervation (cPSD) improves glucose tolerance in WT and db/db mice, but decreases in DIO mice. In situ cPSD also enhances insulin sensitivity in diabetic mice without affecting WT mice. Overall, our findings advance our comprehension of diabetes by highlighting the distinctive impact of pancreatic sympathetic innervation on glucose regulation. | | 1:45a |
A hierarchical multiscale model of forward and backward alpha-band traveling waves in the visual system
Recent studies have shown that low-frequency oscillations in the cortex are often organized as traveling waves. The dynamical properties of these waves, that span different scales, have been linked to both sensory processing and cognitive functions. In EEG recordings, alpha-band (~10Hz) traveling waves propagate predominantly in both directions of the occipital-frontal axis, with forward waves being most prominent during visual processing, while backward waves dominate at rest and during sensory suppression. While a previous study has proposed a functional model to explain their generation and propagation, a multi-scale, biologically plausible implementation is still lacking. Here, we present a multi-scale network model with mean-field dynamics that, building on known interlaminar and cortico-cortical projections, reproduces the dynamics of alpha-band traveling waves observed in EEG recordings. We show that scalp-level forward and backward waves can arise from two distinct sub-networks that are connected in infragranular layers at each area. At rest, the network generates spontaneous backward waves and switches to a forward state upon bottom-up sensory stimulation, reproducing the dynamics we observed in EEG recordings in healthy participants. We then expand our model to a cortico-thalamic network with a parallel feedforward pathway through the pulvinar. Our results show that this pathway biases the cortical dynamics to the forward state and that high pulvinar engagement leads to spontaneous forward waves without external input. This result is in line with previous studies suggesting a key role for the pulvinar in directing information flow in the cortex, and provide a computational basis to investigate the role of the pulvinar in cortical dynamics. In summary, our model provides a biologically plausible architecture for modeling the dynamics of macroscale traveling waves. Importantly, our study bridges the gap between distinct scales by connecting laminar mean-field activity to spatial patterns at the scalp level, providing a biologically grounded and comprehensive view of the generation and propagation of alpha-band traveling waves. | | 1:50p |
Morphological brain alterations and morphological brain network disorganizations in heroin and methamphetamine abstinent patients
Heroin and methamphetamine are the two common types of drugs abused, which poses significant health risks. However, the neural mechanisms underlying the effects of drug addiction on human brain are unclear. In this study, we collected T1-weighted magnetic resonance imaging data from 26 heroin abstinent (HA) patients, 24 methamphetamine abstinent (MA) patients and 32 healthy controls. Four surface-based morphological features including cortical thickness (CT), fractal dimension (FD), gyrification index (GI), and sulcal depth (SD) were calculated, and further used to construct the morphological brain networks. We observed the common CT reductions of the right TE 1.0 and TE 1.2 and SD reductions of the right intermediate lateral area 20 for HA and MA patients, HA-specific CT reductions in the left area 2, and the MA-specific GI reductions in the left medial area 6 and right dorsomedial parietooccipital sulcus. For the morphological brain networks, HA patients exhibited the global disorganizations (higher shortest path length) in CT-based networks, whereas MA patients showed the disrupted nodal efficiency of the left medial area 38 in CT-based networks, the right caudal area 7 in GI-based networks, and the right inferior occipital gyrus in SD-based networks. Furthermore, the altered SD of HA patients and disrupted nodal efficiency of MA patients were associated with drug abuse-related clinical variables. Our findings suggest the morphological index-dependent effects of drug addiction on human brain morphology, and indicate the differential neural mechanism underlying heroin and methamphetamine abuses which attack the global and local information transfer of morphological brain networks, respectively. | | 1:50p |
Functional Connectivity Alterations in Cocaine Use Disorder: Insights from the Triple Network Model and the Addictions Neuroclinical Assessment Framework
Cocaine use disorder (CUD) disrupts functional connectivity within key brain networks, specifically the default mode network (DMN), salience network (SN), and central executive network (CEN). While the triple network model has been proposed to explain various psychiatric disorders, its applicability to CUD requires further exploration. In the present study, we built machine learning classifiers based on different combinations of DMN/SN/CEN to distinguish cocaine-use disorder (CUD) subjects from healthy control (HC) subjects. Among them, the combination of the SN and the CEN results in a remarkably high accuracy of 73.4% (sensitivity/specificity: 69.6%/78.6%, AUC: 0.78), outperforming the model based on the full triple network. This supports the hypothesis that during the binge/intoxication stage of addiction, the SN and the CEN play a more critical role than the DMN, consistent with the Addictions Neuroclinical Assessment (ANA) framework. Functional connectivity analysis revealed decreased connectivity within the DMN and the SN and increased connectivity within the CEN in CUD patients, suggesting that alterations in these networks could serve as biomarkers for addiction severity. | | 3:47p |
Attention-dependent attribute comparisons underlie multi-attribute decision-making in orbitofrontal cortex
Economic decisions often require weighing multiple dimensions, or attributes. The orbitofrontal cortex FC) is thought to be important for computing the integrated value of an option from its attributes and comparing lues to make a choice. Although OFC neurons are known to encode integrated values, evidence for value mparison has been limited. Here, we used a multi-attribute choice task for monkeys to investigate how OFC eurons integrate and compare multi-attribute options. Attributes were represented separately and eye tracking as used to measure attention. We found that OFC neurons encode the value of attended attributes, dependent of other attributes in the same option. Encoding was negatively weighted by the value of the same tribute in the other option, consistent with a comparison between the two like attributes. These results indicate at OFC computes comparisons among attributes rather than integrated values, and does so dynamically, ifting with the focus of attention. | | 3:47p |
Sortilin C-terminal fragment deposition depicts tangle-related nonamyloid neuritic plaque growth in Alzheimers disease
Sortilin C-terminal fragments (sorfra) can co-deposit in {beta}-amyloid (A{beta}) plaques in human brain. However, sorfra plaques develop in the cerebrum with a spatiotemporal trajectory as of tauopathy. Here we examined sorfra pathogenesis relative to neuritic plaque evolution in the human brains with amyloid and tau pathologies converged in the neocortex and hippocampus. Sorfra plaques occurred in correlation with pTau/tangle, but not A{beta}, pathologies across cerebral regions, neighboring cortical/hippocampal areas, and along the sulcal valley to gyral hilltop transition. Sorfra plaques and neuritic plaques were matchable in location, shape and size between consecutive sections, and were colocalized in double-labeling preparations. Microscopical study and tissue clearance three-dimensional imaging revealed sorfra/A{beta} colocalized as well as independent plaques. Among the former, sorfra labeling correlated negatively to A{beta}/amyloid labeling and {beta}-secretase-1 labeling in dystrophic neurites. Sorfra plaques were depleted of microtubule-associated protein 2 (MAP2) labeled neuronal somata and dendrites, whereas normal looking MAP2/sortilin co-labeled profiles occurred nearby. Sorfra deposits were seen in astrocytes but not microglia around the plaques. Taken together, sorfra plaques are anatomically matchable to silver stained neuritic plaques. They develop with tangle-related somatodendritic degeneration, presenting as nonamyloid growth of the A{beta} plaques and formation of A{beta}-independent neuritic plaques during Alzheimers disease pathogenesis. | | 4:20p |
WMH-DualTasker: A Dual-Task Deep Learning Model with Self-supervised Consistency for Automated Segmentation and Visual Rating of White matter Hyperintensities - a Multicentre study
BackgroundWhite matter hyperintensities (WMH) are neuroimaging markers linked to an elevated risk of cognitive decline. WMH severity is typically assessed via visual rating scales and through volumetric segmentation. While visual rating scales are commonly used in clinical practice, they offer limited descriptive power. In contrast, supervised volumetric segmentation requires manually annotated masks, which is labor-intensive and challenging to scale for large studies. Therefore, our goal was to develop an automated deep learning model that can provide accurate and holistic quantification of WMH severity with minimal supervision. MethodsWe developed WMH-DualTasker, a deep learning model that simultaneously performs voxel-wise segmentation and visual rating score prediction. The model leverages self-supervised, transformation-invariant consistency constraints, using WMH visual ratings from clinical settings as the sole supervisory signal. Additionally, we assessed its clinical utility by applying it to identify individuals with mild cognitive impairment (MCI) and to predict dementia conversion. FindingsThe volumetric quantification performance of WMH-DualTasker was either superior to or on par with existing supervised methods, as demonstrated on the MICCAI-WMH dataset (N=60, Dice=0.602) and the SINGER dataset (N=64, Dice=0.608). Furthermore, the model exhibited strong agreement with clinical visual rating scales on an external dataset (SINGER, MAE=1.880, K=0.77). Importantly, WMH severity metrics derived from WMH-DualTasker improved predictive performance beyond conventional clinical features for MCI classification (AUC=0.718, p<0.001), MCI conversion prediction (AUC=0.652, p<0.001) using the ADNI dataset. InterpretationsWMH-DualTasker substantially reduces the reliance on labor-intensive manual annotations, facilitating more efficient and scalable quantification of WMH severity in large-scale population studies. This innovative approach has the potential to advance preventive and precision medicine by enhancing the assessment and management of vascular cognitive impairment associated with WMH. Code and model weights are publicly available at https://github.com/hzlab/WMH-DualTasker. | | 4:20p |
Type I interferon signaling enhances kainic acid-induced seizure severity
Epilepsy is a chronic neurological disorder characterized by recurrent seizures, yet the role and mechanisms of type I interferon (IFN) signaling in seizure conditions remain elusive. In this study, we demonstrate that type I IFN signaling exacerbates seizure phenotypes in a kainic acid-induced seizure mouse model. We found that the absence of type I IFN signaling in Ifnar1-/- mice led to decreased neuronal excitability and microglial activation in response to kainic acid stimulation. Conversely, intracerebroventricular injection of IFN-{beta} heightened the severity of kainic acid-induced seizures. In vitro calcium imaging revealed that IFN-{beta} treatment amplified both basal and kainic acid-induced neuronal excitability, though no significant difference was observed in basal neuronal excitability between wild-type and Ifnar1-/- neurons. Furthermore, Ifnar1-/- mice exhibited reduced mTOR activation in the brain following kainic acid administration. Consistent with this finding, IFN-{beta} treatment induced mTOR activation, as indicated by S6 phosphorylation in in vitro mixed glial cultures. Taken together, these results demonstrate the critical role of type I IFN signaling in seizure pathogenesis and suggest that targeting type I IFNs could be a promising therapeutic strategy for reducing seizure severity and mitigating epilepsy. | | 4:20p |
Heat Acclimation in Mice Requires Preoptic BDNF Neurons and Postsynaptic Potentiation
Heat acclimation (HA) is a key adaptive response in mammals to repeated heat exposure, essential for fitness and survival1-3. HA improves cardiovascular function, thermal comfort, and exercise capacity4, 5. However, the lack of a genetically tractable model has hindered understanding of the molecular and neural mechanisms underlying HA. Here, we show that 10 days of daily 38{degrees}C exposure lowers core body temperature (Tcore) and reduces anxiety during subsequent heat exposures in mice. HA increases brain-derived neurotrophic factor (BDNF) expression in the medial preoptic area (MPO). BDNF-expressing MPO neurons (MPOBDNF) show increased intrinsic heat sensitivity after HA. These neurons orchestrate downstream targets in the dorsomedial hypothalamus (DMH) and rostral raphe pallidus (rRPa) to mediate HA effects. BDNF, acting through its receptor tropomyosin-related kinase B (TrkB) in the DMH, facilitates the anxiolytic effect of HA by enhancing excitatory synaptic connections between MPOBDNF and DMH neurons. This study provides new insights into HA mechanisms, setting the stage for future research on heat stress reduction and exercise optimization. | | 4:20p |
Chemogenetic Breakdown of the Dentate Gate Causes Seizures and Spatial Memory Deficits
The dentate gyrus has often been posited to act as a gate that dampens highly active afferent input into the hippocampus. Effective gating is thought to prevent seizure initiation and propagation in the hippocampus and support learning and memory processes. Pathological changes to DG circuitry that occur in temporal lobe epilepsy (TLE) can increase DG excitability and impair its gating ability which can contribute to seizures and cognitive deficits. There is evidence that TLE pathologies and seizures may independently contribute to learning and memory deficits in TLE through distinct mechanisms. These two factors are difficult to untangle since TLE pathologies can drive seizures, and seizures can worsen TLE pathologies. Here we assessed whether chemogenetically increasing dentate granule cell (DGC) excitability was enough to break down the dentate gate in the absence of TLE pathologies. We found that increasing excitability specifically in DGCs caused seizures in non-epileptic mice. Importantly, due to the modulatory nature of DREADD effects, seizures were driven by intrinsic circuit activity rather than direct activation of DGCs. These seizures resulted in a spatial memory deficit when induced after training in the spatial object recognition task and showed stereotypical patterns of activity in miniscope calcium recordings. Our results provide direct support for the dentate gate hypothesis since seizures could be induced in non-epileptic animals by artificially degrading the dentate gate with chemogenetics in the absence of epilepsy pathologies. | | 5:33p |
Impact of adolescent high-fat diet and psychosocial stress on neuroendocrine stress responses and binge eating behavior in adult male Lewis rats
Childhood obesity is a multifactorial disease affecting more than 160 million adolescents worldwide. Adolescent exposure to obesogenic environments, characterized by access to high-fat diets and stress, precipitates maladaptive eating habits in adulthood such as binge eating. Evidence suggests a strong association between Western-like high-saturated-fat (WD) food consumption and dysregulated hormone fluctuations. However, few studies have explored the long-term impact of adolescent WD and psychosocial stress on brain and behavior. This longitudinal study aimed to investigate the impact of adolescent exposure to an obesogenic diet on stress resiliency and increased susceptibility for binge-like eating behaviors. Adolescent male Lewis rats were given WD (41% fat; n=40) or control diet (CD, 16% fat; n=38) for 4 weeks before undergoing a stress paradigm of predator exposure and social instability (CDE, WDE, CDU, WDU; n=16/group). Subjects were provided intermittent WD access (24 h/week) to evaluate binge eating-like behavior in adulthood. Fecal corticosterone and testosterone were measured at four timepoints throughout adolescence and adulthood. WD rats exhibited increased body weight (p = 0.0217) and elevated testosterone in mid-adolescence (p=0.0312) and blunted stress-induced corticosterone response in mid-late adolescence (CDE:WDE, p=0.028). Adolescent hormone levels were negatively correlated with bingeing and explained the variability between adult rats expressing hyperphagic and hypophagic behaviors. These results demonstrate that exposure to WD in adolescence disrupts hormone fluctuations and stress responsivity, with effects persisting into adulthood. This underscores the importance of addressing obesogenic environments early to mitigate their lasting impact on hormone regulation and stress responsiveness. | | 7:32p |
PrP turnover in vivo and the time to effect of prion disease therapeutics
PrP lowering is effective against prion disease in animal models and is being tested clinically. Therapies in the current pipeline lower PrP production, leaving pre-existing PrP to be cleared according to its own half-life. We hypothesized that PrPs half-life may be a rate-limiting factor for the time to effect of PrP-lowering drugs, and one reason why late treatment of prion-infected mice is not as effective as early treatment. Using isotopically labeled chow with targeted mass spectrometry, as well as antisense oligonucleotide treatment followed by timed PrP measurement, we estimate a half-life of 5-6 days for PrP in the brain. PrP turnover is not affected by over- or under-expression. Mouse PrP and human PrP have similar turnover rates measured in wild-type or humanized knock-in mice. CSF PrP appears to mirror brain PrP in real time in rats. PrP is more readily quantifiable in colon than in other peripheral organs, and appears to have a shorter half-life in colon than in brain. Our data may inform the design of both preclinical and clinical studies of PrP-lowering drugs. |
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