bioRxiv Subject Collection: Neuroscience's Journal
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Wednesday, January 24th, 2024
| Time |
Event |
| 11:32a |
Predicting individual variations in mental effort-based decision-making using machine learning: Neurometabolic signature in the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex
Exploring why individuals vary in their willingness to exert effort in decision-making is fundamental for understanding human behavior. Our study focuses on the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC), a crucial brain region in motivation and decision-making, to uncover the neurobiological factors influencing these individual differences. We utilized 7T proton magnetic resonance spectroscopy (1H-MRS) to analyze metabolite concentrations in the dmPFC/dACC and anterior insula (AI) of 75 participants, aiming to predict individual variability in effort-based decision-making. Employing computational modeling, we identified key motivational parameters and, using machine learning models, pinpointed glutamate, aspartate, and lactate as crucial metabolites predicting decision-making to exert high mental effort, signifying their role as potential biomarkers for mental effort decision-making. Additionally, we examined the relationships between plasma and brain metabolite concentrations. Our findings provide novel insights into the neurometabolic underpinnings of motivated behavior, offering new perspectives in the field of cognitive neuroscience and human behavior. | | 11:32a |
MSK1 expression in the GABAergic network and its relationship with striatal growth, BDNF-mediated MeCP2 phosphorylation and schizophrenia
It is becoming evident that impairments of the neuronal circuitry during development are the basis for some human disorders including autism and schizophrenia. To understand how synaptic wiring is formed and maintained is not only a major scientific challenge, but also has an important biomedical implication. The striatal GABAergic projection neurons, also called Medium Spiny Neurons (MSNs), represent more than 95% of the neuronal population in the striatum. This inhibitory brain hub is associated to voluntary body movements, reward-associated learning, and social behaviour control. The functional loss of the striatum has been associated with several neurological disorders including Huntington disease, Rett syndrome, and schizophrenia. The aetiology of these pathologies implies not only a decrease in the glutamatergic and dopaminergic inputs that reaches into the striatum, but also alterations of the GABAergic brain circuits occurring during development. Using a new MSK1 knockout murine model, we describe that mitogen- and stress-activated protein kinase-1 (MSK1) controls MSNs arborization during mouse brain development, phosphorylation of serine 421 in methyl-CpG binding protein-2 (MeCP2) and regulation of genes involved in gamma-aminobutyric acid (GABA) and dopamine functions, factors that finally cause behaviours reminiscent of schizophrenia in patients. | | 11:32a |
A specialized inhibitory function sharpens somatosensory hand representation and enhances the production and perception of fast multifinger movements in pianists
Accurate control of fast movements of multiple body parts characterizes experts' skills, such as playing musical instruments. While performing these movements, the somatosensory system is challenged to successively and in parallel process a large amount of somatosensory information originating from different body parts in a short period. Here, we show that pianists possess a unique inhibitory function that isolates the somatosensory processing of different fingers in the somatosensory cortex. Weak electrical stimulation to the ulnar nerve successfully augmented this inhibitory function, which also improved both the perception and production of fast and complex multifinger sequential movements in pianists. In nonmusicians, neither the inhibitory effects on the somatosensory process nor the perception of multifinger movements was enhanced by this stimulation. Together, these findings provide the first evidence of the experience-dependent plasticity of inhibition of the somatosensory system, which underlies the fine control of fast and complex multifinger movements in expert pianists. | | 8:18p |
Minimizing variability in the filament middle cerebral artery occlusion model in C57BL/6 mice by surgical optimization - the PURE-MCAo Model
BACKGROUND In the intraluminal filament middle cerebral artery occlusion (fMCAo) model, there is considerable variability in infarct volumes, especially in C57BL/6 mice, which often lack the P1 segment of the posterior cerebral artery (PCA) and therefore develop not only MCA but also PCA area infarcts after fMCAo. Another factor contributing to infarct volume variability is collateral flow to the MCA area. The aim of this study was to establish an optimal surgical method to reduce the infarct volume variability in C57BL/6 mice. METHODS C57BL/6 mice were subjected to 60 min of fMCAo with cerebral blood flow monitored by laser Doppler fluxmetry. The influence of the common carotid artery (CCA) ligation, filament morphology, and the pterygopalatine artery (PPA) ligation on lesion volume and neurological severity score 24 hours after reperfusion were assessed. RESULT The use of filaments with appropriate length of coating and ligation of the PPA while maintaining perfusion of the CCA prevented the development of infarcts in the PCA area, resulted in pure MCA infarcts (68.3 plus/minus 14.5mm3, 26.1 plus/minus 3.6% of the hemisphere with Swanson's correction) and reduced the variability of infarct volumes by more than half to 13.9% of the standard deviation divided by mean. CONCLUSIONS Using improved surgical methods with suitable filaments to induce MCA occlusion in mice, we were able to produce PCA area-unaffected reproducible infarcts exclusively in the MCA area with reduced variability (PURE-MCAo). Our results may thus help to increase the reproducibility of the fMCAo model and reduce the number of animals required in preclinical stroke research. | | 11:45p |
Treatment effects in epilepsy: a mathematical framework for understanding response over time
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, both mono- and poly-therapy. However more invasive therapies such as surgery, electrical stimulation and focal drug delivery may also be considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis one of the biggest challenges for treating epilepsy. In this work, we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. We show that, for small networks, vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom. |
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