An energy budget for signaling in the grey matter of the brain

The role of mitochondrial uncoupling in the regulation of mitostasis after traumatic brain injury

Mitostasis, the maintenance of healthy mitochondria, plays a critical role in brain health. The brain's high energy demands and reliance on mitochondria for energy production make mitostasis vital for neuronal function. Traumatic brain injury (TBI) disrupts mitochondrial homeostasis, leading to secondary cellular damage, neuronal degeneration, and cognitive deficits. Mild mitochondrial uncoupling, which dissociates ATP production from oxygen consumption, offers a promising avenue for TBI treatment. Accumulating evidence, from endogenous and exogenous mitochondrial uncoupling, suggests that mitostasis is closely regulating by mitochondrial uncoupling and cellular injury environments may be more sensitive to uncoupling. Mitochondrial uncoupling can mitigate calcium overload, reduce oxidative stress, and induce mitochondrial proteostasis and mitophagy, a process that eliminates damaged mitochondria. The interplay between mitochondrial uncoupling and mitostasis is ripe for further investigation in the context of TBI. These multi-faceted mechanisms of action for mitochondrial uncoupling hold promise for TBI therapy, with the potential to restore mitochondrial health, improve neurological outcomes, and prevent long-term TBI-related pathology.

Drug-drug interactions between propofol and ART drugs: Inhibiting neuronal activity by affecting glucose metabolism

BACKGROUND

The use of two or more drugs carries the potential risk of drug-drug interactions (DDIs), which may result in adverse reactions. Some human immunodeficiency virus (HIV)-infected patients who receive antiretroviral therapy (ART) may require general anesthesia with propofol (PRL) before undergoing surgical treatment. Both PRL and ART drugs may lead to neuronal dysfunction, which can be accompanied by energy metabolism disorders. Neurons take in glucose mainly through glucose transporter 3 (Glut3) which is specifically expressed on the cell membranes of neurons. However, to date, no study has examined whether the DDIs of PRL and ART drugs interfere with glucose metabolism and Glut3 expression in neurons.

METHODS

An in vitro model was constructed using the primary cultures of neurons. PRL and ART drugs (EFV, AZT, and 3TC), were added at different concentrations (low, medium, and high). The neurons were exposed to the drugs for 1, 4, 8, and 12 h. The optimal drug concentration and exposure time were selected. The cellular survival rate, glucose concentration, electrophysiology, and the expression of Glut3 were detected.

RESULTS

There were no significant changes in the cellular survival rates of the neurons that were exposed to both PRL and ART drugs at low concentrations for 1 h. However, the survival rates of the neurons decreased significantly as the drug concentrations and durations increased. The glucose concentration of the neurons that were exposed to both PRL and the ART drugs was significantly decreased. The glucose concentration of the neurons was not affected by any individual drug. The amplitude of the action potential and the expression of Glut3 were decreased in the neurons that were exposed to both PRL and ART drugs.

CONCLUSIONS

The main adverse reactions induced by the DDIs between PRL and the ART drugs were decreased glucose metabolism and neuronal damage, which were caused by inhibiting the expression of Glut3. More importantly, we found that decreases in glucose metabolism predated neuronal damage.

Characterization of the three-dimensional synaptic and mitochondrial nanoarchitecture within glutamatergic synaptic complexes in postmortem human brain via focused ion beam-scanning electron microscopy

Glutamatergic synapses are the primary site of excitatory synaptic signaling and neural communication in the cerebral cortex. Electron microscopy (EM) studies in non-human model organisms have demonstrated that glutamate synaptic activity and functioning are directly reflected in quantifiable ultrastructural features. Thus, quantitative EM analysis of glutamate synapses in ex vivo preserved human brain tissue has the potential to provide novel insight into in vivo synaptic functioning. However, factors associated with the acquisition and preservation of human brain tissue have resulted in persistent concerns regarding the potential confounding effects of antemortem and postmortem biological processes on synaptic and sub-synaptic ultrastructural features. Thus, we sought to determine how well glutamate synaptic relationships and nanoarchitecture are preserved in postmortem human dorsolateral prefrontal cortex (DLPFC), a region that substantially differs in size and architecture from model systems. Focused ion beam-scanning electron microscopy (FIB-SEM), a powerful volume EM (VEM) approach, was employed to generate high-fidelity, fine-resolution, three-dimensional (3D) micrographic datasets appropriate for quantitative analyses. Using postmortem human DLPFC with a 6-hour postmortem interval, we optimized a tissue preservation and staining workflow that generated samples of excellent ultrastructural preservation and the high-contrast staining intensity required for FIB-SEM imaging. Quantitative analysis of sub-cellular, sub-synaptic and organelle components within glutamate axo-spinous synapses revealed that ultrastructural features of synaptic function and activity were well-preserved within and across individual synapses in postmortem human brain tissue. The synaptic, sub-synaptic and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, dense reconstruction of neuropil revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of neuronal processes with heightened synaptic communication, integration and plasticity. Altogether, our findings provide a critical proof-of-concept that ex vivo VEM analysis provides a valuable and informative means to infer in vivo functioning of human brain.

Thermodynamic limitations on brain oxygen metabolism: physiological implications

Recent thermodynamic modelling indicates that maintaining the brain tissue ratio of O2 to CO2 (abbreviated tissue O2 /CO2 ) is critical for preserving the entropy increase available from oxidative metabolism of glucose, with a fall of that available entropy leading to a reduction of the phosphorylation potential and impairment of brain energy metabolism. This provides a novel perspective for understanding physiological responses under different conditions in terms of preserving tissue O2 /CO2 . To enable estimation of tissue O2 /CO2 in the human brain, a detailed mathematical model of O2 and CO2 transport was developed, and applied to reported physiological responses to different challenges, asking: how well is tissue O2 /CO2 preserved? Reported experimental results for increased neural activity, hypercapnia and hypoxia due to high altitude are consistent with preserving tissue O2 /CO2 . The results highlight two physiological mechanisms that control tissue O2 /CO2 : cerebral blood flow, which modulates tissue O2 ; and ventilation rate, which modulates tissue CO2 . The hypoxia modelling focused on humans at high altitude, including acclimatized lowlanders and Tibetan and Andean adapted populations, with a primary finding that decreasing CO2 by increasing ventilation rate is more effective for preserving tissue O2 /CO2 than increasing blood haemoglobin content to maintain O2 delivery to tissue. This work focused on the function served by particular physiological responses, and the underlying mechanisms require further investigation. The modelling provides a new framework and perspective for understanding how blood flow and other physiological factors support energy metabolism in the brain under a wide range of conditions. KEY POINTS: Thermodynamic modelling indicates that preserving the O2 /CO2 ratio in brain tissue is critical for preserving the entropy change available from oxidative metabolism of glucose and the phosphorylation potential underlying energy metabolism. A detailed model of O2 and CO2 transport was developed to allow estimation of the tissue O2 /CO2 ratio in the human brain in different physiological states. Reported experimental results during hypoxia, hypercapnia and increased oxygen metabolic rate in response to increased neural activity are consistent with maintaining brain tissue O2 /CO2 ratio. The hypoxia modelling of high-altitude acclimatization and adaptation in humans demonstrates the critical role of reducing CO2 with increased ventilation for preserving tissue O2 /CO2 . Preservation of tissue O2 /CO2 provides a novel perspective for understanding the function of observed physiological responses under different conditions in terms of preserving brain energy metabolism, although the mechanisms underlying these functions are not well understood.

The role of glutamate and glutamine metabolism and related transporters in nerve cells

BACKGROUND

Glutamate and glutamine are the most abundant amino acids in the blood and play a crucial role in cell survival in the nervous system. Various transporters found in cell and mitochondrial membranes, such as the solute carriers (SLCs) superfamily, are responsible for maintaining the balance of glutamate and glutamine in the synaptic cleft and within cells. This balance affects the metabolism of glutamate and glutamine as non-essential amino acids.

AIMS

This review aims to provide an overview of the transporters and enzymes associated with glutamate and glutamine in neuronal cells.

DISCUSSION

We delve into the function of glutamate and glutamine in the nervous system by discussing the transporters involved in the glutamate-glutamine cycle and the key enzymes responsible for their mutual conversion. Additionally, we highlight the role of glutamate and glutamine as carbon and nitrogen donors, as well as their significance as precursors for the synthesis of reduced glutathione (GSH).

CONCLUSION

Glutamate and glutamine play a crucial role in the brain due to their special effects. It is essential to focus on understanding glutamate and glutamine metabolism to comprehend the physiological behavior of nerve cells and to treat nervous system disorders and cancer.

Shared input and recurrency in neural networks for metabolically efficient information transmission

Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.

Responses and correlation among ER stress, Ca2+ homeostasis, and fatty acid metabolism in Penaeus vannamei under ammonia stress

The role of endoplasmic reticulum (ER) stress, Ca2+ homeostasis, and fatty acid metabolism in the environmental adaptation of aquatic animals is significant, but further confirmation of the relationship between these factors is needed. This study aimed to investigate the responses and correlations among ER stress, Ca2+ homeostasis, and fatty acid metabolism in Penaeus vannamei under ammonia stress. A total of 640 P. vannamei weighing 3.0 ± 0.4 g were selected and exposed to different total ammonia concentrations (0 mg/L for the control group and 3.80, 7.60, and 11.40 mg/L for the stress groups). The experiment involved a 96 h ammonia stress period to assess indicators related to ER stress, Ca2+ homeostasis, and fatty acid metabolism. The experimental results revealed that after 12 h, exposure to ammonia induced the ER stress response in the hepatopancreas of the shrimp. The groups exposed to concentrations of 3.8 mg/L and 7.6 mg/L exhibited an increase in ER Ca2+ efflux, a decrease in influx, an elevation in mitochondrial Ca2+ influx, an enhanced energy demand within the organism, and substantial consumption of triglycerides. The 11.3 mg/L group exhibited a significant enhancement in fatty acid metabolism. At 24 h, the ER stress response induced by ammonia in the shrimp exhibited a gradual recovery. In the 7.6 mg/L and 11.3 mg/L groups, the ER Ca2+ influx and efflux exhibited significant enhancements, while the mitochondrial Ca2+ influx decreased and the organism's energy demand increased. Moreover, there was a substantial enhancement in fatty acid metabolism. At 48 h, the ER stress response disappeared in each stress group, ER Ca2+ efflux was reduced, triglycerides were consumed, and the body's energy homeostasis was basically restored. At 96 h, a stress response reoccurred in the ER in each stress group, resulting in increased influx of Ca2+ into the ER, augmented energy demand within the organism, and notable enhancement in fatty acid metabolism. Pearson correlation analysis revealed a significant positive correlation between the NH3-N content in the hepatopancreas and the expression of ER stress-related genes, as well as between ER Ca2+ influx/efflux and energy homeostasis/fatty acid metabolism. The findings indicate that the stress induced by ammonia triggers an ER stress response in P. vannamei, resulting in ER Ca2+ efflux and mitochondrial Ca2+ influx, which, in turn, enhances fatty acid metabolism to generate additional energy for adaptation in stressful environments. This study contributes to a deeper understanding of the environmental adaptability of P. vannamei in the context of Ca2+ homeostasis.

Transient anticonvulsant effects of time-restricted feeding in the 6-Hz mouse model

INTRODUCTION

Intermittent fasting enhances neural bioenergetics, is neuroprotective, and elicits antioxidant effects in various animal models. There are conflicting findings on seizure protection, where intermittent fasting regimens often cause severe weight loss resembling starvation which is unsustainable long-term. Therefore, we tested whether a less intensive intermittent fasting regimen such as time-restricted feeding (TRF) may confer seizure protection.

METHODS

Male CD1 mice were assigned to either ad libitum-fed control, continuous 8 h TRF, or 8 h TRF with weekend ad libitum food access (2:5 TRF) for one month. Body weight, food intake, and blood glucose levels were measured. Seizure thresholds were determined at various time points using 6-Hz and maximal electroshock seizure threshold (MEST) tests. Protein levels and mRNA expression of genes, enzyme activity related to glucose metabolism, as well as mitochondrial dynamics were assessed in the cortex and hippocampus. Markers of antioxidant defence were evaluated in the plasma, cortex, and liver.

RESULTS

Body weight gain was similar in the ad libitum-fed and TRF mouse groups. In both TRF regimens, blood glucose levels did not change between the fed and fasted state and were higher during fasting than in the ad libitum-fed groups. Mice in the TRF group had increased seizure thresholds in the 6-Hz test on day 15 and on day 19 in a second cohort of 2:5 TRF mice, but similar seizure thresholds at other time points compared to ad libitum-fed mice. Continuous TRF did not alter MEST seizure thresholds on day 28. Mice in the TRF group showed increased maximal activity of pyruvate dehydrogenase in the cortex, which was accompanied by increased protein levels of mitochondrial pyruvate carrier 1 in the cortex and hippocampus. There were no other major changes in protein or mRNA levels associated with energy metabolism and mitochondrial dynamics in the brain, nor markers of antioxidant defence in the brain, liver, or plasma.

CONCLUSIONS

Both continuous and 2:5 TRF regimens transiently increased seizure thresholds in the 6-Hz model at around 2 weeks, which coincided with stability of blood glucose levels during the fed and fasted periods. Our findings suggest that the lack of prolonged anticonvulsant effects in the acute electrical seizure models employed may be attributed to only modest metabolic and antioxidant adaptations found in the brain and liver. Our findings underscore the potential therapeutic value of TRF in managing seizure-related conditions.

The value of FDG combined with PiB PET in the diagnosis of patients with cognitive impairment in a memory clinic

AIMS

To analyze the value of 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with amyloid PET in cognitive impairment diagnosis.

METHODS

A total of 187 patients with dementia or mild cognitive impairment (MCI) who underwent 11 C-Pittsburgh compound B (PiB) and FDG PET scans in a memory clinic were included in the final analysis.

RESULTS

Amyloid-positive and amyloid-negative dementia patient groups showed a significant difference in the proportion of individuals presenting temporoparietal cortex (p < 0.001) and posterior cingulate/precuneus cortex (p < 0.001) hypometabolism. The sensitivity and specificity of this hypometabolic pattern for identifying amyloid pathology were 72.61% and 77.97%, respectively, in patients clinically diagnosed with AD and 60.87% and 76.19%, respectively, in patients with MCI. The initial diagnosis was changed in 32.17% of patients with dementia after considering both PiB and FDG results. There was a significant difference in both the proportion of patients showing the hypometabolic pattern and PiB positivity between dementia conversion patients and patients with a stable diagnosis of MCI (p < 0.05).

CONCLUSION

Temporoparietal and posterior cingulate/precuneus cortex hypometabolism on FDG PET suggested amyloid pathology in patients with cognitive impairment and is helpful in diagnostic decision-making and predicting AD dementia conversion from MCI, particularly when combined with amyloid PET.

Evaluating the influence of altered cerebral hemodynamics on cognitive performance in asymptomatic carotid artery stenosis: A systematic review

OBJECTIVE

Substantial controversy exists regarding asymptomatic carotid stenosis (ACS) and its potential role in the pathophysiology of cognitive impairment. If proven, this hypothesis may suggest an additional definition for symptomatic carotid disease that would alter current management. This study aimed to synthesize the literature evaluating the relationship between impaired cerebral hemodynamics and cognition in patients with ACS.

METHODS

A literature search was performed using MEDLINE, Embase, and EBM Reviews through May 2022. We included prospective case-control studies that used validated, objective measure(s) of either global cognition or one or more domains of cognitive function and assessed cerebrovascular reserve (CVR).

RESULTS

Five studies were included, comprising a total of 782 patients with moderate (50%-69%) to severe (70%-99%) ACS. Patients with ACS and impaired ipsilateral CVR demonstrated significant cognitive impairment compared with controls. Patients with unilateral or bilateral ACS and normal CVR had cognitive scores similar to controls. Those with bilateral CVR impairment demonstrated the lowest cognitive scores.

CONCLUSIONS

This review lends support to the claim that cognitive impairment, likely the result of impaired cerebral hemodynamics, is an under-recognized morbidity in patients with ACS. CVR may serve as an additional tool to determine whether patients are in fact symptomatic from their carotid stenosis and warrant consideration for intervention.

The Role of Glutamine Homeostasis in Emotional and Cognitive Functions

Glutamine (Gln), a non-essential amino acid, is synthesized de novo by glutamine synthetase (GS) in various organs. In the brain, GS is exclusively expressed in astrocytes under normal physiological conditions, producing Gln that takes part in glutamatergic neurotransmission through the glutamate (Glu)-Gln cycle. Because the Glu-Gln cycle and glutamatergic neurotransmission play a pivotal role in normal brain activity, maintaining Gln homeostasis in the brain is crucial. Recent findings indicated that a neuronal Gln deficiency in the medial prefrontal cortex in rodents led to depressive behaviors and mild cognitive impairment along with lower glutamatergic neurotransmission. In addition, exogenous Gln supplementation has been tested for its ability to overcome neuronal Gln deficiency and reverse abnormal behaviors induced by chronic immobilization stress (CIS). Although evidence is accumulating as to how Gln supplementation contributes to normalizing glutamatergic neurotransmission and the Glu-Gln cycle, there are few reviews on this. In this review, we summarize recent evidence demonstrating that Gln supplementation ameliorates CIS-induced deleterious changes, including an imbalance of the Glu-Gln cycle, suggesting that Gln homeostasis is important for emotional and cognitive functions. This is the first review of detailed mechanistic studies on the effects of Gln supplementation on emotional and cognitive functions.

Ga2O3 Bipolar Heterojunction-Based Optoelectronic Synapse Array with Visual Attention

The human brain efficiently processes only a fraction of visual information, a phenomenon termed attentional control, resulting in energy savings and heightened adaptability. Translating this mechanism into artificial visual neurons holds promise for constructing energy-efficient, bioinspired visual systems. Here, we propose a self-rectifying artificial visual neuron (SEVN) based on a NiO/Ga2O3 bipolar heterojunction with attentional control on patterns with a target color. The device exhibits short-term potentiation (STP) with quantum point contact (QPC) traits at low bias and transitions to long-term potentiation (LTP) at high bias, particularly facilitated by electron capture in deep defects upon ultraviolet (UV) exposure. With the utilization of two wavelengths of light upon the target and interference part of CAPTCHA to simulate top-down attentional control, the recognition accuracy is enhanced from 74 to 84%. These findings have the potential to augment the visual capability of neuromorphic systems with implications for diverse applications, including cybersecurity, healthcare, and machine vision.

Cooperativity, Information Gain, and Energy Cost During Early LTP in Dendritic Spines

We investigate a mutual relationship between information and energy during the early phase of LTP induction and maintenance in a large-scale system of mutually coupled dendritic spines, with discrete internal states and probabilistic dynamics, within the framework of nonequilibrium stochastic thermodynamics. In order to analyze this computationally intractable stochastic multidimensional system, we introduce a pair approximation, which allows us to reduce the spine dynamics into a lower-dimensional manageable system of closed equations. We found that the rates of information gain and energy attain their maximal values during an initial period of LTP (i.e., during stimulation), and after that, they recover to their baseline low values, as opposed to a memory trace that lasts much longer. This suggests that the learning phase is much more energy demanding than the memory phase. We show that positive correlations between neighboring spines increase both a duration of memory trace and energy cost during LTP, but the memory time per invested energy increases dramatically for very strong, positive synaptic cooperativity, suggesting a beneficial role of synaptic clustering on memory duration. In contrast, information gain after LTP is the largest for negative correlations, and energy efficiency of that information generally declines with increasing synaptic cooperativity. We also find that dendritic spines can use sparse representations for encoding long-term information, as both energetic and structural efficiencies of retained information and its lifetime exhibit maxima for low fractions of stimulated synapses during LTP. Moreover, we find that such efficiencies drop significantly with increasing the number of spines. In general, our stochastic thermodynamics approach provides a unifying framework for studying, from first principles, information encoding, and its energy cost during learning and memory in stochastic systems of interacting synapses.

Hemispheric lateralization of language processing: insights from network-based symptom mapping and patient subgroups

The hemispheric laterality of language processing has become a hot topic in modern neuroscience. Although most previous studies have reported left-lateralized language processing, other studies found it to be bilateral. A previous neurocomputational model has proposed a unified framework to explain that the above discrepancy might be from healthy and patient individuals. This model posits an initial symmetry but imbalanced capacity in language processing for healthy individuals, with this imbalance contributing to language recovery disparities following different hemispheric injuries. The present study investigated this model by analyzing the lateralization patterns of language subnetworks across multiple attributes with a group of 99 patients (compared to nonlanguage processing) and examining the lateralization patterns of language subnetworks in subgroups with damage to different hemispheres. Subnetworks were identified using a whole-brain network-based lesion-symptom mapping method, and the lateralization index was quantitatively measured. We found that all the subnetworks in language processing were left-lateralized, while subnetworks in nonlanguage processing had different lateralization patterns. Moreover, diverse hemisphere-injury subgroups exhibited distinct language recovery effects. These findings provide robust support for the proposed neurocomputational model of language processing.

Cortical activity and spatiotemporal parameters during gait termination and walking: A preliminary study

Gait termination requires an interaction between the biomechanical and neuromuscular systems to arrest forward momentum. Currently, the biomechanical characteristics of gait termination have been demonstrated; however, the neural mechanism of gait termination remains unclear. This study aimed to investigate cortical activity during gait termination using functional near-infrared spectroscopy (fNIRS). Thirteen healthy younger adults (mean age:24.0 ± 1.7) participated in this study. All participants performed three experimental sessions: planned gait termination (PGT), unplanned gait termination (UGT), and walking. Each experimental session comprised a block paradigm design (three cycles; 20 s resting, 45 s task). Cortical activity in the dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), and primary motor cortex (M1) and spatiotemporal parameters were measured. We compared the cortical activities and spatiotemporal parameters among PGT, UGT, and walking sessions. In addition, we performed Pearson correlations between hemodynamic responses and spatiotemporal parameters. The PGT was activated in the right DLPFC, whereas the UGT and walking were activated in the left SMA (p < 0.05). Comparing cortical activation between sessions, both the PGT and UGT showed significantly higher activation in the right DLPFC than during walking (p < 0.05). There were no significant differences in cortical activity between PGT and UGT (p > 0.05). In addition, the gait termination time revealed moderate positive correlation with hemodynamic responses in the right DLPFC (p < 0.05). Our results indicate that the right DLPFC is associated with gait termination, regardless of gait termination type. Our findings provide the potential implication that the hemodynamic response in the right DLPFC would be a biomarker to evaluate the ability of gait termination.

Mitochondrial dysfunction in neurodegenerative disorders

Recent advances in understanding the role of mitochondrial dysfunction in neurodegenerative diseases have expanded the opportunities for neurotherapeutics targeting mitochondria to alleviate symptoms and slow disease progression. In this review, we offer a historical account of advances in mitochondrial biology and neurodegenerative disease. Additionally, we summarize current knowledge of the normal physiology of mitochondria and the pathogenesis of mitochondrial dysfunction, the role of mitochondrial dysfunction in neurodegenerative disease, current therapeutics and recent therapeutic advances, as well as future directions for neurotherapeutics targeting mitochondrial function. A focus is placed on reactive oxygen species and their role in the disruption of telomeres and their effects on the epigenome. The effects of mitochondrial dysfunction in the etiology and progression of Alzheimer's disease, amyotrophic lateral sclerosis, Parkinson's disease, and Huntington's disease are discussed in depth. Current clinical trials for mitochondria-targeting neurotherapeutics are discussed.

Sugar-free synapses run on mitochondrial Sirtuin 3

Metabolic plasticity of neurons ensures their activity continues when glucose is limited. Walsh and Simon discuss new work by Ashrafi and colleagues (https://doi.org/10.1083/jcb.202305048) that finds Sirtuin 3 directs local metabolic adaptation at synapses during sustained glucose deprivation.

Fractal Resonance: Can Fractal Geometry Be Used to Optimize the Connectivity of Neurons to Artificial Implants?

In parallel to medical applications, exploring how neurons interact with the artificial interface of implants in the human body can be used to learn about their fundamental behavior. For both fundamental and applied research, it is important to determine the conditions that encourage neurons to maintain their natural behavior during these interactions. Whereas previous biocompatibility studies have focused on the material properties of the neuron-implant interface, here we discuss the concept of fractal resonance - the possibility that favorable connectivity properties might emerge by matching the fractal geometry of the implant surface to that of the neurons.To investigate fractal resonance, we first determine the degree to which neurons are fractal and the impact of this fractality on their functionality. By analyzing three-dimensional images of rat hippocampal neurons, we find that the way their dendrites fork and weave through space is important for generating their fractal-like behavior. By modeling variations in neuron connectivity along with the associated energetic and material costs, we highlight how the neurons' fractal dimension optimizes these constraints. To simulate neuron interactions with implant interfaces, we distort the neuron models away from their natural form by modifying the dendrites' fork and weaving patterns. We find that small deviations can induce large changes in fractal dimension, causing the balance between connectivity and cost to deteriorate rapidly. We propose that implant surfaces should be patterned to match the fractal dimension of the neurons, allowing them to maintain their natural functionality as they interact with the implant.

Distance from main arteries influences microstructural and functional brain tissue characteristics

Given the substantial dependence of neurons on continuous supply of energy, the distribution of major cerebral arteries opens a question whether the distance from the main supply arteries constitutes a modulating factor for the microstructural and functional properties of brain tissue. To tackle this question, multimodal MRI acquisitions of 102 healthy volunteers over the full adult age span were utilised. Relaxation along a fictitious field in the rotating frame of rank n = 4 (RAFF4), adiabatic T1ρ, T2ρ,  and intracellular volume fraction (fICVF) derived from diffusion-weighted imaging were implemented to quantify microstructural (cellularity, myelin density, iron concentration) tissue characteristics and degree centrality and fractional amplitude of low-frequency fluctuations to probe for functional metrics. Inverse correlation of arterial distance with robust homogeneity was detected for T1ρ, T2ρ and RAFF4 for cortical grey matter and white matter, showing substantial complex microstructural differences between brain tissue close and farther from main arterial trunks. Albeit with wider variability, functional metrics pointed to increased connectivity and neuronal activity in areas farther from main arteries. Surprisingly, multiple of these microstructural and functional distance-based gradients diminished with higher age, pointing to uniformization of brain tissue with ageing. All in all, this pilot study provides a novel insight on brain regionalisation based on artery distance, which merits further investigation to validate its biological underpinnings.

Metabolic energetics underlying attractors in neural models

Neural population modeling, including the role of neural attractors, is a promising tool for understanding many aspects of brain function. We propose a modeling framework to connect the abstract variables used in modeling to recent cellular-level estimates of the bioenergetic costs of different aspects of neural activity, measured in ATP consumed per second per neuron. Based on recent work, an empirical reference for brain ATP use for the awake resting brain was estimated as ∼2 × 109 ATP/s-neuron across several mammalian species. The energetics framework was applied to the Wilson-Cowan (WC) model of two interacting populations of neurons, one excitatory (E) and one inhibitory (I). Attractors were considered to exhibit steady-state behavior and limit cycle behavior, both of which end when the excitatory stimulus ends, and sustained activity that persists after the stimulus ends. The energy cost of limit cycles, with oscillations much faster than the average neuronal firing rate of the population, is tracked more closely with the firing rate than the limit cycle frequency. Self-sustained firing driven by recurrent excitation, though, involves higher firing rates and a higher energy cost. As an example of a simple network in which each node is a WC model, a combination of three nodes can serve as a flexible circuit element that turns on with an oscillating output when input passes a threshold and then persists after the input ends (an "on-switch"), with moderate overall ATP use. The proposed framework can serve as a guide for anchoring neural population models to plausible bioenergetics requirements.NEW & NOTEWORTHY This work bridges two approaches for understanding brain function: cellular-level studies of the metabolic energy costs of different aspects of neural activity and neural population modeling, including the role of neural attractors. The proposed modeling framework connects energetic costs, in ATP consumed per second per neuron, to the more abstract variables used in neural population modeling. In particular, this work anchors potential neural attractors to physiologically plausible bioenergetics requirements.

Ultralow Energy Consumption Angstrom-Fluidic Memristor

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic functions of biological neurons. Here, we report neuromorphic signaling using Angstrom-scale funnel-shaped channels with poly-l-lysine (PLL) assembled at nano-openings. We found frequency-dependent current-voltage characteristics under sweeping voltage, which represents a diode in low frequencies, but it showed pinched current hysteresis as frequency increases. The current hysteresis is strongly dependent on pH values but weakly dependent on salt concentration. We attributed the current hysteresis to the entropy barrier of PLL molecules entering and exiting the Angstrom channels, resulting in reversible voltage-gated open-close state transitions. We successfully emulated the synaptic adaptation of Hebbian learning using voltage spikes and obtained a minimum energy consumption of 2-23 fJ in each spike per channel. Our findings pave a new way to mimic neuronal functions by Angstrom channels in low energy consumption.

Information encoded in volumes and areas of dendritic spines is nearly maximal across mammalian brains

Many experiments suggest that long-term information associated with neuronal memory resides collectively in dendritic spines. However, spines can have a limited size due to metabolic and neuroanatomical constraints, which should effectively limit the amount of encoded information in excitatory synapses. This study investigates how much information can be stored in the population of sizes of dendritic spines, and whether it is optimal in any sense. It is shown here, using empirical data for several mammalian brains across different regions and physiological conditions, that dendritic spines nearly maximize entropy contained in their volumes and surface areas for a given mean size in cortical and hippocampal regions. Although both short- and heavy-tailed fitting distributions approach [Formula: see text] of maximal entropy in the majority of cases, the best maximization is obtained primarily for short-tailed gamma distribution. We find that most empirical ratios of standard deviation to mean for spine volumes and areas are in the range [Formula: see text], which is close to the theoretical optimal ratios coming from entropy maximization for gamma and lognormal distributions. On average, the highest entropy is contained in spine length ([Formula: see text] bits per spine), and the lowest in spine volume and area ([Formula: see text] bits), although the latter two are closer to optimality. In contrast, we find that entropy density (entropy per spine size) is always suboptimal. Our results suggest that spine sizes are almost as random as possible given the constraint on their size, and moreover the general principle of entropy maximization is applicable and potentially useful to information and memory storing in the population of cortical and hippocampal excitatory synapses, and to predicting their morphological properties.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

A thermodynamical model of non-deterministic computation in cortical neural networks

Neuronal populations in the cerebral cortex engage in probabilistic coding, effectively encoding the state of the surrounding environment with high accuracy and extraordinary energy efficiency. A new approach models the inherently probabilistic nature of cortical neuron signaling outcomes as a thermodynamic process of non-deterministic computation. A mean field approach is used, with the trial Hamiltonian maximizing available free energy and minimizing the net quantity of entropy, compared with a reference Hamiltonian. Thermodynamic quantities are always conserved during the computation; free energy must be expended to produce information, and free energy is released during information compression, as correlations are identified between the encoding system and its surrounding environment. Due to the relationship between the Gibbs free energy equation and the Nernst equation, any increase in free energy is paired with a local decrease in membrane potential. As a result, this process of thermodynamic computation adjusts the likelihood of each neuron firing an action potential. This model shows that non-deterministic signaling outcomes can be achieved by noisy cortical neurons, through an energy-efficient computational process that involves optimally redistributing a Hamiltonian over some time evolution. Calculations demonstrate that the energy efficiency of the human brain is consistent with this model of non-deterministic computation, with net entropy production far too low to retain the assumptions of a classical system.

Synaptic plasticity through a naturalistic lens

From the myriad of studies on neuronal plasticity, investigating its underlying molecular mechanisms up to its behavioral relevance, a very complex landscape has emerged. Recent efforts have been achieved toward more naturalistic investigations as an attempt to better capture the synaptic plasticity underpinning of learning and memory, which has been fostered by the development of in vivo electrophysiological and imaging tools. In this review, we examine these naturalistic investigations, by devoting a first part to synaptic plasticity rules issued from naturalistic in vivo-like activity patterns. We next give an overview of the novel tools, which enable an increased spatio-temporal specificity for detecting and manipulating plasticity expressed at individual spines up to neuronal circuit level during behavior. Finally, we put particular emphasis on works considering brain-body communication loops and macroscale contributors to synaptic plasticity, such as body internal states and brain energy metabolism.

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical for the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally and structurally characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with a mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

Energy optimisation predicts the capacity of ion buffering in the brain

Neurons store energy in the ionic concentration gradients they build across their cell membrane. The amount of energy stored, and hence the work the ions can do by mixing, can be enhanced by the presence of ion buffers in extra- and intracellular space. Buffers act as sources and sinks of ions, however, and unless the buffering capacities for different ion species obey certain relationships, a complete mixing of the ions may be impeded by the physical conditions of charge neutrality and isotonicity. From these conditions, buffering capacities were calculated that enabled each ion species to mix completely. In all valid buffer distributions, the [Formula: see text] ions were buffered most, with a capacity exceeding that of [Formula: see text] and [Formula: see text] buffering by at least an order of magnitude. The similar magnitude of the (oppositely directed) [Formula: see text] and [Formula: see text] gradients made extracellular space behave as a [Formula: see text]-[Formula: see text] exchanger. Anions such as [Formula: see text] were buffered least. The great capacity of the extra- and intracellular [Formula: see text] buffers caused a large influx of [Formula: see text] ions as is typically observed during energy deprivation. These results explain many characteristics of the physiological buffer distributions but raise the question how the brain controls the capacity of its ion buffers. It is suggested that neurons and glial cells, by their great sensitivity to gradients of charge and osmolarity, respectively, sense deviations from electro-neutral and isotonic mixing, and use these signals to tune the chemical composition, and buffering capacity, of the extra- and intracellular matrices.

Aerobic glycolysis is the predominant means of glucose metabolism in neuronal somata, which protects against oxidative damage

It is generally thought that under basal conditions, neurons produce ATP mainly through mitochondrial oxidative phosphorylation (OXPHOS), and glycolytic activity only predominates when neurons are activated and need to meet higher energy demands. However, it remains unknown whether there are differences in glucose metabolism between neuronal somata and axon terminals. Here, we demonstrated that neuronal somata perform higher levels of aerobic glycolysis and lower levels of OXPHOS than terminals, both during basal and activated states. We found that the glycolytic enzyme pyruvate kinase 2 (PKM2) is localized predominantly in the somata rather than in the terminals. Deletion of Pkm2 in mice results in a switch from aerobic glycolysis to OXPHOS in neuronal somata, leading to oxidative damage and progressive loss of dopaminergic neurons. Our findings update the conventional view that neurons uniformly use OXPHOS under basal conditions and highlight the important role of somatic aerobic glycolysis in maintaining antioxidant capacity.

Stable isotope tracing reveals disturbed cellular energy and glutamate metabolism in hippocampal slices of aged male mice

Neurons and astrocytes work in close metabolic collaboration, linking neurotransmission to brain energy and neurotransmitter metabolism. Dysregulated energy metabolism is a hallmark of the aging brain and may underlie the progressive age-dependent cognitive decline. However, astrocyte and neurotransmitter metabolism remains understudied in aging brain research. In particular, how aging affects metabolism of glutamate, being the primary excitatory neurotransmitter, is still poorly understood. Here we investigated critical aspects of cellular energy metabolism in the aging male mouse hippocampus using stable isotope tracing in vitro. Metabolism of [U-13C]glucose demonstrated an elevated glycolytic capacity of aged hippocampal slices, whereas oxidative [U-13C]glucose metabolism in the TCA cycle was significantly reduced with aging. In addition, metabolism of [1,2-13C]acetate, reflecting astrocyte energy metabolism, was likewise reduced in the hippocampal slices of old mice. In contrast, uptake and subsequent metabolism of [U-13C]glutamate was elevated, suggesting increased capacity for cellular glutamate handling with aging. Finally, metabolism of [15N]glutamate was maintained in the aged slices, demonstrating sustained glutamate nitrogen metabolism. Collectively, this study reveals fundamental alterations in cellular energy and neurotransmitter metabolism in the aging brain, which may contribute to age-related hippocampal deficits.

Energetically efficient learning in neuronal networks

Human and animal experiments have shown that acquiring and storing information can require substantial amounts of metabolic energy. However, computational models of neural plasticity only seldom take this cost into account, and might thereby miss an important constraint on biological learning. This review explores various ways to reduce energy requirements for learning in neural networks. By comparing the resulting learning rules to cognitive and neurophysiological observations, we discuss how energy efficiency might have shaped biological learning.

Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species

So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that self-initiated firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. To demonstrate the viability of our hypothesis, we built a set of models that incorporate recent experimental results indicating homeostatic control of metabolic products-Adenosine triphosphate (ATP), adenosine diphosphate (ADP), and reactive oxygen species (ROS)-by changes in firing. We explore the relationship of metabolic cost of spiking with its effect on the temporal patterning of spikes and reproduce experimentally observed changes in intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with ROS-modulated potassium channels. We also show that metabolic spiking homeostasis can produce indefinitely sustained avalanche dynamics in cortical circuits. Our theory can account for key features of neuronal activity observed in many studies ranging from ion channel function all the way to resting state dynamics. We finish with a set of experimental predictions that would confirm an integrated, crucial role for metabolically regulated spiking and firmly link metabolic homeostasis and neuronal function.

Deep reinforcement learning with significant multiplications inference

We propose a sparse computation method for optimizing the inference of neural networks in reinforcement learning (RL) tasks. Motivated by the processing abilities of the brain, this method combines simple neural network pruning with a delta-network algorithm to account for the input data correlations. The former mimics neuroplasticity by eliminating inefficient connections; the latter makes it possible to update neuron states only when their changes exceed a certain threshold. This combination significantly reduces the number of multiplications during the neural network inference for fast neuromorphic computing. We tested the approach in popular deep RL tasks, yielding up to a 100-fold reduction in the number of required multiplications without substantial performance loss (sometimes, the performance even improved).

Changes in brain perfusion with training-related visuomotor improvement in MS

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. A better understanding of the mechanisms supporting brain plasticity in MS would help to develop targeted interventions to promote recovery. A total of 29 MS patients and 19 healthy volunteers underwent clinical assessment and multi-modal MRI acquisition [fMRI during serial reaction time task (SRT), DWI, T1w structural scans and ASL of resting perfusion] at baseline and after 4-weeks of SRT training. Reduction of functional hyperactivation was observed in MS patients following the training, shown by the stronger reduction of the BOLD response during task execution compared to healthy volunteers. The functional reorganization was accompanied by a positive correlation between improvements in task accuracy and the change in resting perfusion after 4 weeks' training in right angular and supramarginal gyri in MS patients. No longitudinal changes in WM and GM measures and no correlation between task performance improvements and brain structure were observed in MS patients. Our results highlight a potential role for CBF as an early marker of plasticity, in terms of functional (cortical reorganization) and behavioral (performance improvement) changes in MS patients that may help to guide future interventions that exploit preserved plasticity mechanisms.

Prevalence and localization of nocturnal epileptiform discharges in mild cognitive impairment

Recent evidence shows that identifying and treating epileptiform abnormalities in patients with Alzheimer's disease could represent a potential avenue to improve clinical outcome. Specifically, animal and human studies have revealed that in the early phase of Alzheimer's disease, there is an increased risk of seizures. It has also been demonstrated that the administration of anti-seizure medications can slow the functional progression of the disease only in patients with EEG signs of cortical hyperexcitability. In addition, although it is not known at what disease stage hyperexcitability emerges, there remains no consensus regarding the imaging and diagnostic methods best able to detect interictal events to further distinguish different phenotypes of Alzheimer's disease. In this exploratory work, we studied 13 subjects with amnestic mild cognitive impairment and 20 healthy controls using overnight high-density EEG with 256 channels. All participants also underwent MRI and neuropsychological assessment. Electronic source reconstruction was also used to better select and localize spikes. We found spikes in six of 13 (46%) amnestic mild cognitive impairment compared with two of 20 (10%) healthy control participants (P = 0.035), representing a spike prevalence similar to that detected in previous studies of patients with early-stage Alzheimer's disease. The interictal events were low-amplitude temporal spikes more prevalent during non-rapid eye movement sleep. No statistically significant differences were found in cognitive performance between amnestic mild cognitive impairment patients with and without spikes, but a trend in immediate and delayed memory was observed. Moreover, no imaging findings of cortical and subcortical atrophy were found between amnestic mild cognitive impairment participants with and without epileptiform spikes. In summary, our exploratory study shows that patients with amnestic mild cognitive impairment reveal EEG signs of hyperexcitability early in the disease course, while no other significant differences in neuropsychological or imaging features were observed among the subgroups. If confirmed with longitudinal data, these exploratory findings could represent one of the first signatures of a preclinical epileptiform phenotype of amnestic mild cognitive impairment and its progression.

Interactions of amyloidogenic proteins with mitochondrial protein import machinery in aging-related neurodegenerative diseases

Most mitochondrial proteins are targeted to the organelle by N-terminal mitochondrial targeting sequences (MTSs, or "presequences") that are recognized by the import machinery and subsequently cleaved to yield the mature protein. MTSs do not have conserved amino acid compositions, but share common physicochemical properties, including the ability to form amphipathic α-helical structures enriched with basic and hydrophobic residues on alternating faces. The lack of strict sequence conservation implies that some polypeptides can be mistargeted to mitochondria, especially under cellular stress. The pathogenic accumulation of proteins within mitochondria is implicated in many aging-related neurodegenerative diseases, including Alzheimer's, Parkinson's, and Huntington's diseases. Mechanistically, these diseases may originate in part from mitochondrial interactions with amyloid-β precursor protein (APP) or its cleavage product amyloid-β (Aβ), α-synuclein (α-syn), and mutant forms of huntingtin (mHtt), respectively, that are mediated in part through their associations with the mitochondrial protein import machinery. Emerging evidence suggests that these amyloidogenic proteins may present cryptic targeting signals that act as MTS mimetics and can be recognized by mitochondrial import receptors and transported into different mitochondrial compartments. Accumulation of these mistargeted proteins could overwhelm the import machinery and its associated quality control mechanisms, thereby contributing to neurological disease progression. Alternatively, the uptake of amyloidogenic proteins into mitochondria may be part of a protein quality control mechanism for clearance of cytotoxic proteins. Here we review the pathomechanisms of these diseases as they relate to mitochondrial protein import and effects on mitochondrial function, what features of APP/Aβ, α-syn and mHtt make them suitable substrates for the import machinery, and how this information can be leveraged for the development of therapeutic interventions.

An exact mapping from ReLU networks to spiking neural networks

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an exact mapping from a network with Rectified Linear Units (ReLUs) to an SNN that fires exactly one spike per neuron. For our constructive proof, we assume that an arbitrary multi-layer ReLU network with or without convolutional layers, batch normalization and max pooling layers was trained to high performance on some training set. Furthermore, we assume that we have access to a representative example of input data used during training and to the exact parameters (weights and biases) of the trained ReLU network. The mapping from deep ReLU networks to SNNs causes zero percent drop in accuracy on CIFAR10, CIFAR100 and the ImageNet-like data sets Places365 and PASS. More generally our work shows that an arbitrary deep ReLU network can be replaced by an energy-efficient single-spike neural network without any loss of performance.

Metabolic remodeling in astrocytes: Paving the path to brain tumor development

The brain is a highly metabolic organ, composed of multiple cell classes, that controls crucial functions of the body. Although neurons have traditionally been the main protagonist, astrocytes have gained significant attention over the last decade. In this regard, astrocytes are a type of glial cells that have recently emerged as critical regulators of central nervous system (CNS) function and play a significant role in maintaining brain energy metabolism. However, in certain scenarios, astrocyte behavior can go awry, which poses a significant threat to brain integrity and function. This is definitively the case for mutations that turn normal astrocytes and astrocytic precursors into gliomas, an aggressive type of brain tumor. In addition, healthy astrocytes can interact with tumor cells, becoming part of the tumor microenvironment and influencing disease progression. In this review, we discuss the recent evidence suggesting that disturbed metabolism in astrocytes can contribute to the development and progression of fatal human diseases such as cancer. Emphasis is placed on detailing the molecular bases and metabolic pathways of this disease and highlighting unique metabolic vulnerabilities that can potentially be exploited to develop successful therapeutic opportunities.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Single-nucleus genomics in outbred rats with divergent cocaine addiction-like behaviors reveals changes in amygdala GABAergic inhibition

The amygdala processes positive and negative valence and contributes to addiction, but the cell-type-specific gene regulatory programs involved are unknown. We generated an atlas of single-nucleus gene expression and chromatin accessibility in the amygdala of outbred rats with high and low cocaine addiction-like behaviors following prolonged abstinence. Differentially expressed genes between the high and low groups were enriched for energy metabolism across cell types. Rats with high addiction index (AI) showed increased relapse-like behaviors and GABAergic transmission in the amygdala. Both phenotypes were reversed by pharmacological inhibition of the glyoxalase 1 enzyme, which metabolizes methylglyoxal-a GABAA receptor agonist produced by glycolysis. Differences in chromatin accessibility between high and low AI rats implicated pioneer transcription factors in the basic helix-loop-helix, FOX, SOX and activator protein 1 families. We observed opposite regulation of chromatin accessibility across many cell types. Most notably, excitatory neurons had greater accessibility in high AI rats and inhibitory neurons had greater accessibility in low AI rats.

Mitochondrial DNA damage triggers spread of Parkinson's disease-like pathology

In the field of neurodegenerative diseases, especially sporadic Parkinson's disease (sPD) with dementia (sPDD), the question of how the disease starts and spreads in the brain remains central. While prion-like proteins have been designated as a culprit, recent studies suggest the involvement of additional factors. We found that oxidative stress, damaged DNA binding, cytosolic DNA sensing, and Toll-Like Receptor (TLR)4/9 activation pathways are strongly associated with the sPDD transcriptome, which has dysregulated type I Interferon (IFN) signaling. In sPD patients, we confirmed deletions of mitochondrial (mt)DNA in the medial frontal gyrus, suggesting a potential role of damaged mtDNA in the disease pathophysiology. To explore its contribution to pathology, we used spontaneous models of sPDD caused by deletion of type I IFN signaling (Ifnb-/-/Ifnar-/- mice). We found that the lack of neuronal IFNβ/IFNAR leads to oxidization, mutation, and deletion in mtDNA, which is subsequently released outside the neurons. Injecting damaged mtDNA into mouse brain induced PDD-like behavioral symptoms, including neuropsychiatric, motor, and cognitive impairments. Furthermore, it caused neurodegeneration in brain regions distant from the injection site, suggesting that damaged mtDNA triggers spread of PDD characteristics in an "infectious-like" manner. We also discovered that the mechanism through which damaged mtDNA causes pathology in healthy neurons is independent of Cyclic GMP-AMP synthase and IFNβ/IFNAR, but rather involves the dual activation of TLR9/4 pathways, resulting in increased oxidative stress and neuronal cell death, respectively. Our proteomic analysis of extracellular vesicles containing damaged mtDNA identified the TLR4 activator, Ribosomal Protein S3 as a key protein involved in recognizing and extruding damaged mtDNA. These findings might shed light on new molecular pathways through which damaged mtDNA initiates and spreads PD-like disease, potentially opening new avenues for therapeutic interventions or disease monitoring.

Postictal hypoxia involves reactive oxygen species and is ameliorated by chronic mitochondrial uncoupling

Prolonged severe hypoxia follows brief seizures and represents a mechanism underlying several negative postictal manifestations without interventions. Approximately 50% of the postictal hypoxia phenomenon can be accounted for by arteriole vasoconstriction. What accounts for the rest of the drop in unbound oxygen is unclear. Here, we determined the effect of pharmacological modulation of mitochondrial function on tissue oxygenation in the hippocampus of rats after repeatedly evoked seizures. Rats were treated with mitochondrial uncoupler 2,4 dinitrophenol (DNP) or antioxidants. Oxygen profiles were recorded using a chronically implanted oxygen-sensing probe, before, during, and after seizure induction. Mitochondrial function and redox tone were measured using in vitro mitochondrial assays and immunohistochemistry. Postictal cognitive impairment was assessed using the novel object recognition task. Mild mitochondrial uncoupling by DNP raised hippocampal oxygen tension and ameliorated postictal hypoxia. Chronic DNP also lowered mitochondrial oxygen-derived reactive species and oxidative stress in the hippocampus during postictal hypoxia. Uncoupling the mitochondria exerts therapeutic benefits on postictal cognitive dysfunction. Finally, antioxidants do not affect postictal hypoxia, but protect the brain from associated cognitive deficits. We provided evidence for a metabolic component of the prolonged oxygen deprivation that follow seizures and its pathological sequelae. Furthermore, we identified a molecular underpinning of this metabolic component, which involves excessive oxygen conversion into reactive species. Mild mitochondrial uncoupling may be a potential therapeutic strategy to treat the postictal state where seizure control is absent or poor.

Laminar organization of neocortical activities during systemic anoxia

The neocortex is highly susceptible to metabolic dysfunction. When exposed to global ischemia or anoxia, it suffers a slowly propagating wave of collective neuronal depolarization that ultimately impairs its structure and function. While the molecular signature of anoxic depolarization (AD) is well documented, little is known about the brain states that precede and follow AD onset. Here, by means of multisite extracellular local field potentials and intracellular recordings from identified pyramidal cells, we investigated the laminar expression of cortical activities induced by transient anoxia in rat primary somatosensory cortex. Soon after the interruption of brain oxygenation, we observed a well-organized sequence of stereotyped activity patterns across all cortical layers. This sequence included an initial period of beta-gamma activity, rapidly replaced by delta-theta oscillations followed by a decline in all spontaneous activites, marking the entry into a sustained period of electrical silence. Intracellular recordings revealed that cortical pyramidal neurons were depolarized and highly active during high-frequency activity, became inactive and devoid of synaptic potentials during the isoelectric state, and showed subthreshold composite synaptic depolarizations during the low-frequency period. Contrasting with the strong temporal coherence of pre-AD activities along the vertical axis of the cortical column, the onset of AD was not uniform across layers. AD initially occurred in layer 5 or 6 and then propagated bidirectionally in the upward and downward direction. Conversely, the post-anoxic waves that indicated the repolarization of cortical neurons upon brain reoxygenation did not exhibit a specific spatio-temporal profile. Pyramidal neurons from AD initiation site had a more depolarized resting potential and higher spontaneous firing rate compared to superficial cortical cells. We also found that the propagation pattern of AD was reliably reproduced by focal injection of an inhibitor of sodium‑potassium ATPases, suggesting that cortical AD dynamics could reflect layer-dependent variations in cellular metabolic regulations.

Metabolic regulation of microglial phagocytosis: Implications for Alzheimer's disease therapeutics

Microglia, the resident immune cells of the brain, are increasingly implicated in the regulation of brain health and disease. Microglia perform multiple functions in the central nervous system, including surveillance, phagocytosis and release of a variety of soluble factors. Importantly, a majority of their functions are closely related to changes in their metabolism. This natural inter-dependency between core microglial properties and metabolism offers a unique opportunity to modulate microglial activities via nutritional or metabolic interventions. In this review, we examine the existing scientific literature to synthesize the hypothesis that microglial phagocytosis of amyloid beta (Aβ) aggregates in Alzheimer's disease (AD) can be selectively enhanced via metabolic interventions. We first review the basics of microglial metabolism and the effects of common metabolites, such as glucose, lipids, ketone bodies, glutamine, pyruvate and lactate, on microglial inflammatory and phagocytic properties. Next, we examine the evidence for dysregulation of microglial metabolism in AD. This is followed by a review of in vivo studies on metabolic manipulation of microglial functions to ascertain their therapeutic potential in AD. Finally, we discuss the effects of metabolic factors on microglial phagocytosis of healthy synapses, a pathological process that also contributes to the progression of AD. We conclude by enlisting the current challenges that need to be addressed before strategies to harness microglial phagocytosis to clear pathological protein deposits in AD and other neurodegenerative disorders can be widely adopted.

The energy challenges of artificial superintelligence

We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be "superintelligent". This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the "Erasi equation", for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions.

Neuroinflammation increases oxygen extraction in a mouse model of Alzheimer's disease

Neuroinflammation, impaired metabolism, and hypoperfusion are fundamental pathological hallmarks of early Alzheimer's disease (AD). Numerous studies have asserted a close association between neuroinflammation and disrupted cerebral energetics. During AD progression and other neurodegenerative disorders, a persistent state of chronic neuroinflammation reportedly exacerbates cytotoxicity and potentiates neuronal death. Here, we assessed the impact of a neuroinflammatory challenge on metabolic demand and microvascular hemodynamics in the somatosensory cortex of an AD mouse model. We utilized in vivo 2-photon microscopy and the phosphorescent oxygen sensor Oxyphor 2P to measure partial pressure of oxygen (pO2) and capillary red blood cell flux in cortical microvessels of awake mice. Intravascular pO2 and capillary RBC flux measurements were performed in 8-month-old APPswe/PS1dE9 mice and wildtype littermates on days 0, 7, and 14 of a 14-day period of lipopolysaccaride-induced neuroinflammation. Before the induced inflammatory challenge, AD mice demonstrated reduced metabolic demand but similar capillary red blood cell flux as their wild type counterparts. Neuroinflammation provoked significant reductions in cerebral intravascular oxygen levels and elevated oxygen extraction in both animal groups, without significantly altering red blood cell flux in capillaries. This study provides evidence that neuroinflammation alters cerebral oxygen demand at the early stages of AD without substantially altering vascular oxygen supply. The results will guide our understanding of neuroinflammation's influence on neuroimaging biomarkers for early AD diagnosis.

A simple 100% normobaric oxygen treatment can substantially enhance sequence learning processes

Motor learning processes are crucial for our everyday life, and improving skills by tailored interventions is of great clinical interest and value. Our previous work revealed a positive effect of normo-baric oxygen treatment on visuomotor adaptation. Here, we investigate whether it could positively affect sequence learning (SL) processes as well. Sixty-four healthy young adults were divided into a 100% oxygen treatment (NbOxTr; N = 32, M=20.7 ± 1.63 yrs.) and a normal air treatment (AirTr; N = 32, M=20.8 ± 0.95 yrs.) group. Participants performed a standardized SL task by pressing the spatial-compatible key on a keyboard according to four visual stimuli with two pre-determined 8-item sequences with different training depths. Following a baseline session (10 trials), both groups received a gas treatment (5 L/min, via nasal cannula) during the next training session (4 blocks, 45 trials each block), followed by a testing session (30 trials) without gas treatment. On day two, participants completed another 30 trials, similar to the first-day testing session, also without gas treatment. ANOVA revealed no significant group differences during baseline (p > 0.05) but a significantly faster response time (+45.5%) in the NbOxTr than AirTr group in the training session with gas treatment for all training depths (p < 0.05). The positive NbOxTr effect consolidated into the following testing session without gas treatment for deeply trained sequences (+17%; p < 0.05), and for all training depth on day-two testing (+45.2%; p < 0.05). Results suggest that the NbOxTr substantially improved participants' SL processing speed. Notably, improvements consolidated after an overnight sleep. The present work confirms a beneficial effect of a single, simple NbOxTr on fundamental motor learning processes. This treatment approach may provide promising implications for practice in neurological rehabilitation and other motor learning-related scenarios and should be further investigated in future research.

Alterations of neural activity in the prefrontal cortex associated with deficits in working memory performance

Working memory (WM), a core cognitive function, enables the temporary holding and manipulation of information in mind to support ongoing behavior. Neurophysiological recordings conducted in nonhuman primates have revealed neural correlates of this process in a network of higher-order cortical regions, particularly the prefrontal cortex (PFC). Here, we review the circuit mechanisms and functional importance of WM-related activity in these areas. Recent neurophysiological data indicates that the absence of these neural correlates at different stages of WM is accompanied by distinct behavioral deficits, which are characteristic of various disease states/normal aging and which we review here. Finally, we discuss emerging evidence of electrical stimulation ameliorating these WM deficits in both humans and non-human primates. These results are important for a basic understanding of the neural mechanisms supporting WM, as well as for translational efforts to developing therapies capable of enhancing healthy WM ability or restoring WM from dysfunction.

Tipping points in neurodegeneration

In Alzheimer's disease (AD), Aβ deposits form slowly, several decades before further pathological events trigger neurodegeneration and dementia. However, a substantial proportion of affected individuals remains non-demented despite AD pathology, raising questions about the underlying factors that determine the transition to clinical disease. Here, we emphasize the critical function of resilience and resistance factors, which we extend beyond the concept of cognitive reserve to include the glial, immune, and vascular system. We review the evidence and use the metaphor of "tipping points" to illustrate how gradually forming AD neuropathology in the preclinical stage can transition to dementia once adaptive functions of the glial, immune, and vascular system are lost and self-reinforcing pathological cascades are unleashed. Thus, we propose an expanded framework for pathomechanistic research that focuses on tipping points and non-neuronal resilience mechanisms, which may represent previously untapped therapeutic targets in preclinical AD.

Effects of stochastic coding on olfactory discrimination in flies and mice

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

Laws of taxation for multicellular organisms: The economics of sleep

In macro-public finance, the Ramsey rule (RR) concerns variable taxation to maximize social welfare and economic efficiency in a purely competitive monopolistic system. To extract tax revenue with the least loss of utility to the representative individual, RR dictates that optimal, proportionate taxes should be such as to diminish in the same proportion the production of each commodity taxed. The sources of supply that are inelastic, i.e., necessities/utilities, must be taxed more. We hypothesize that the Ramsey's economical approach might provide a general mechanism to investigate far-flung biological issues, such as preys/predators dynamics, food restriction in ecological niches, local changes in blood flow in rival or complementary organs of multicellular organisms. In particular, RR suggests a quantifiable relationship between the physiological decrease in cortical spike frequency occurring during sleep and energy consumption. Since small decreases in spike frequency during sleep are correlated with large decreases in the amount of consumed ATP, the brain could be considered an inelastic commodity which can be "taxed" more than other organs, allowing the whole organism to spare energy. Shedding light on the energy budget of the central nervous system, RR improves our knowledge of cerebral perfusion during sensory-evoked responses and tissue hypoxia caused by decreased blood flow, suggesting that energy from outside can be provided to counteract brain ischemia. In sum, the economical approach provided by Ramsey stands for a useful methodological tool that could be used in biological contexts to investigate the dynamical correlations among different organs in multicellular organisms.