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Category: neuroscience – Page 5

Fat particles could be key to treating metabolic brain disorders

Evidence challenging the long-held assumption that neuronal function in the brain is solely powered by sugars has given researchers new hope of treating debilitating brain disorders. A University of Queensland study led by Dr. Merja Joensuu and published in Nature Metabolism showed that neurons also use fats for fuel as they fire off the signals for human thought and movement.

“For decades, it was widely accepted that relied exclusively on glucose to fuel their functions in the brain,” Dr. Joensuu said. “But our research shows fats are undoubtedly a crucial part of the neuron’s in the brain and could be a key to repairing and restoring function when it breaks down.”

Dr. Joensuu from the Australian Institute for Bioengineering and Nanotechnology along with lab members Ph.D. candidate Nyakuoy Yak and Dr. Saber Abd Elkader from UQ’s Queensland Brain Institute set out to examine the relationship of a particular gene (DDHD2) to hereditary spastic paraplegia 54 (HSP54).

Neuroscientists can now predict dementia from the way you breathe in your sleep

Scientists have discovered that disrupted breathing during sleep, particularly conditions like sleep apnea, creates a measurable cascade of brain changes that predicts cognitive decline with startling accuracy.

Recent research analyzing over one million health records found that people with sleep-disordered breathing face between 1.3 and 5.11 times higher risk of developing various forms of dementia, depending on the specific condition.

The most dramatic finding: those with documented sleep breathing problems showed dementia risk ratios that peaked above five-fold for certain neurodegenerative diseases.

Biomaterials and cell-based therapy post spinal cord injury

Spinal cord injury (SCI) imposes a significant physical, social, and economic burden on millions of patients and their families worldwide. Although medical and surgical care improvements have decreased mortality rates, sustained recovery remains constrained. Cell-based therapies offer a promising strategy for neuroprotection and neuro-regeneration post-SCI. This article reviews the most promising preclinical approaches, encompassing the transplantation of embryonic stem cells (ESCs), mesenchymal stem cells (MSCs), neural stem cells (NSCs), oligodendrocyte progenitor cells (OPCs), Schwann cells (SCs), and olfactory ensheathing cells (OECs), along with the activation of endogenous pluripotency cell banking strategies. We also outline key ancillary strategies to enhance graft cell viability and differentiation, such as trophic factor assistance, engineered biomaterials for supportive scaffolds, and innovative methods for a synergistic effect in treatment, including promoting neuronal regeneration and reducing glial scars. We highlight the key aspects of SCI pathophysiology, the fundamental biology of cell treatments, and the advantages and limitations of each approach.

There are several approaches to treating spinal cord injuries that show great promise: Cellular therapies, which utilize a range of cells such as embryonic, neural, and mesenchymal stem cells, along with astrocytes, Schwann cells, olfactory ensheathing cells, and reprogrammed cells; The use of innovative biomaterials, including hydrogels, collagen, polycaprolactone fibers, and advanced 3D-printing technologies, provides valuable support for tissue repair.

Brain probe powerfully records neural circuits during behavior

Trying to document how single brain cells participate in networks that govern behavior is a daunting task. Brain probes called Neuropixels, which feature high-density silicon arrays, have enabled scientists to collect electrophysiological data of this nature from a variety of animals. These include fish, reptiles, rodents and primates, as well as humans.

Neuropixels, which come in several versions, record electrical activity from hundreds to thousands of neurons simultaneously. Neurons are nerve cells that receive, process and transmit information.

While the data collected has led to insights on the neural basis of perception and decision-making, those probes cannot sample fine-scale brain structures. They also are limited in resolving (separately distinguishing) the electrical fields around individual brain cells.

‘Alien: Earth’ predicts a transhumanist future, but could we ever digitize human consciousness? We asked the experts (exclusive)

Alien: Earth suggests a future where consciousness isn’t confined to the body you’re born in, but is this a realistic possibility, or merely science fiction? We spoke to experts to find out.

Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference

The (changing) belief distribution over possible environmental states may be represented in ventromedial prefrontal cortex (vmPFC). Several lines of evidence point to a general function of this brain region in maintaining a compact internal model of the environment (ie state belief) by extracting information across individual experiences to guide goal-directed behavior, such as the value of different choice options (Levy and Glimcher 2012; Averbeck and O’Doherty 2022; Klein-Flügge et al. 2022), cognitive maps (Boorman et al. 2021; Klein-Flügge et al. 2022; Schuck et al. 2016; Wilson et al. 2014), or schemas (Gilboa and Marlatte 2017; Bein and Niv 2025). Studies employing probabilistic learning tasks furthermore show that neural activity in vmPFC also reflects uncertainty about external states, which were linked to adaptive exploration behavior and learning-rate adjustments (Karlsson et al. 2012; McGuire et al. 2014; Starkweather et al. 2018; Domenech et al. 2020; Trudel et al. 2021). Notably, Karlsson et al. (2012) found that trial-to-trial neural population spiking variability in the medial PFC of mice peaked around transitions from exploitation to exploration periods following changes in reward structure when state uncertainty is highest, which may reflect more variable belief states. While ours is the first study to link human brain signal variability to belief precision, a previous study by Grady and Garrett (2018) observed increased BOLD signal variability while subjects performed externally-versus internally-oriented tasks; an effect spanning the vmPFC and other nodes of the canonical default mode network (DMN; Yeo et al. 2011). Since learning an abstract world model reflects a shift towards an internal cognitive mode, we tentatively expected brain signal variability compression over the course of learning to be (partly) expressed in the vmPFC.

We assume that uncertainty-related neural dynamics unfold on a fast temporal scale, as suggested by electrophysiological evidence in human and nonhuman animals (Berkes et al. 2011; Palva et al. 2011; Rouhinen et al. 2013; Honkanen et al. 2015; Orbán et al. 2016; Grundy et al. 2019). However, within-trial dynamics should also affect neural variability across independent learning trials (see Fig. 1). A more variable system should have a higher probability of being in a different state every time it is (sparsely) sampled. Conversely, when a system is in a less stochastic state, the within-trial variance is expected to reduce, yielding less across-trial variance at the same time. This argument aligns with work by Orbán et al. (2016), who showed that a computational model of the sampling account of sensory uncertainty captures empirically observed across-trial variability of neural population responses in primary visual cortex. In the case of human research, this means that neuroimaging methods with slower sampling rates, such as functional MRI (fMRI), may be able to approximate within-trial neural variability from variability observed across trials. Indeed, the majority of previous fMRI studies reporting within-region, within-subject modulation of brain signal variability by task demand have exclusively employed block designs, necessitating that the main source of variability be between-rather than within-trial (Garrett et al. 2013; Grady and Garrett 2014; Garrett et al. 2015; Armbruster-Genç et al. 2016).

In the current study, we acquired fMRI while participants performed a “marble task”. In this task, participants had to learn the probability of drawing a blue marble from an unseen jar (ie urn) based on five samples (ie draws from the urn with replacement). In a Bayesian inference framework, the jar marble ratio can be considered a latent state that participants must infer. We hypothesized that (i) across-trial variability in the BOLD response (SDBOLD) would compress over the sampling period, thus mirroring the reduction in state uncertainty, and that (ii) subjects with greater SDBOLD compression would show smaller estimation errors of the jars’ marble ratios as an index of more efficient belief updating. A secondary aim of the current study was to directly compare the effect of uncertainty on SDBOLD with a more standard General Linear Modeling (GLM) approach, which looks for correlations between average BOLD activity and uncertainty. This links our findings directly to previous investigations of neural uncertainty correlates, which disregarded the magnitude of BOLD variability (Huettel et al. 2005; Grinband et al. 2006; Behrens et al. 2007; Bach et al. 2011; Bach and Dolan 2012; Badre et al. 2012; Vilares et al. 2012; Payzan-LeNestour et al. 2013; McGuire et al. 2014; Michael et al. 2015; Meyniel and Dehaene 2017; Nassar et al. 2019; Meyniel 2020; Tomov et al. 2020; Trudel et al. 2021; Walker et al. 2023). We hypothesized (iii) that SDBOLD would uniquely predict inference accuracy compared to these standard neural uncertainty correlates.

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