bioRxiv Subject Collection: Neuroscience's Journal
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Saturday, June 14th, 2025
| Time |
Event |
| 5:39a |
Lipid droplet dysmetabolism affects cell homeostasis in an in vitro model of Alzheimer disease
Lipid droplets (LD) are dynamic organelles involved in neutral lipid storage, energy homeostasis, and can prevent lipotoxicity and oxidative distress. LD dysmetabolism has been considered a pathological hallmark in neurodegenerative disorders, including Alzheimer disease (AD). In this study, we investigated the alterations in LD metabolism and their impact on mitochondria-associated membranes (MAM) in an in vitro model of AD, namely the mouse neuroblastoma cell (N2A) line overexpressing the amyloid precursor protein with the familial Swedish mutation (APPswe). In APPswe cells, we found depletion of LD associated with an accumulation of free fatty acids that can be related with the observed LD degradation by chaperone-mediated autophagy. In these cells we also found decreased levels of seipin, which might contribute to triacylglycerol accumulation. These lipid alterations are associated with increased levels of ROS and lipid peroxidation in APPswe cells. The pharmacological modulation of DGAT1, that mediates triacylglycerol synthesis, normalized LD size and improved ER-mitochondria contacts and mitochondrial function in APPswe cells. In summary, these observations suggest the involvement of altered LD metabolism in AD pathophysiology, which impact on MAM and mitochondria function leading to cell dyshomeostasis. Our findings also support the idea that LD are relevant therapeutic targets in AD. | | 5:39a |
Personalized whole-brain Ising models with heterogeneous nodes capture differences among brain regions
Multiple lines of research have studied how complex brain dynamics emerge from underlying connectivity by using Ising models as simplified neural mass models. However, limitations on parameter estimation have prevented their use with individual, high-resolution human neuroimaging data. Furthermore, most studies focus only on connectivity, ignoring node heterogeneity, even though real brain regions have different structural and dynamical properties. Here we present an improved approach to fitting Ising models to 360-region functional MRI data: derivation of an initial guess model from group data, optimization of simulation temperature, and two stages of Boltzmann learning, first with group data, then with individual data. We then analyze how data binarization threshold affects goodness-of-fit, the role of the external field in model behavior, and correlations between model parameters and features from structural MRI, including myelination and sulcus depth. The fitted models can reproduce the functional connectivity of the data across a wide range of thresholds, but the role of the external field parameters in model behavior increases with threshold. In parallel, the coupling between nodes correlates with structural connectivity throughout this range, but correlations between the external field and structural features increase with threshold. These results show how our methodology enables personalized, biophysically interpretable modeling of structure-function relationships at the whole-brain level, which can aid understanding of individual differences in brain network organization and dynamics. This approach will help to bridge the gap between connectomics, which emphasizes brain networks, and translational neuroscience, which often focuses on the unique roles of brain regions. | | 5:39a |
Experience-dependent sharp-wave ripple deficits in an Alzheimer's disease mouse model
Amyloid pathology is a hallmark of Alzheimer's disease (AD). Hippocampal sharp-wave ripples (SWRs) play a role in memory consolidation and are impaired in various AD mouse models. However, it remains unclear how experience affects SWRs and how extrinsic signals contribute to SWR generation in AD. Here, by combining behavioral, in vivo electrophysiological and fiber photometry approaches, we show that an experience-dependent increase in hippocampal SWRs is disrupted in male and female 5xFAD mice. In wild-type mice, SWRs during non-rapid eye movement sleep (NREMS) increased after exploring a novel environment and the SWR rate gradually decreased with NREMS episodes and multiple behavioral sessions. However, 5xFAD mice did not show such experience-dependent SWR rate changes. A similar deficit was observed after a novel object recognition test. On the other hand, sleep spindles were intact in 5xFAD mice under all conditions. Since deficits in basal forebrain cholinergic neurons have been implicated in 5xFAD mice and SWRs are regulated by hippocampal cholinergic tone, we examined if hippocampal cholinergic signals could explain experience-dependent SWR deficits. Using fiber photometry and expressing a genetically encoded acetylcholine (ACh) sensor in the hippocampus, we found that ACh dynamics in the hippocampus were intact in 5xFAD mice across sleep-wake cycles, including NREMS, while we also found a negative correlation of infraslow cortical signal power dynamics with hippocampal ACh signals during NREMS regardless of genotypes. These results suggest that experience-dependent SWR deficits stem from non-cholinergic pathological changes. | | 6:46a |
The mPFC molecular clock mediates the effects of sleep deprivation on depression-like behavior and regulates sleep consolidation and homeostasis
Disruptions in sleep, circadian rhythms, and neural plasticity are closely linked to the pathophysiology and treatment of depression. Acute sleep deprivation (SD) produces rapid but transient antidepressant effects, yet the underlying mechanisms remain poorly understood. Using a mouse model of stress-induced depression, we found altered sleep architecture, impaired sleep homeostasis, and disrupted day-night oscillations of the markers of glutamatergic plasticity - Homer1a and synaptic AMPAR expression in the medial prefrontal cortex (mPFC). These changes were accompanied by a blunted homeostatic response to SD. We further show that SD and ketamine, both rapid-acting antidepressants, exert opposing effects on mPFC circadian gene expression: SD enhances the expression of negative clock loop genes (e.g., Per, Cry), mirroring stress effects, while ketamine downregulates these same genes. Targeted deletion of the core clock gene Bmal1 in CaMK2a-expressing excitatory neurons of the mPFC disrupted sleep-wake architecture, elevated slow-wave activity, and abolished the behavioral and molecular (Homer1a) response to SD. Additionally, pharmacological activation of the clock repressor REV-ERB suppressed the antidepressant effects of SD. Our results demonstrate that the mPFC molecular clock is essential for the regulation of sleep consolidation and homeostasis, and mediates the effects of SD on behavior. | | 6:46a |
Drosophila learn about properties of objects through physical interaction
Animals interact with unfamiliar objects to learn about their properties and guide future behavior, but the underlying neurobiological mechanism is not well understood. Here, we developed a behavioral paradigm in which freely walking Drosophila melanogaster are repeatedly guided to spherical objects using a visual cue. Flies exhibited diverse and structured object interaction motifs, including 'ball pulling', and 'ball walking', that evolved over time. Notably, flies developed a strong preference for immobile over mobile objects, despite their near identical appearance, suggesting they learn about object stability through physical interaction. This preference was impaired by silencing specific h{Delta} neurons in the fan-shaped body, a circuit known for spatial navigation through vector computations. h{Delta} neurons also modulated object interaction motifs and fidelity of following visual guidance cues, pointing to a role in balancing goal-directed and exploratory behaviors. These findings establish Drosophila as a model for investigating how internal representations and multimodal feedback contribute to adaptive object interaction. | | 7:16a |
White matter micro- and macrostructural properties in midlife individuals at risk for Alzheimer's disease: Associations with sex and menopausal status
Women are at greater lifetime risk for Alzheimer's disease (AD), potentially due to midlife fractional anisotropy (FA) and lower mean diffusivity in fornix and corpus callosum, indicating more densely organized white matter. Perimenopausal women were the exception, with white matter profiles closely resembling those of men. Perimenopausal women exhibited minimal or absent fiber cross-section and FDC sex differences and a reversal of the fornix FA advantage observed in pre- and postmenopausal women. These cross-sectional results are consistent with sex differences in white matter organization. Importantly, the perimenopause emerges as a critical window of neural reorganization in the female midlife aging brain characterized by temporary convergence toward male-like white matter organization. Longitudinal analyses are key to identifying women who do or do not revert to a premenopausal profile, which may inform AD risk. | | 7:16a |
Attenuated single neuron and network hyperexcitability following microRNA-134 inhibition in mice with drug-resistant temporal lobe epilepsy
The multi-factorial pathophysiology of acquired epilepsies lends itself to a multi-targeting therapeutic approach. MicroRNAs (miRNA) are short noncoding RNAs that individually can negatively regulate dozens of protein-coding transcripts. Previously, we reported that central injection of antisense oligonucleotides targeting microRNA-134 (Ant-134) shortly after status epilepticus potently suppressed the development of recurrent spontaneous seizures in rodent models of temporal lobe epilepsy. The mechanism(s) of these anti-seizure effects remain, however, incompletely understood. Here we show that intracerebroventricular microinjection of Ant-134 in male mice with pre-existing epilepsy caused by intraamygdala kainic acid-induced status epilepticus potently reduces the occurrence of spontaneous seizures. Recordings from ex vivo brain slices collected 2-4 days after Ant-134 injection in epileptic mice, detected a number of electrophysiological phenotypic changes consistent with reduced excitability. Specifically, Ant-134 reduced action potential bursts after current injection in CA1 neurons and reduced miniature excitatory post-synaptic current frequencies in CA1 neurons. Ant-134 also reduced general network excitability, including attenuating pro-excitatory CA1 responses to Schaffer collateral stimulation in hippocampal slices from epileptic mice. Together, the present study demonstrates inhibiting miR-134 reduces single neuron and network hyperexcitability in mice and extends support for this approach to treat drug-resistant epilepsies. | | 8:36a |
Replay of episodic experience in human infants
Sequentially structured and temporally compressed reactivation of episodes of experience plays a key role for memory and learning in the mature mammalian brain. This type of neural coding, known as replay, could also drive the enormous progress in sequential learning seen in human early life. Rodent models, however, suggest that replay emerges slowly and still refines in the juvenile phase. To test whether this prediction holds in humans, we introduced a novel fast event-related stimulation paradigm for infants undergoing non-invasive electroencephalography. Leveraging time-resolved decoding techniques we discovered replay of visual objects already in infants as young as 10 months. Reactivation responses revealed a forward sequential structure and temporal compression of the learnt sequence by a factor of 6 to 7. These results demonstrate that human replay develops considerably earlier than in rodents. Our insights stimulate future research exploring how replay contributes to the development of fundamental human cognitive capacities including language processing and action planning. | | 8:36a |
Modulation of decodable semantic features of brain activity via selection attention
We frequently encounter linguistic information in multiple modalities, such as text and speech, simultaneously. In such cases, we understand the information by selectively attending to one of the modalities. Previous research has shown that selective attention to a specific stimulus modality modulates cortical activity patterns. However, it remains unclear which aspects of linguistic information are selectively modulated by attention and how such modulation influences the decodability of semantic content. To address this question, we constructed decoding models of latent semantic features from the functional magnetic resonance imaging data of six participants in both unimodal (either visual or auditory) and bimodal conditions (simultaneously visual and auditory, with participants attending to a single modality at a time). In unimodal conditions, we successfully decoded the semantic contents from the brain activity. Decodable features were consistent across modalities in both intra-modal and cross-modal decoding. For bimodal conditions, decoding accuracies for the attended stimuli were higher than for the ignored when training and test stimuli belonged to the same modality. Furthermore, decodable features were more consistent across modalities with attended than ignored stimuli in both intra-modal and cross-modal decoding. These results indicate common decodable semantic features regardless of the presentation modality and that selective attention enhances the semantic representations contributing to such decodability. | | 8:36a |
Credit Assignment via Behavioral Timescale Synaptic Plasticity: Theoretical Frameworks
Behavioral Timescale Synaptic Plasticity (BTSP) is a form of synaptic plasticity in which dendritic Ca2+; plateau potentials in hippocampal pyramidal neurons drive rapid place field formation. Unlike traditional learning rules, BTSP learns correlations on the timescales of seconds and rapidly changes single-unit activity in only a few trials. To explore how BTSP-like learning can be integrated into network models, we propose a generalized BTSP rule (gBTSP), which we apply to unsupervised and supervised learning tasks, in both feedforward and recurrent networks. Unsupervised gBTSP mirrors classical frameworks of competitive learning, learning place field maps (in the feed-forward case), and attractive memory networks (in the recurrent case). For supervised learning, we show that plateau events can reduce task error, enabling gBTSP to solve tasks such as trajectory matching and delayed non-match-to-sample. However, we find that credit assignment via gBTSP becomes harder to achieve with increased network depth or CA3-like recurrence. This suggests that additional features may be needed to support BTSP-mediated few-shot learning of complex tasks in the hippocampus. | | 9:46a |
A theoretically driven calculation for language dominance and degree of multilingualism
Bilingualism research has long been challenged by a lack of a unified approach to quantifying language dominance and degree of multilingualism. While numerous questionnaires (e.g., LHQ, BLP, LEAP Q, and LUQ) provide valuable data on language background variables, they lack a standardized formula to compute key measures from it. We introduce two formulas that synthesize critical linguistic variables to efficiently calculate language dominance and a multilingualism score that ranges from perfect monolingualism to native-like proficiency in multiple languages. Validation across two large datasets shows our dominance measure closely aligns with more complex PCA methods while being simpler and more efficient. | | 9:46a |
Mapping concept and relational semantic representation in the brain using large language models
How the brain organizes semantic information is one of the most challenging and expansive questions in cognitive neuroscience. To shed light on this issue, prior studies have attempted to decode how the brain represents concepts. We instead examined how relational information is encoded, which we pursued by submitting texts to a contemporary large language model and extracting relational embeddings from the model. Using behavioral data (N = 636), we found these embeddings capture independent information about scenes and objects, along with relational information on their semantic links. Turning to fMRI data (N = 60), we leveraged these embeddings for representational similarity analysis: The occipitotemporal cortex represents concepts in isolation, whereas the dorsolateral prefrontal cortex and basal ganglia principally encode relational information. Relational coding within prefrontal and striatal areas also tracks how participants reason about scenes and objects. Altogether, this research maps how information progresses from concept-level to integrative forms and how this translates into behavior. |
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