bioRxiv Subject Collection: Neuroscience's Journal
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Saturday, March 22nd, 2025
| Time |
Event |
| 8:32a |
Three-photon population imaging of subcortical brain regions
Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of [≤]0.3 mm2. Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of [gt]3 mm2. LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate. | | 8:32a |
Differential representations of spatial location by aperiodic and alpha oscillatory activity in working memory
Decades of research have shown working memory (WM) relies on sustained prefrontal cortical activity and visual extrastriate activity, particularly in the alpha (8-12 Hz) frequency range. This alpha activity tracks the spatial location of WM items, even when spatial position is task-irrelevant and there is no stimulus currently being presented. Traditional analyses of putative oscillations using bandpass filters, however, conflate oscillations with non-oscillatory aperiodic activity. Here, we reanalyzed seven different human electroencephalography (EEG) visual WM datasets to test the hypothesis that aperiodic activity--which is thought to reflect the relative contributions of excitatory and inhibitory drive--plays a distinct role in visual WM from true alpha oscillations. To do this, we developed a novel, time-resolved spectral parameterization approach to disentangle oscillations from aperiodic activity during WM encoding and maintenance. Across all seven tasks, totaling 112 participants, we captured the representation of spatial location from total alpha power using an inverted encoding model (IEM), replicating traditional analyses. We then trained separate IEMs to estimate the strength of spatial location representation from aperiodic-adjusted alpha (reflecting just the oscillatory component) and aperiodic activity, and find that IEM performance improves for aperiodic-adjusted alpha compared to total alpha power that blends the two signals. We also discover a novel role for aperiodic activity, where IEM performance trained on aperiodic activity is highest during stimulus presentation, but not during the WM maintenance period. Our results emphasize the importance of controlling for aperiodic activity when studying neural oscillations while uncovering a novel functional role for aperiodic activity in the encoding of visual WM information. | | 9:48a |
Reticular Thalamic Hyperexcitability Drives Autism Spectrum Disorder Behaviors in the Cntnap2 Model of Autism
Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders characterized by social communication deficits, repetitive behaviors, and comorbidities such as sensory abnormalities, sleep disturbances, and seizures. Dysregulation of thalamocortical circuits has been implicated in these comorbid features, yet their precise roles in ASD pathophysiology remain elusive. This study focuses on the reticular thalamic nucleus (RT), a key regulator of thalamocortical interactions, to elucidate its contribution to ASD-related behavioral deficits using a Cntnap2 knockout (KO) mouse model. Our behavioral and EEG analyses comparing Cntnap2+/+ and Cntnap2-/- mice demonstrated that Cntnap2 knockout heightened seizure susceptibility, elevated locomotor activity, and produced hallmark ASD phenotypes, including social deficits, and repetitive behaviors. Electrophysiological recordings from thalamic brain slices revealed increased spontaneous and evoked network oscillations with increased RT excitability due to enhanced T-type calcium currents and burst firing. We observed behavior related heightened RT population activity in vivo with fiber photometry. Notably, suppressing RT activity via Z944, a T-type calcium channel blocker, and via C21 and the inhibitory DREADD hM4Di, improved ASD-related behavioral deficits. These findings identify RT hyperexcitability as a mechanistic driver of ASD behaviors and underscore RT as a potential therapeutic target for modulating thalamocortical circuit dysfunction in ASD. | | 9:48a |
Mitochondrial DNA heteroplasmy drives cortical neuronal disturbances in human organoids harbouring the common m.3243A>G mutation
Mitochondrial diseases frequently affect the brain leading to severe and disabling neurological symptoms. The heteroplasmic m.3243A>G mutation in MT-TL1, encoding mt-tRNALeu, is responsible for ~80% of mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS), which is one of the most characteristic mitochondrial syndromes, leading to disability and early death. There are no animal models harbouring this mutation to provide precise mechanistic insights informing therapeutic interventions. Here, we generated a human iPSC-derived cerebral organoid slice model that recapitulates cortical architecture and mitochondrial pathology. Using biological assays and single-cell RNA sequencing, we uncovered heteroplasmy-dependent transcriptional shifts and changes in key cellular processes in cortical neurons. Organoids with high heteroplasmy showed a predominant impairment of deep-layer neurons triggered by mitochondrial stress, leading to axonal degeneration and apoptosis, similar to brain autopsy of a MELAS patient. Our findings provide insights into the vulnerability of long-range projection neurons in mitochondrial diseases, advancing our understanding of disease mechanisms with a view to potential therapeutic strategies. | | 9:48a |
Auditory Perception Induces Cortical and Thalamic Event-Related Desynchronization in the Mouse
Studies of human perception have shown early cortical signals for primary information encoding, and later signals for higher order processing. An important late signal is the cortical event-related desynchronization (ERD) in the alpha (8-12Hz) and beta (12-30Hz) frequency band, which has been linked to human perceptual awareness. Detailed mechanistic investigation of the ERD would be greatly facilitated by availability of a suitable animal model. We conducted local field potential recordings in the mouse frontal association cortex (FrA), thalamic intralaminar centrolateral nucleus (Cl), primary auditory cortex (A1), and primary visual cortex (V1) during two auditory tasks. Fully audible brief 50 ms stimuli with both tasks produced early broadband gamma (30-100Hz) frequency activity at 0-250ms, followed by a late cortical alpha/beta ERD 250 - 750 ms after stimulus onset. The ERD was statistically significant in FrA and A1, but not in V1. Interestingly, a significant ERD was also observed in thalamic Cl. The magnitude of the ERD at full stimulus intensity, and the slope of the relationship between stimulus intensity versus ERD magnitude, were both largest in FrA, and smaller in Cl and A1. Conversely, for early broadband gamma activity the magnitude at full intensity and slopes were largest in A1, smaller in Cl and smaller still in FrA. These findings suggest that mice, like humans, process perceptual signals in hierarchically organized corticothalamic networks, and strongly support mice as a promising platform for further investigation of the ERD to better understand the origin and function of this robust yet understudied electrophysiological phenomenon. | | 9:48a |
Brain structural modules associated to functional high-order interactions in the human brain,
The brain's modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brain's efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brain's high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brain's modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brain's architecture underlying its modular organization. | | 6:30p |
Resting-State EEG Aperiodic Exponent Moderates the Association Between Age and Memory Performance in Older Adults
Memory functions are susceptible to age-related cognitive decline, making it essential to explore the underlying neurophysiological mechanisms that contribute to memory function during healthy ageing. Resting-state EEG (rsEEG) parameters, particularly the aperiodic exponent, a marker of cortical excitation-inhibition balance, and individual alpha frequency (IAF), a correlate of neural processing efficiency, have demonstrated associations with ageing and cognitive functions. This study investigated associations between these rsEEG markers and performance across multiple memory systems in healthy older adults (n = 99) aged 50-84 years, specifically the moderating and mediating effects on memory and age-memory relationships across episodic, working, and visual short-term memory systems, assessed via computerised tasks. Results revealed significant moderating effects of the aperiodic exponent on age-related performance in episodic (EM) and visual short-term memory (VSTM). Notably, for individuals with a higher exponent, age was not significantly associated with EM or VSTM performance, whereas those with average and lower exponent values showed poorer performance with older age. These findings suggest that average and lower aperiodic exponents may reflect a marker of decrement in age-related memory performance and higher exponents may index an underlying protective mechanism against age-related memory decline. This investigation extends the current understanding of cognitive ageing mechanisms by identifying the aperiodic exponent as a potential biomarker explaining individual differences in cognitive ageing trajectories in older adult populations, particularly in EM and VSTM systems, and establishes a framework for studying neuroprotective mechanisms and developing interventions to preserve cognitive function in older adults. | | 8:30p |
MEEGNet: An open source python library for the application of convolutional neural networks to MEG
Artificial Neural Networks (ANNs) are rapidly gaining traction in neuroscience, proving invaluable for decoding and modeling brain signals from techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Although these networks are beginning to find applications in magnetoencephalography (MEG), their use in this domain is still in the early stages. Here, we introduce MEEGNet, a novel Python library paired with an intuitive convolutional neural network (CNN) architecture designed primarily for MEG data, yet adaptable to EEG signals. The MEEGNet model was trained and cross-validated using MEG data from 643 participants across four classification tasks, including auditory and visual stimulus classification and age prediction. Our model achieves competitive performance across all tasks, with a notable balance of accuracy and efficiency - for instance, reaching 92.70% test accuracy in an auditory vs. visual classification task while maintaining shorter training times than other architectures. The MEEGNet pipeline also integrates latent space visualization tools, adapted for MEG and EEG data. These include saliency maps and Grad-CAM methods, which enhance the interpretability of ANN-based classification and help address the black-box critique of such models. Importantly, the MEEGNet library is designed for extensibility, allowing the neuroscience and machine learning communities to add functionalities and ANN models. By prioritizing usability, transparency, and interpretability, MEEGNet empowers MEG- and EEG-based research with a user-friendly and modular framework. |
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