bioRxiv Subject Collection: Neuroscience's Journal
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Sunday, December 29th, 2024
| Time |
Event |
| 4:38a |
Interaction between native and prosthetic visual responses in optogenetic visual restoration
Degenerative retinal disorders leading to irreversible photoreceptor death are a common cause of blindness. Optogenetic gene therapy aims to restore vision in affected individuals by introducing light sensitive opsins into the surviving neurons of inner retina. While up until now the main focus of optogenetic therapy has been on terminally blind individuals, treating at stages where residual native vision is present could have several advantages. Yet, it is still unknown how residual native and optogenetic vision would interact if present at the same time.
Using transgenic mice expressing the optogenetic tool ReaChR in ON-bipolar cells, we herein examine this interaction through electroretinography (ERG) and visually evoked potentials (VEP). We find that optogenetic responses show a peculiar ERG signature and are enhanced in retinas without photoreceptor loss. Conversely, native responses are dampened in the presence of ReaChR. Moreover, in VEP recordings we find that optogenetic responses reach the cortex asynchronous to the native response.
These findings should be taken into consideration when planning future clinical trials and may direct future preclinical research to optimize optogenetic approaches for visual restoration. The identified ERG signatures moreover may serve to track treatment efficiency in clinical trials. | | 4:38a |
Volitional stopping is preceded by a transient beta oscillation
Human motor cortex EEG beta (15-30 Hz) oscillations undergo transient power modulations (bursts) during volitional control of movements. They are a potential control signal for brain-machine interfaces and are a therapeutic target in Parkinsons disease. The prevailing view is that EEG beta bursts increase during stopping and immobility, but do not precede stopping. In contrast to prior work in humans and animals that used a latent and unobservable stopping time in the stop-signal task, we developed a translational animal model to align EEG with overt action stopping. We recorded 32-electrode EEG along with the angular velocity of a treadmill while head-fixed rats stopped in-progress running on a freely-rotating, non-motorized treadmill. Contrasting prior work, motor cortex beta bursts increased before stopping and not during stopping or immobility. Using information theoretic measures, we show that beta power was informative about treadmill velocity 200 msec in the future, but only during planning to stop. By introducing artificial temporal jitter to mimic the estimation of stopping time used in prior work, we show that this predictive brain-action relationship fails with even small jitter. Finally, we use a variety of machine learning methods to show that, despite EEG beta oscillations being a clear neural correlate preceding stopping, it has limited utility for real-time action decoding. Our work suggests a new conceptual model for neural control of action stopping. | | 4:38a |
Hampered AMPK-ULK1 cascade in Alzheimer disease (AD) instigates mitochondria dysfunctions and AD-related alterations that are alleviated by metformin
BackgroundMitochondrial structure and function alterations are key pathological features in Alzheimers disease (AD) brains. The adenosine monophosphate-activated protein kinase (AMPK) and its downstream effector Unc-51 like autophagy activating kinase 1 (ULK1) represent a key node controlling mitochondria health, the alteration of which likely contribute to AD development.
MethodsWe designed this study to investigate AMPK-ULK1 activation state in post-mortem human sporadic AD brains, in 3xTgAD mice that recapitulate most of human AD features, and in neuronal cells expressing the amyloid precursor protein with the familial Swedish mutation (APPswe). We examined the impact of the pharmacological and genetic modulation of AMPK-ULK1 cascade on mitochondria structure and functions in APPswe cells. We evaluated the potential beneficial impact of AMPK-ULK1 activation by Metformin (Met) on mitochondria defects, as well as on early- and late-stage AD-related alterations in vivo and ex vivo.
ResultsAt first, we show that AMPK-ULK1 cascade is defective in murine and human AD brains as well as in APPswe cells. We then report that Met administration to 3xTgAD mice alleviates the alterations of neuronal mitochondria structure and function and we consolidate these results in cells using both pharmacological and genetic tools to modulate AMPK-ULK1 cascade. In mice brains, Met reduces the early accumulation of APP C-terminal fragments (APP-CTFs) as well as the amyloid beta (A{beta}) burden present in aged mice. Mechanistically, we show that Met increases the localization of APP-CTFs within cathepsin D-positive lysosomal compartments in vivo and enhances cathepsin D activity in vitro. The reduction of A{beta} load by Met occurs through an increased recruitment of Iba1+ cells to A{beta} plaques and an enhancement of the phagocytic activity of microglia. Accordingly, in symptomatic 3xTgAD mice, Met alleviates microgliosis and astrogliosis, modulates microglia morphology, reduces peripheral proinflammatory cytokines levels, and regulates the expression of a set of inflammatory genes. In addition, Met normalizes dendritic spines shape in organotypic hippocampal slice cultures modeling AD and improves learning performance of 3xTgAD mice.
ConclusionsOur study demonstrates potential therapeutic benefits of targeting AMPK-ULK1 cascade to reverse both early and late AD-related alterations, deserving further investigation in fundamental research and in human clinical studies. | | 4:38a |
Functional expression and sex dimorphism of the T-type Cav3.2 Calcium Channel in human DRG Neurons
T-type/Cav3 calcium channels are key in neuronal excitability and pain processing with Cav3.2 being the prominent isoform in primary sensory neurons of the dorsal root ganglion (DRG). Its pharmacological inhibition or gene silencing induces analgesia in several preclinical models of inflammatory and neuropathic pain. However, the presence of Cav3.2, encoded by the CACNA1H gene, in human DRG neurons remains unresolved. Using RNA in-situ hybridization and electrophysiological recordings, we show that human DRGs express Cav3.2 in a subset of neurons positive for the neurotrophic factor receptor TrkB (NTRK2 gene). The Cav3.2 current exhibits typical biophysical and pharmacological properties, including inhibition by a low concentration of nickel and by Z944, a specific T-type calcium channel blocker in advanced clinical development. Conversely, ABT-639, a T-type calcium channel inhibitor that failed in Phase 2 trials for pain relief, does not inhibit Cav3.2 currents in human DRG neurons. Importantly, Cav3.2 currents are prominent in neurons from female organ donors, supporting the presence of sex differences in pain mechanisms in humans. These findings underscore the potential of continued exploration of Cav3.2 as a therapeutic target for pain treatment and highlight a specific subset of human neurons that likely rely on this channel to modulate their excitability. | | 4:38a |
Human precentral gyrus neurons link speech sequences from listening to speaking
Speech perception and production are interconnected processes, but the underlying neural mechanisms remain unclear. We investigated this relationship by recording large-scale single-neuron activity in the human brain during a delayed sentence repetition task. Contrary to the traditional view that the precentral gyrus is solely responsible for motor execution, we found that neurons there encoded activity across all task phases of listening, delay, initiation, and speaking. Notably, we discovered "mirror" neurons that activated transiently after hearing and before producing specific speech sounds, and "bridge" neurons that maintained activity between the same speech elements during listening and speaking. Population analysis revealed distinct latent components for each task phase and persistent dynamics for specific sentences. Overall, this study provides novel insights into the neuronal basis of speech processing, emphasizing the intricate interplay between perception, production, and verbal working memory. | | 4:38a |
Convergent representations and spatiotemporal dynamics of speech and language in brain and deep neural networks
Recent studies have explored the correspondence between single-modality DNN models (speech or text) and specific brain networks for speech and language. The key factors underlying these correlations and their spatiotemporal evolution within the brain language network remain unclear, particularly across different DNN modalities. To address these questions, we analyzed the representation similarity between self-supervised learning (SSL) models for speech (Wav2Vec2) and language (GPT-2), against neural responses to naturalistic speech captured via high-density electrocorticography. Our results indicated high prediction accuracy of both types of SSL models relative to neural activity before and after word onsets. It was the shared components between Wav2Vec2.0 and GPT-2 that explained the majority portion of the SSL-brain similarity. Furthermore, we observed distinct spatiotemporal dynamics: both models showed high encoding accuracy 40 milliseconds before word onset, especially in the mid-superior temporal gyrus (mid-STG), which can be explained by the shared contextual components in the SSL models; the Wav2Vec2.0 also peaked at 200 milliseconds after word onset around the posterior STG, which was mainly attributed to the acoustic-phonetic and static semantic information encoded in the SSL models. These results highlight how contextual and acoustic-phonetic cues encoded in DNNs align with spatiotemporal neural activity patterns, suggesting a significant overlap in how artificial and biological systems process linguistic information. | | 7:01a |
Fluid intelligence relates to neural measures of cognitive map formation
Psychometric research on intelligence consistently identifies latent factors underlying performance correlations across cognitive tasks, with one general factor (g) explaining most variance and predicting general life outcomes. Their biological basis is yet unresolved, in particular with regard to the neural information processing mechanisms that may underlie intelligence. Here we test the hypothesis that interindividual differences in relational processing, supported by cognitive maps in the hippocampus, are related to fluid intelligence (gf), a statistical approximation of the general factor. Using standardized cognitive tests and fMRI of mnemonic tasks, we demonstrate a positive correlation specifically between gf and map-like relational encoding of task information. This relationship was neither present for non-relational mnemonic responses in the hippocampus nor for less g-related factors. These findings offer first empirical support for a link between neural mechanisms related to relational reasoning and general cognitive performance and probe the presumed relevance of hippocampal coding properties for cognition more broadly. | | 12:48p |
Plasticity of visual looming response reveals a dissociation of innate and learned components
Animals rely on both innate and learned behaviour to respond optimally to their environment. However, little is known about how the brain may reconcile the ability to produce hardwired responses essential to survival while still allowing for adaptive flexibility. Here, we demonstrate that innate looming stimulus responses, an innate predator-evasion behaviour, can be robustly extinguished via repeated unreinforced presentation over several days. We report that this extinction is long-lasting and generalises to other contexts, but can be rapidly recovered via the pairing of the visual stimulus with an aversive electric foot-shock stimulus. Moreover, fiber photometric recordings reveal that this behavioural paradigm results in the attenuation of SC and PAG physiological responses to visual looming stimuli, and that these responses do not recover following recovery of behavioural responses. An analysis of c-Fos expression patterns throughout the midbrain and hippocampus uncovered a ventral CA1 (vCA1) ensemble that is active during both innate and learned visual looming fear responses. We investigate the functional significance of this vCA1 ensemble and report that, while its activity is not necessary for innate defensive behaviour, it is necessary for learned fear responses. Together, these findings reveal a novel role of the hippocampus in enabling adaptive behavioural responses to the innately threatening visual looming stimulus which acts in complement with innate circuitry of the SC and PAG. | | 10:22p |
Human Slip Control: Investigating the Role of Hand Acceleration Modulation in Preventing Slips
Ensuring a stable grasp during manipulative movements is crucial for robotic applications. While grip force has been the primary means of slip control, our human study revealed that trajectory modulation is also an effective slip control policy during pick-and-place tasks. Motivated by these findings, we developed and compared a slip control policy based on trajectory modulation to one based on grip force control for robotic pick-and-place tasks. Our results show that trajectory modulation significantly outperforms grip force control in certain scenarios, highlighting its potential for slip control in robotics. Moreover, we demonstrate the importance of incorporating forward models in developing effective trajectory modulation slip control systems. Overall, our study provides insight into an alternative method for slip control and suggests that humans use trajectory modulation as an alternative to grip force control for slippage prevention. This insight helps design algorithm for improving robotic manipulation tasks to prevent slippage. | | 10:22p |
Motor unit mechanisms of speed control in mouse locomotion
During locomotion, the coordinated activity of dozens of muscles shapes the kinematic features of each stride, including systematic changes in limb movement across walking speed. Motor units, each of which consists of a single motor neuron and the muscle fibers it innervates, contribute to the total activation of each muscle through their recruitment and firing rate when active. However, it remains unknown how the nervous system controls locomotor speed by changing the firing of individual motor units. To address this, we combined quantitative behavioral analysis of mouse locomotion with single motor unit recordings from the lateral and long heads of the triceps brachii, which drive monoarticular extension of the elbow and biarticular movements of the elbow and shoulder, respectively. In contrast to prior studies employing bulk EMG to examine muscle activity, our recordings revealed the diversity of spike patterning across motor units as well as systematic differences in motor unit activity across muscles and locomotor speeds. First, motor unit activity differed significantly across the lateral and long heads, suggesting differential control of these two closely apposed elbow extensor muscles. Second, we found that individual units were recruited probabilistically during only a subset of strides, showing that bulk EMG signals consistently present in every stride in fact reflect stochastically varying subsets of individual motor units. Finally, although recruitment probability and firing rate both increased at faster walking speeds, increases in recruitment were proportionally larger than rate changes, and recruitment of individual units accompanied changes in limb kinematics. Together, these results reveal how the firing of individual motor units varies systematically across muscles and walking speeds to produce flexible locomotor behavior. | | 10:22p |
Neural mechanisms of attention, not expectation, govern spatial selection by probabilistic cueing
Spatial probabilistic "Posner" cueing is widely employed in studies of endogenous spatial attention. Such cueing guides attention by providing prior knowledge about the likely spatial location of task-relevant events. Yet, it has been compellingly argued that such spatial priors also elicit expectation effects, rendering Posner cueing unsuitable for measuring attentional effects in isolation. We address this debate by combining signal detection theory models of behavior with concurrent electrophysiological recordings, and directly compare Posner cueing effects with those of attention and expectation cueing. Participants performed two tasks: a dual cueing task, with orthogonal relevance (attention) and probability (expectation) cues, as well as a Posner cueing task. Relevance and probability cueing independently modulated distinct behavioral parameters - perceptual sensitivity and decisional criterion, respectively - whereas Posner cueing modulated both sensitivity and criterion. However, only sensitivity modulations by Posner cueing were correlated with those of relevance cueing. Criterion modulations by Posner cueing were uncorrelated with those of probability cueing. Both Posner and relevance cueing, but not probability cueing, modulated various neural markers of spatial attention, including steady-state visually evoked potential (SSVEP) amplitude and alpha-band (8-12 Hz) oscillation power. Representational similarity analysis and cue-label prediction with deep convolutional neural networks revealed dissociable underpinnings of relevance and probability cueing, and identified Posner cueings neural representations with those of relevance cueing. Our results address a long-standing debate in the attention literature and clearly demonstrate that spatial selection by probabilistic cueing is governed by neural mechanisms of attention, not expectation.
Significance StatementHow does our brain select relevant information for accomplishing task goals? "Posner cueing" is a popular choice for studying brain mechanisms of goal-driven attention in the lab. Posner cues guide attention by providing advance information about the likely location of task-relevant events. Yet, Posner cueing has been criticized because it engages brain mechanisms not only of attention but of event expectation, as well. We address this critique by employing state-of-the-art neural decoding approaches, including deep convolutional neural networks, and show that behavioral and neural underpinnings of Posner cueing closely match those of attention, not expectation. The results address a long-standing debate in the attention literature and validate Posner cueing as a reliable method for measuring attentions effects in the brain. |
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