bioRxiv Subject Collection: Neuroscience's Journal
[Most Recent Entries]
[Calendar View]
Thursday, June 20th, 2024
| Time |
Event |
| 2:45a |
Multimodal Precision Neuroimaging of the Individual Human Brain at Ultra-high fields
Multimodal neuroimaging allows for non-invasive examination of human brain structure and function across multiple scales. Precision neuroimaging builds upon this foundation, enabling the mapping of brain structure, function, and connectivity patterns with high fidelity in single individuals. Ultra-high field (UHF) neuroimaging, operating at magnetic field strengths of 7 Tesla or higher, increases signal-to-noise ratio and offers even higher spatial resolution. Here, we provide a multimodal Precision Neuroimaging and Connectomics (PNI) dataset, utilizing UHF 7T magnetic resonance imaging (MRI). Ten healthy individuals underwent a comprehensive MRI protocol, including T1 relaxometry, magnetization transfer imaging, T2*-weighted imaging, diffusion MRI, and multi-state functional MRI paradigms, aggregated across three imaging sessions. Alongside anonymized raw imaging data, we release cortex-wide connectomes from different modalities across multiple parcellation scales, and supply gradients that compactly characterize spatial patterning of cortical organization. Our precision imaging dataset will advance our understanding of structure-function relationships in the individual human brain and is publicly available via the Open Science Framework ( https://osf.io/mhq3f/) and the Canadian Open Neuroscience Platform data portal ( https://portal.conp.ca). | | 3:22a |
A computational approach to understanding effort-based decision-making in depression
Background: Motivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to collect rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. Methods: Effort-based decision-making was assessed using the Apple Gathering Task, in which participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. Results: Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: an overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was driven by lower effort acceptance bias, but not altered effort or reward sensitivity. Conclusions: This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention. | | 7:52a |
Partially dissociable roles of the Orbitofrontal cortex and dorsal Hippocampus in context-dependent (hierarchical) reward predictions and contextual inference in learning
Reward cues are often ambiguous; what is good in one context is not necessarily good in another context. To solve this ambiguity, animals form hierarchical associations in which the context acts as a gatekeeper in the retrieval of the appropriate cue-evoked memory, ensuring context-appropriate behavior. These hierarchical associative structures also influence future learning by promoting the formation of new context-dependent associations (leading to the inference of context-dependency for new associations). The orbitofrontal cortex (OFC) and the dorsal hippocampus (DH) are both proposed to encode a "cognitive map" that includes the representation of hierarchical, context-dependent, associations. However the causal role of the OFC and DH in the different functional properties of hierarchical associations remains controversial. Here we used chemogenetic inactivations, in rats, to examine the role of OFC and DH in 1) the contextual regulation of performance, and 2) the contextual learning bias conferred by hierarchical associations. We show that OFC is required for both manifestations of hierarchical associations. In contrast, DH contribution appears limited to the contextual learning bias. This study provides novel insight into the different functional properties of context-dependent hierarchical associations, and establishes the OFC as a critical orchestrator of these different contextual effects. | | 7:52a |
A failure to discriminate social from non-social touch at the circuit level may underlie social avoidance in autism
Social touch is critical for communication and to impart emotions and intentions. However, certain autistic individuals experience aversion to social touch, especially when it is unwanted. We used a novel social touch assay and Neuropixels probes to compare neural responses to social vs. non-social interactions in three relevant brain regions: vibrissal somatosensory cortex, tail of striatum, and basolateral amygdala. We find that wild type (WT) mice showed aversion to repeated presentations of an inanimate object but not of another mouse. Cortical neurons cared most about touch context (social vs. object) and showed a preference for social interactions, while striatal neurons changed their preference depending on whether mice could choose or not to interact. Amygdalar and striatal neurons were preferentially modulated by forced object touch, which was the most aversive. In contrast, the Fmr1 knockout (KO) model of autism found social and non-social interactions equally aversive and displayed more aversive facial expressions to social touch when it invaded their personal space. Importantly, when Fmr1 KO mice could choose to interact, neurons in all three regions did not discriminate social valence. Thus, a failure to differentially encode social from non-social stimuli at the circuit level may underlie social avoidance in autism. | | 7:52a |
Synaptic Organization-Function Relationships of Amygdala Interneurons Supporting Associative Learning
Coordinated activity of basolateral amygdala (BLA) GABAergic interneurons (INs) and glutamatergic principal cells (PCs) is critical for associative learning, however the microcircuit organization-function relationships of distinct IN classes remain uncertain. Here, we show somatostatin (SOM) INs provide inhibition onto, and are excited by, local PCs, whereas vasoactive intestinal peptide (VIP) INs are driven by extrinsic afferents. Parvalbumin (PV) INs inhibit PCs and are activated by local and extrinsic inputs. Thus, SOM and VIP INs exhibit complementary roles in feedback and feedforward inhibition, respectively, while PV INs contribute to both microcircuit motifs. Functionally, each IN subtype reveals unique activity patterns across fear- and extinction learning with SOM and VIP INs showing most divergent characteristics, and PV INs display an intermediate phenotype parallelling synaptic data. Finally, SOM and PV INs dynamically track behavioral state transitions across learning. These data provide insight into the synaptic microcircuit organization-function relationships of distinct BLA IN classes. | | 8:17a |
Spine loss in depression impairs dendritic signal integration in human cortical microcircuit models
Major depressive disorder (depression) is associated with altered dendritic structure and function in excitatory cortical pyramidal neurons, due to decreased inhibition from somatostatin interneurons and loss of spines and associated synapses, as indicated in postmortem human studies. Dendrites play an important role in signal processing as they receive the majority of synaptic inputs and exhibit nonlinear properties including backpropagating action potentials and dendritic Na+ spikes that enhance the computational power of the neuron. However, it is currently unclear how depression-related dendritic changes impact the integration of signals. Here, we expanded our previous data-driven detailed computational models of human cortical microcircuits in health and depression to include active dendritic properties that enable backpropagating action potentials as measured in human neurons, and spine loss in depression in terms of synapse loss and altered intrinsic property. We show that spine loss dampens signal response and thus results in a larger impairment of cortical function such as signal detection than due to reduced somatostatin interneuron inhibition alone. We further show that the altered intrinsic properties due to spine loss abolish nonlinear dendritic integration of signals and impair recurrent microcircuit activity. Our study thus mechanistically links cellular changes in depression to impaired dendritic processing in human cortical microcircuits. | | 3:32p |
Feedback Control of Neuronal Excitability and Epileptiform Bursting using a Photocaged Adenosine A1 Agonist
Adenosine is a potent regulator of neurotransmission and neuronal excitability through activation of Gi protein-coupled adenosine A1 receptors (A1Rs). It has gained interest as a potential anticonvulsant due to its endogenous involvement in ending ongoing seizure activity. A recently developed coumarin-caged derivative of the A1R agonist N6-cyclopentyl-adenosine (CPA), cCPA, was used for photo-uncaging of CPA with millisecond flashes of 405 nm light. At population level, CPA reduces Schaffer Collateral stimulated extracellular dendritic field potentials (FPs) in the CA1 region of the hippocampus with an ED50 of 44.1{+/-}2.8 nM and a Hill coefficient of 3. Response onset is CPA dependent and takes less than seconds, while recovery is CPA independent with a time constant of around 20 minutes. A closed-loop feedback system used the amplitude of evoked dendritic FPs to photorelease CPA and was able to control FP amplitude to user defined levels between 10% and 90% of baseline level. In the acute elevated potassium model of epilepsy raising extracellular K+ to 8.5 mM enhances neuronal excitability and induces regularly occurring epileptiform bursts, but FPs evoked with low intensity could still continuously monitor excitability without interfering with bursting. In this model the closed-loop system that controlled CPA release, was able to suppress epileptiform bursting, while maintaining an acceptable level of functional neurotransmission. Including in the control algorithm a second parameter that combined population spike amplitude and number of population spikes, enabled the system to automatically find a level of functional neurotransmission that was just below the threshold for multiple spiking and epileptiform bursting. The combination of photopharmacological adenosinergic modulation with real-time FP monitoring provides a first step towards closed-loop precision treatment for diseases related to neuronal hyperexcitability such as epilepsy. | | 5:36p |
An Open-Source Deep Learning-Based GUI Toolbox For Automated Auditory Brainstem Response Analyses (ABRA)
In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA. |
|