Top 10 Biorxiv Papers Today in Neuroscience


2.042 Mikeys
#1. Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types
Claire Bomkamp, Shreejoy Tripathy, Carolina Bengtsson Gonzales, Jens Hjerling Leffler, Ann Marie Craig, Paul Pavlidis
In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were "class-driven," meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from Patch-seq experiments, allowing simultaneous...
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biorxivpreprint: Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types https://t.co/iGgYrYV2po #bioRxiv
biorxiv_neursci: Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types https://t.co/FSND7nqSay #biorxiv_neursci
CyrilPedia: 'In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.' https://t.co/DMShh4W4Eg
Claire_Bomkamp: Hey so @neuronJoy and I and some other people did a thing? We got to play with a couple of really cool (and publicly accessible!) datasets from @AllenInstitute https://t.co/tSFkQiCe0o
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Authors: 6
Total Words: 18502
Unqiue Words: 4714

2.036 Mikeys
#2. Individual differences in the effects of priors on perception: a multi-paradigm approach
Kadi Tulver, Jaan Aru, Renate Rutiku, Talis Bachmann
The present study investigated individual differences in how much subjects rely on prior information, such as expectations or knowledge, when faced with perceptual ambiguity. The behavioural performance of forty-four participants was measured on four different visual paradigms (Mooney face recognition, illusory contours, blur detection and representational momentum) in which priors have been shown to affect perception. In addition, questionnaires were used to measure autistic and schizotypal traits in the non-clinical population. We hypothesized that someone who in the face of ambiguous or noisy perceptual input relies heavily on priors, would exhibit this tendency across a variety of tasks. This general pattern would then be reflected in high pairwise correlations between the behavioural measures and an emerging common factor. On the contrary, our results imply that there is no single factor that explains the individual differences present in the aforementioned tasks, as further evidenced by the overall lack of robust...
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biorxivpreprint: Individual differences in the effects of priors on perception: a multi-paradigm approach https://t.co/aFhn3G69me #bioRxiv
biorxiv_neursci: Individual differences in the effects of priors on perception: a multi-paradigm approach https://t.co/Nv1btCyOzx #biorxiv_neursci
PavelProsselkov: "...the effects of priors likely originate from several independent sources and it is important to consider the role of specific tasks and stimuli more carefully when reporting effects of priors on perception" https://t.co/AaIF92kZyN
jaaanaru: Which priors do you mean? We found that there is no general factor of "relying on priors" in different visual tasks: priors depend on the task. Hence, making general claims about a particular (patient) population having strong/weak priors seems unwarranted https://t.co/iP0JZmGWfg
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Sample Sizes : [44]
Authors: 4
Total Words: 11631
Unqiue Words: 3138

2.026 Mikeys
#3. Ontological Dimensions of Cognitive-Neural Mappings
Taylor Bolt, Jason S. Nomi, Rachel Arens, Shruti G. Vij, Michael Riedel, Taylor Salo, Angela R. Laird, Simon B. Eickhoff, Lucina Q. Uddin
The growing literature reporting results of cognitive-neural mappings has increased calls for an adequate organizing ontology, or taxonomy, of these mappings. This enterprise is non-trivial, as relevant dimensions that might contribute to such an ontology are not yet agreed upon. We propose that any candidate dimensions should be evaluated on their ability to explain observed differences in functional neuroimaging activation patterns. In this study, we use a large sample of task-based functional magnetic resonance imaging (task-fMRI) results and a data-driven strategy to identify these dimensions. First, using a data-driven dimension reduction approach and multivariate distance matrix regression (MDMR), we quantify the variance among activation maps that is explained by existing ontological dimensions. We find that 'task paradigm' categories explain the most variance among task-activation maps than other dimensions, including latent cognitive categories. Surprisingly, 'study ID', or the study from which each activation map was...
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LucinaUddin: Another BCCL collaboration, brought to you by Dr. Taylor Bolt, with @GopalVij_Neuro, @phyzang, @INM7_ISN and others: Ontological Dimensions of Cognitive-Neural Mappings - https://t.co/zEjNcQl1co
danjlurie: RT @biorxiv_neursci: Ontological Dimensions of Cognitive-Neural Mappings https://t.co/K1A6YfY7gI #biorxiv_neursci
branka_mvojevic: RT @biorxiv_neursci: Ontological Dimensions of Cognitive-Neural Mappings https://t.co/K1A6YfY7gI #biorxiv_neursci
RJ_SanDiego: RT @dixy0: Ontological Dimensions of Cognitive-Neural Mappings https://t.co/6jkhFVhvAg @LucinaUddin
HaiyangGeng: RT @dixy0: Ontological Dimensions of Cognitive-Neural Mappings https://t.co/6jkhFVhvAg @LucinaUddin
Github

NMF and Clustering of BrainMap Database

Repository: BrainMap-Cognitive-Ontology
User: tsb46
Language: MATLAB
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Total Words: 8075
Unqiue Words: 2389

2.023 Mikeys
#4. Domain-specific working memory, but not dopamine-related genetic variability, shapes reward-based motor learning
Olivier Codol, Peter J Holland, Elizabeth Oxley, Maddison Taylor, Elizabeth Hamshere, Shadiq Joseph, Laura Huffer, Joseph M Galea
The addition of rewarding feedback to motor learning tasks has been shown to increase the retention of learning, spurring interest in the possible utility for rehabilitation. However, laboratory-based motor tasks employing rewarding feedback have repeatedly been shown to lead to great inter-individual variability in performance. Understanding the causes of such variability is vital for maximising the potential benefits of reward-based motor learning. Thus, in this pre-registered study, we assessed whether spatial (SWM), verbal (VWM) and mental rotation (RWM) working memory capacity as well as dopamine-related genetic profiles could predict performance in two reward-based motor tasks, using a large cohort of participants (N=241). The first task assessed participant's ability to follow a hidden and slowly shifting reward region based on hit/miss (binary) feedback. The second task investigated participant's capacity to preserve performance with binary feedback after adapting to the shift with full visual feedback. Our results...
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PavelProsselkov: "...no dopamine-related genotypes predicted performance...working memory capacity plays a pivotal in determining individual ability in reward-based motor learning" https://t.co/rnGCYzcwSj
cornu_copiae: Incentivized motor tasks produce large inter-individual variability in performance, but what is causing this variability? A large pre-reg study suggests it's primarily due to domain-specific working memory, not dopamine-related genes. @OlivierCodol et al. https://t.co/bvQusb2u6L https://t.co/1XqzAykvl1
OlivierCodol: Our latest work on reward-based motor learning. What drives good individual performance in these tasks? Domain-specific working memory, apparently. @P_J_Holland @GaleaLab https://t.co/sQfVbbZaj0
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Sample Sizes : [241, 121, 30, 121, 82, 120, 85]
Authors: 8
Total Words: 12479
Unqiue Words: 3737

2.021 Mikeys
#5. Evolving super stimuli for real neurons using deep generative networks
Carlos R. Ponce, Will Xiao, Peter Schade, Till S Hartmann, Gabriel Kreiman, Margaret S Livingstone
Finding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.
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neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
PromPreprint: Evolving super stimuli for real neurons using deep generative networks https://t.co/I6SsTQ8R8i
peterfschade: New pre-print on #bioRxiv 'Evolving super stimuli for real neurons using deep generative networks'. With @HombreCerebro, @WillXiao1, @Till_S_Hartmann, @gkreiman, and @mlivingstonehms https://t.co/OZMhiPllK1 https://t.co/iTaI4nBEcE
whatnowning: Evolving super stimuli for real neurons using deep generative networks https://t.co/3xYemltpdk
neuromusic: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
KaiLashArul: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
evolvingstuff: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
andronovhopf: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
allmeasures: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
Lingzhong_Fan: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
a_tschantz: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
balicea1: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
indy9000: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
AIIA_ngn: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
MushiKachi: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
AntonioLozanoDL: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
tim_sainburg: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
NusWu: RT @neuroecology: Evolving super stimuli for real neurons using deep generative networks https://t.co/4DYygsbHZN https://t.co/v3mjFUHCHI
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Total Words: 9626
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2.021 Mikeys
#6. RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling
Patricia M. Garay, Alex Chen, Takao Tsukahara, Rafi Kohen, J. Christian Althaus, Margarete A. Wallner, Roman J. Giger, Michael A. Sutton, Shigeki Iwase
Long-lasting forms of synaptic plasticity such as synaptic scaling are critically dependent on transcription. Activity-dependent transcriptional dynamics in neurons, however, have not been fully characterized, because most previous efforts relied on measurement of steady-state mRNAs. Here, we profiled transcriptional dynamics of primary neuron cultures undergoing network activity shifts using nascent RNA sequencing. We found pervasive transcriptional changes, in which ~45% of expressed genes respond to network activity shifts. Notably, the majority of these genes respond to increases or decreases of network activity uniquely, rather than reciprocally. We further linked the chromatin regulator Retinoic acid induced 1 (RAI1), the Smith-Magenis Syndrome gene, to the specific transcriptional program driven by reduced network activity. Finally, we show that RAI1 is essential for homeostatic synaptic upscaling but not downscaling. These results demonstrate the utility of bona fide transcription profiling to discover mechanisms of...
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biorxivpreprint: RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling https://t.co/VUh9uRxSEI #bioRxiv
biorxiv_neursci: RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling https://t.co/VI1TfUiqgB #biorxiv_neursci
brainchromatin: Preprint of ⁦⁦@TriciaGaray⁩ ‘s first research manuscript. RAI1, the Smith-Magenis syndrome gene, is selectively required for transcriptional program of reduced network activity. Congrats Tricia! https://t.co/d9WCJfZyxG
LeightonHDuncan: RT @biorxiv_neursci: RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling https://t.co/VI1TfUiqgB #biorxiv_neursci
AlexC924: RT @biorxiv_neursci: RAI1 Regulates Activity-Dependent Nascent Transcription and Synaptic Scaling https://t.co/VI1TfUiqgB #biorxiv_neursci
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Authors: 9
Total Words: 16602
Unqiue Words: 5315

2.015 Mikeys
#7. Thalamus drives two complementary input strata of the neocortex in parallel
Robert Egger, Rajeevan T Narayanan, Daniel Udvary, Arco Bast, Jason M Guest, Suman Das, Christiaan P J de Kock, Marcel Oberlaender
Sensory information enters the neocortex via thalamocortical axons that define the major 'input' layer 4. The same thalamocortical axons, however, additionally innervate the deep 'output' layers 5/6. How such bistratification impacts cortical processing remains unknown. Here, we find a class of neurons that cluster specifically around thalamocortical axons at the layer 5/6 border. We show that these border stratum cells are characterized by extensive horizontal axons, that they receive strong convergent input from the thalamus, and that this input is sufficient to drive reliable sensory-evoked responses, which precede those in layer 4. These cells are hence strategically placed to amplify and relay thalamocortical inputs across the cortical area, for example to drive the fast onsets of cortical output patterns. Layer 4 is therefore not the sole starting point of cortical processing. Instead, parallel activation of layer 4 and the border stratum is necessary to broadcast information out of the neocortex.
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biorxivpreprint: Thalamus drives two complementary input strata of the neocortex in parallel https://t.co/nIzHzCvJkY #bioRxiv
biorxiv_neursci: Thalamus drives two complementary input strata of the neocortex in parallel https://t.co/duPc7j9bhm #biorxiv_neursci
NeuroSyntheSys: RT @biorxiv_neursci: Thalamus drives two complementary input strata of the neocortex in parallel https://t.co/duPc7j9bhm #biorxiv_neursci
JasonMGuest1: Thalamus drives two complementary input strata of the neocortex in parallel https://t.co/MptKoK7K2S
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Total Words: 17746
Unqiue Words: 5102

2.012 Mikeys
#8. Direct binding of the flexible C-terminal segment of periaxin to β4 integrin suggests a molecular basis for CMT4F
Arne Raasakka, Helen Linxweiler, Peter J Brophy, Diane L Sherman, Petri Kursula
The process of myelination in the nervous system requires coordinated formation of both transient and stable supramolecular complexes. Myelin-specific proteins play key roles in these assemblies, which may link membranes to each other or connect the myelinating cell cytoskeleton to the extracellular matrix. The myelin protein periaxin is known to play an important role in linking the Schwann cell cytoskeleton to the basal lamina through membrane receptors, such as the dystroglycan complex. Mutation that truncate periaxin from the C terminus cause demyelinating peripheral neuropathy, Charcot-Marie-Tooth disease type 4F, indicating a function for the periaxin C-terminal region in myelination. We identified the cytoplasmic domain of β4 integrin as a specific high-affinity binding partner for periaxin. The C-terminal region of periaxin remains unfolded and flexible when bound to the third fibronectin type III domain of β4 integrin. Our data suggest that periaxin is able to link the Schwann cell cytoplasm to the basal lamina through a...
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petrikursula: Molecular basis for CMT4F https://t.co/d7Coc4trPm @UiB @UniOulu @esrfsynchrotron @desy @DiamondLightSou #cmt #myelin #neuropathy
Alanacowell: RT @petrikursula: Molecular basis for CMT4F https://t.co/d7Coc4trPm @UiB @UniOulu @esrfsynchrotron @desy @DiamondLightSou #cmt #myelin #neu…
PeredaLab: RT @petrikursula: Molecular basis for CMT4F https://t.co/d7Coc4trPm @UiB @UniOulu @esrfsynchrotron @desy @DiamondLightSou #cmt #myelin #neu…
Jose_A_Manso: RT @petrikursula: Molecular basis for CMT4F https://t.co/d7Coc4trPm @UiB @UniOulu @esrfsynchrotron @desy @DiamondLightSou #cmt #myelin #neu…
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Authors: 5
Total Words: 12317
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2.01 Mikeys
#9. Distinct genetic signatures of cortical and subcortical regions associated with human memory
Pin Kwang Tan, Egor Ananyev, Po-Jang (Brown) Hsieh
Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of human memory remains a challenge. Here, we devised a framework combining human transcriptome data and a functional neuroimaging map to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions. Results were validated with animal literature and our framework proved to be highly effective and specific to the targeted cognitive function versus a control function. Genes preferentially expressed in cortical memory regions are linked to associative learning and ribosome biogenesis. Genes expressed in subcortical memory regions are associated with synaptic signaling and epigenetic processes. Cortical and subcortical regions share a number of memory-related biological processes and genes, e.g. translational initiation and GRIN1. Thus, cortical and subcortical memory regions exhibit distinct genetic signatures that potentially reflect functional differences in health and disease, and propose...
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biorxivpreprint: Distinct genetic signatures of cortical and subcortical regions associated with human memory https://t.co/tJCecfCKw0 #bioRxiv
biorxiv_neursci: Distinct genetic signatures of cortical and subcortical regions associated with human memory https://t.co/kaqyiynm7b #biorxiv_neursci
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Total Words: 13404
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2.01 Mikeys
#10. The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies
Valeria Bonapersona, Jiska Kentrop, Caspar J Van Lissa, Rixt van der Veen, Marian Joels, Ratna Angela Sarabdjitsingh
Background: Altered cognitive performance has been suggested as an intermediate phenotype mediating the effects of early life adversity (ELA) on later-life development of mental disorders, e.g. depression. Whereas most human studies are limited to correlational conclusions, rodent studies can prospectively investigate how ELA alters cognitive performance in a number of domains. Despite the vast volume of reports, no consensus has yet been reached on the i) behavioral domains being affected by ELA and ii) the extent of these effects. Methods: To test how ELA (here: aberrant maternal care) affects specific behavioral domains, we used a 3-level mixed-effect meta-analysis, a flexible model that accounts for the dependency of observations. We thoroughly explored heterogeneity with MetaForest, a machine-learning data-driven analysis never applied before in preclinical literature. We validated the robustness of our findings with substantial sensitivity analyses and bias assessments. Results: Our results, based on > 400 independent...
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VBonapersona: Those 200-something articles I read show clear behavioral phenotype of early life adversity in rodents that resembles those of humans (https://t.co/RMVc6H6sBW) - comes with an app to perform your own meta-analysis https://t.co/rIIb9Erhud #myfirstTweet #metaanalysis https://t.co/cpvJCiPRdn
jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
namikkirlic: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/AcapXoOCGe
EvidenceRobot: RT @namikkirlic: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/AcapXoOCGe
EvidenceRobot: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
NicoleNugentPhD: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
jessicabakerphd: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
ampolter: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
nicoleacrowley: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
CamronBryantPhD: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
PascoFearon: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
anita_thapar1: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
gard_arianna: RT @jorsmo: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/ZNL7TJHLLw
NadineP54457742: RT @biorxiv_neursci: The behavioral phenotype of early life adversity: a 3-level meta-analysis of rodent studies https://t.co/Tfw63YBiyz #…
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