bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beta spectral power during sleep is associated with impaired recall of extinguished fear
Authors: Denis Dan , Bottary Ryan , Cunningham Tony J. , Drummond Sean P.A. , Straus Laura D. 
Published: September 2022

AbstractThe failure to retain memory for extinguished fear plays a major role in the maintenance of post-traumatic stress disorder (PTSD), with successful extinction recall necessary for symptom reduction. Disturbed sleep, a hallmark symptom of PTSD, impairs fear extinction recall. However, our understanding of the electrophysiological mechanisms underpinning sleep’s role in extinction retention remain underdetermined. We examined the relationship between the microarchitecture of sleep and extinction recall in healthy humans (n=46, both male and females included) and a pilot study in individuals with PTSD (n=12). Participants underwent a fear conditioning and extinction protocol over two days, with sleep recording occurring between conditioning and extinction. Twenty-four hours after extinction learning, participants underwent extinction recall. Power spectral density (PSD) was computed for pre- and post-extinction learning sleep. Increased beta band PSD (∼17-26Hz) during pre-extinction learning sleep was associated with worse extinction recall in healthy participants (r=.41,p=.004). Beta PSD was highly stable across three nights of sleep (intraclass correlations (ICC)>0.92). Individuals with PTSD were found to have increased beta PSD compared to healthy controls (ps < .039), and beta PSD correlated with extinction recall in the PTSD group at a similar magnitude to controls (r=.39). Results suggest beta band PSD is elevated in PTSD, and is specifically implicated in difficulties recalling extinguished fear.Significance statementDisturbed sleep is a hallmark feature of posttraumatic stress disorder (PTSD). Certain neural oscillations that occur during sleep have been shown to be altered in PTSD. These include increased 15-30Hz beta oscillations, which are believed to index cortical hyperarousal. Alongside sleep disturbances, patients also show difficulty in recalling extinguished fear. Although prior studies have linked sleep with extinction retention, no studies have investigated the role that neural oscillations during sleep play in this process. Here, we show in both healthy participants and PTSD patients, that increased beta oscillatory power during sleep is associated with impaired extinction retention. Therefore, this study provides new evidence that electrophysiological changes in the sleep EEG of PTSD patients is also implicated in extinction recall processes.
The BigMac dataset: an open resource combining multi-contrast MRI and microscopy in the macaque brain
Authors: Howard Amy FD , Huszar Istvan N , Smart Adele , Cottaar Michiel , Daubney Greg , Hanayik Taylor , Khrapitchev Alexandre A , Mars Rogier B , Mollink Jeroen , Scott Connor , Sibson Nicola R , Sallet Jerome , Jbabdi Saad , Miller Karla L 
Published: September 2022

AbstractUnderstanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.
Amplicon structure creates collateral therapeutic vulnerability in cancer
Authors: Bei Yi , Bramé Luca , Kirchner Marieluise , Fritsche-Guenther Raphaela , Kunz Sevrine , Bhattacharya Animesh , Köppke Julia , Proba Jutta , Wittstruck Nadine , Sidorova Olga A. , González Rocío Chamorro , Garcia Heathcliff Dorado , Brückner Lotte , Xu Robin , Giurgiu Mădălina , Rodriguez-Fos Elias , Koche Richard , Schmitt Clemens , Schulte Johannes H. , Eggert Angelika , Haase Kerstin , Kirwan Jennifer , Hagemann Anja I.H. , Mertins Philipp , Dörr Jan R. , Henssen Anton G. 
Published: September 2022

SummaryAlthough DNA amplifications in cancers frequently harbor passenger genes alongside oncogenes, the functional consequence of such co-amplifications and their impact for therapy remains ill-defined. We discovered that passenger co-amplifications can create amplicon structure-specific collateral vulnerabilities. We present the DEAD-box helicase 1 (DDX1) gene as a bona fide passenger co-amplified with MYCN in cancers. Survival of cancer cells with DDX1 co-amplifications strongly depends on the mammalian target of rapamycin complex 1 (mTORC1). Mechanistically, aberrant DDX1 expression inhibits the tricarboxylic acid cycle through a previously unrecognized interaction with dihydrolipoamide S-succinyltransferase, a component of the alpha-ketoglutarate dehydrogenase complex. Cells expressing aberrant DDX1 levels compensate for the metabolic shift by enhancing mTORC1 activity. Consequently, pharmacological mTORC1 inhibition triggered cell death specifically in cells harboring the DDX1 co-amplification. This work highlights a significant contribution of passenger gene alterations to the therapeutic susceptibility of cancers.Graphical abstract
SPUMONI 2: Improved pangenome classification using a compressed index of minimizer digests
Authors: Ahmed Omar , Rossi Massimiliano , Gagie Travis , Boucher Christina , Langmead Ben 
Published: September 2022

AbstractGenomics analyses often use a large sequence collection as a reference, like a pangenome or taxonomic database. We previously described SPUMONI, which performs binary classification of nanopore reads using pangenomic matching statistics. Here we describe SPUMONI 2, an improved version that is faster, more memory efficient, works effectively for both short and long reads, and can solve multi-class classification problems with the aid of a novel sampled document array structure. By incorporating minimizers, SPUMONI 2 reduces index size by a factor of 2 compared to SPUMONI, yielding an index more than 65 times smaller than minimap2’s for a mock community pangenome. SPUMONI 2 also achieves a speed improvement of 3-fold compared to SPUMONI and 15-fold compared to minimap2. We show SPUMONI 2 achieves an advantageous mix of accuracy and efficiency for short and long reads, including in an adaptive sampling scenario. We further demonstrate that SPUMONI 2 can detect contaminated contigs in genome assemblies, and can perform multi-class metagenomic read classification.
Associations between methylation age and brain age in late adolescence
Authors: Sanders Faye , Baltramonaityte Vilte , Donohoe Gary , Davies Neil M , Dunn Erin C. , Cecil Charlotte A.M. , Walton Esther 
Published: September 2022

AbstractRecent research suggests that biological age, based on DNA methylation or neuroimaging measures, may predict health traits in adulthood more accurately than chronological age. However, whether these findings apply to earlier stages in life is unknown. We therefore aimed to characterise the performance of and interdependence between measures of biological age during adolescence, leveraging longitudinal data from a subsample of young adolescents from the population-based ALSPAC cohort (n=386).We derived four methylation age measures in late adolescence (17-19 years) and a measure of brain age derived from structural neuroimaging data (18-24 years). We then examined associations between these measures of biological age, and their relationship with five measures of physical, cognitive and mental health (8-18 years).Brain age was largely independent of different measures of methylation age, after accounting for age, cell type composition, array and study (beta range: -0.60 to 0.60, all p>0.05). Smoking and BMI were associated with three measures of advanced methylation age (beta range: -0.35 to 0.52, all p<0.05), but not brain age. Depressive symptoms and cognitive ability were unrelated to all measures of biological age.Our findings emphasize the variability of and independence between these methylation- and brain-based measures of age in adolescents. They also highlight the importance of tracking the mosaic of ageing in younger populations.
Activity in primate visual cortex is minimally driven by spontaneous movements
Authors: Talluri Bharath C. , Kang Incheol , Lazere Adam , Quinn Katrina R. , Kaliss Nicholas , Yates Jacob L. , Butts Daniel A. , Nienborg Hendrikje 
Published: September 2022

AbstractOrganisms process sensory information in the context of their own moving bodies, an idea referred to as embodiment. This idea is important for developmental neuroscience, and increasingly plays a role in robotics and systems neuroscience. The mechanisms that support such embodiment are unknown, but a manifestation could be the observation in mice of brain-wide neuromodulation, including in the primary visual cortex, driven by task-irrelevant spontaneous body movements. Here we tested this hypothesis in macaque monkeys, a primate model for human vision, by simultaneously recording visual cortex activity and facial and body movements. Activity in the visual cortex (V1, V2, V3/V3A) was associated with the animals’ own movements, but this modulation was largely explained by the impact of the movements on the retinal image. These results suggest that embodiment in primate vision may be realized by input provided by the eyes themselves.
Resting fMRI-guided TMS evokes subgenual anterior cingulate response in depression
Authors: Duprat Romain J. , Linn Kristin A. , Satterthwaite Theodore D. , Sheline Yvette I. , Liang Ximo , Bagdon Gabriela , Flounders Matthew W. , Robinson Heather , Platt Michael , Kable Joseph , Long Hannah , Scully Morgan , Deluisi Joseph A. , Thase Michael , Cristancho Mario , Reber Justin , Shinohara Russell T. , Oathes Desmond J. 
Published: September 2022

ABSTRACTDepression alleviation following treatment with repetitive transcranial magnetic stimulation (rTMS) tends to be more effective when TMS is targeted to cortical areas with high resting state functional connectivity (rsFC) with the subgenual anterior cingulate cortex (sgACC). However, it has not yet been confirmed that rsFC-guided TMS coil placement leads to TMS modulation of the sgACC. For each participant (N=115, 34 depressed patients), a peak rsFC cortical ‘hotspot’ for the sgACC and control targets were prospectively identified. Single pulses of TMS interleaved with fMRI readouts were then administered to these targets and established significant downstream fMRI BOLD responses in the sgACC. We then marked an association between TMS-evoked BOLD responses in the sgACC and rsFC between the stimulation site and sgACC. This effect was qualified by a difference between healthy and patient participants: only in depressed patients, positively connected sites of stimulation led to the strongest evoked responses in the sgACC. Our results highlight rsFC-based targeting as a viable strategy to causally modulate sgACC subcortical targets and further suggest that cortical sites with high positive rsFC to the sgACC might represent an alternative target for the treatment of depression.
Predicting microbial community compositions in wastewater treatment plants using artificial neural networks
Authors: Liu Xiaonan , Nie Yong , Wu Xiao-Lei 
Published: September 2022

AbstractActivated sludge (AS) of wastewater treatment plants (WWTP) is one of the world’s largest artificial microbial ecosystems and the microbial community of the AS system is closely related to WWTP performance. However, how to predict its community structure is still unclear. Here, we used artificial neural networks (ANN) to predict the microbial compositions of AS systems collected from WWTPs located worldwide. We demonstrated that the microbial compositions of AS systems are predictable using our approach. The predictive accuracy R21:1 of Shannon-Wiener index reached 60.42%, and the average R21:1 of ASVs appearing in at least 10% of samples (ASVs>10%) and core taxa were 35.09% and 42.99%, respectively. We also found that the predictability of ASVs>10% was significantly positively correlated with their relative abundance and occurrence frequency, but significantly negatively correlated with potential migration rate. The typical functional groups such as nitrifiers, denitrifiers, polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs), and filamentous organisms in AS systems could also be well recovered using an ANN model, with the R21:1 ranging from 32.62% to 56.81%. Furthermore, we found that industry wastewater source (IndConInf) had good predictive abilities, although its correlation with ASVs>10% in the Mantel test analysis was weak, which suggested important factors that cannot be identified using traditional methods may be highlight by the ANN model. Our results provide a better understanding of the factors affecting AS communities through the prediction of the microbial community of AS systems, which could lead to insights for improved operating parameters and control of community structure.
Successful working memory linked to theta connectivity patterns in the hippocampal-entorhinal circuit
Authors: Li Jin , Cao Dan , Yu Shan , Wang Haiyan , Imbach Lukas , Stieglitz Lennart , Sarnthein Johannes , Jiang Tianzi 
Published: September 2022

AbstractWorking memory (WM) is the ability to actively maintain information for a short time and is central to human behavior. Rodent studies have proposed that hippocampal-entorhinal communication supports WM maintenance. However, the exact neural mechanisms of this interaction in WM remains unclear in humans. To address these questions, we combined machine learning analyses with intracranial electroencephalography (iEEG) recordings from the hippocampus and the entorhinal cortex (EC) in human participants, who maintained a set of letters in their WM. We found that WM maintenance was accompanied by elevated bidirectional hippocampal-EC information exchange via the theta band (2–8 Hz) and bidirectional cross-region theta-gamma phase-amplitude coupling (PAC). Further decoding analyses showed that the unidirectional inter-regional communication, with both theta oscillations in the hippocampus modulating EC gamma activity and theta band-coordinated information flow from the hippocampus, could decode correct performance at the level of participants. Taken together, our results demonstrate that theta functional coupling in the hippocampal-EC supports the maintenance of WM information via a specific pattern of frequency and direction. This connectivity-based coding could shed light on the neural mechanisms of WM processing.SignificanceRecent studies suggest a role for the hippocampus in working memory. How does the hippocampus coordinate with other brain regions to retain working memory information? The entorhinal cortex (EC) is the main gateway for information between the hippocampus and neocortex. To delineate whether (and how) the hippocampus and the entorhinal cortex interact during working memory and whether such interaction supports successful working memory, we used machine learning analyses of human intracranial EEG recordings while patients performed working memory tasks. Our results suggest that the human hippocampal-EC circuit supports working memory and is maintained in specific connectivity patterns, with a theta band (2–8 Hz)-coordinated unidirectional influence from the hippocampus to the EC. Our findings reveal that dynamic unidirectional interactions within the hippocampal-EC circuit underlie working memory and can contribute to a mechanistic circuit understanding of working memory.
Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks
Authors: Weir Janelle Shari , Christiansen Nicholas , Sandvig Axel , Sandvig Ioanna 
Published: September 2022

AbstractNeurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.
Invariance of object detection in untrained deep neural networks
Authors: Cheon Jeonghwan , Baek Seungdae , Paik Se-Bum 
Published: September 2022

AbstractThe ability to perceive visual objects with various types of transformations, such as rotation, translation, and scaling, is crucial for consistent object recognition. In machine learning, invariant object detection for a network is often implemented by augmentation with a massive number of training images, but the mechanism of invariant object detection in biological brains — how invariance arises initially and whether it requires visual experience — remains elusive. Here, using a model neural network of the hierarchical visual pathway of the brain, we show that invariance of object detection can emerge spontaneously in the complete absence of learning. First, we found that units selective to a particular object class arise in randomly initialized networks even before visual training. Intriguingly, these units show robust tuning to images of each object class under a wide range of image transformation types, such as viewpoint rotation. We confirmed that this “innate” invariance of object selectivity enables untrained networks to perform an object-detection task robustly, even with images that have been significantly modulated. Our computational model predicts that invariant object tuning originates from combinations of non-invariant units via random feedforward projections, and we confirmed that the predicted profile of feedforward projections is observed in untrained networks. Our results suggest that invariance of object detection is an innate characteristic that can emerge spontaneously in random feedforward networks.HighlightsObject-selective units spontaneously emerge in untrained deep neural networksObject selectivity maintains robustly in a wide range of image transformationsFeedforward model can explain spontaneous emergence of the invarianceInnate invariance enables invariant object detection without learning to variations
Probing visual sensitivity and attention in mice using reverse correlation
Authors: Lehnert Jonas , Cha Kuwook , Yang Kerry , Zheng Daniel F. , Khadra Anmar , Cook Erik P. , Krishnaswamy Arjun 
Published: September 2022

AbstractVisual attention is a fundamental cognitive operation that allows the brain to evoke behaviors based on the most important stimulus features. Although mouse models offer immense potential to gain a circuit-level understanding of this phenomenon, links between visual attention and behavioral decisions in mice are not well understood. Here, we describe a new behavioral task for mice that addresses this limitation. We trained mice to detect weak vertical bars in a background of checkerboard noise while audiovisual cues manipulated their spatial attention. We then modified a reverse correlation method from human studies to link behavioral decisions to stimulus locations and features. We show that mice attended to stimulus locations just rostral of their optical axis, which was highly sensitive for vertically oriented stimulus energy whose spatial frequency matched those of the weak vertical bars. We found that the tuning of sensitivity to orientation and spatial frequency grew stronger during training, was multiplicatively scaled with attention, and approached that of an ideal observer. These results provide a new task to measure spatial- and feature-based attention in mice which can be leveraged with new recording methods to uncover attentional circuits.
LIS1, a glyco-humanized swine polyclonal anti-lymphocyte globulin, as a novel induction treatment in solid organ transplantation
Authors: Rousse Juliette , Royer Pierre-Joseph , Evanno Gwenaëlle , Lheriteau Elsa , Ciron Carine , Salama Apolline , Shneiker Françoise , Duchi Roberto , Perota Andrea , Galli Cesare , Cozzi Emmanuele , Blancho Gilles , Duvaux Odile , Brouard Sophie , Soulillou Jean-Paul , Bach Jean-Marie , Vanhove Bernard 
Published: September 2022

ABSTRACTAnti-thymocyte/lymphocyte globulins (ATGs/ALGs) are immunosuppressive drugs used in induction therapies to prevent acute rejection in solid organ transplantation. Because of animal origin, ATGs/ALGs contain highly immunogenic carbohydrate xenoantigens eliciting antibodies that are associated with subclinical inflammatory events possibly impacting long-term graft survival. Their strong and long-lasting lymphodepleting activity also increases the risk for infections. To circumvent these drawbacks, LIS1 has been engineered as a glyco-humanized polyclonal antibody obtained by immunizing genetically modified pigs knocked out for major xeno-antigens αGal and Neu5Gc. It is Fc-silenced in humans and differs from other ATGs/ALGs by its mechanism of action excluding antibody-dependent cell-mediated cytotoxicity and being restricted to complement and phagocyte-mediated cytotoxicity, apoptosis and antigen masking, resulting in profound inhibition of T-cell alloreactivity in mixed-leucocyte-reactions. Preclinical evaluation in macaques showed that LIS1 impacted CD4+, CD8+ effector T cells but not T-reg, B cells or myeloid cells. Compared with rabbit ATG, LIS1 induced transient depletion of target T cells in the peripheral blood but was equivalent in preventing allograft rejection in a skin allograft model. The novel therapeutic modality of LIS1 might present advantages in induction treatment after organ transplantation by shortening the T-cell depletion period while maintaining adequate immunosuppression and reducing immunogenicity.