bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation
Authors: Kwon Yoonsung , Blazyte Asta , Jeon Yeonsu , Kim Yeo Jin , An Kyungwhan , Jeon Sungwon , Ryu Hyojung , Shin Dong-Hyun , Ahn Jihye , Um Hyojin , Kang Younghui , Bak Hyebin , Kim Byoung-Chul , Lee Semin , Jung Hyung-Tae , Shin Eun-Seok , Bhak Jong 
Published: May 2024

AbstractBackgroundThe changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify the methylation-based biomarkers for anxiety disorders and subsequently validate their reliability.MethodsA comparative differential methylation analysis was performed on whole blood samples from 94 anxiety disorder patients and 296 control samples using targeted bisulfite sequencing. Subsequent validation of identified biomarkers employed an artificial intelligence- based risk prediction models: a linear calculation-based methylation risk score model and two tree-based machine learning models: Random Forest and XGBoost.Results17 novel epigenetic methylation biomarkers were identified to be associated with anxiety disorders. These biomarkers were predominantly localized near CpG islands, and they were associated with two distinct biological processes: 1) cell apoptosis and mitochondrial dysfunction and 2) the regulation of neurosignaling. We further developed a robust diagnostic risk prediction system to classify anxiety disorders from healthy controls using the 17 biomarkers. Machine learning validation confirmed the robustness of our biomarker set, with XGBoost as the best-performing algorithm, an area under the curve of 0.876.ConclusionOur findings support the potential of blood liquid biopsy in enhancing the clinical utility of anxiety disorder diagnostics. This unique set of epigenetic biomarkers holds the potential for early diagnosis, prediction of treatment efficacy, continuous monitoring, health screening, and the delivery of personalized therapeutic interventions for individuals affected by anxiety disorders.
A tale of two n-backs: Diverging associations of dorsolateral prefrontal cortex activation with n-back task performance
Authors: Tubiolo Philip N. , Williams John C. , Van Snellenberg Jared X. 
Published: May 2024

AbstractBackgroundIn studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation-performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain-behavior correlations using two large, publicly available datasets.MethodsWe conducted between-participants analyses of task-based fMRI data from two publicly available datasets: the Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study (n = 459). Participants performed two distinct variations of then-back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2-back − 0-back] contrast) and task performance (2-back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex.ResultsGlobal patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre-supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation-performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants.ConclusionsThe observation of diverging activation-performance relationships between two large datasets performing variations of then-back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.
Predicting and Shaping Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces
Authors: Madduri Maneeshika M. , Yamagami Momona , Li Si Jia , Burckhardt Sasha , Burden Samuel A. , Orsborn Amy L. 
Published: May 2024

AbstractNeural interfaces can restore or augment human sensorimotor capabilities by converting high-bandwidth biological signals into control signals for an external device via a decoder algorithm. Leveraging user and decoder adaptation to create co-adaptive interfaces presents opportunities to improve usability and personalize devices. However, we lack principled methods to model and optimize the complex two-learner dynamics that arise in co-adaptive interfaces. Here, we present new computational methods based on control theory and game theory to analyze and generate predictions for user-decoder co-adaptive outcomes in continuous interactions. We tested these computational methods using an experimental platform where human participants (N=14) learn to control a cursor using an adaptive myoelectric interface to track a target on a computer display. Our framework predicted the outcome of co-adaptive interface interactions and revealed how interface properties can shape user behavior. These findings contribute new tools to design personalized, closed-loop, co-adaptive neural interfaces.
Dengue NS1 interaction with lipids alters its pathogenic effects on monocyte derived macrophages
Authors: Dayarathna Shashika , Senadheera Bhagya , Jeewandara Chandima , Dissanayaka Madushika , Bary Farha , Ogg Graham S. , Malavige Gathsaurie Neelika 
Published: May 2024

AbstractBackgroundWhile dengue NS1 antigen has been shown to be associated with disease pathogenesis in some studies, it has not been linked in other studies, with the reasons remaining unclear. NS1 antigen levels in acute dengue are often associated with increased disease severity, but there have been a wide variation in results based on past dengue infection and infecting dengue virus (DENV) serotype. As NS1 engages with many host lipids, we hypothesize that the type of NS1-lipid interactions alters its pathogenicity.MethodsPrimary human monocyte derived macrophages (MDMs) were co-cultured with NS1 alone or with HDL, LDL, LPS and/or platelet activating factor (PAF) from individuals with a history of past dengue fever (DF=8) or dengue haemorrhagic fever (DHF=8). IL-1β levels were measured in culture supernatants, and gene expression analysis carried out in MDMs. Monocyte subpopulations were assessed by flow cytometry. Hierarchical cluster analysis with Euclidean distance calculations were used to differentiate clusters. Differentially expressed variables were extracted and a classifier model was developed to differentiate between past DF and DHF.ResultsSignificantly higher levels of IL-1β were seen in culture supernatants when NS1 was co-cultured with LDL (p=0.01), but with lower levels with HDL (p=0.05). MDMs of those past DHF produced more IL-1β when NS1 with PAF (p=0.02). MDMs of individuals with past DHF, were significantly more likely to down-regulateRPLP2gene expression when macrophages were co-cultured with either PAF alone, or NS1 combined with PAF, or NS1 combined with LDL. When NS1 was co-cultured with PAF, HDL or LDL two clusters were detected based onIL10expression, but these did not differentiate those with past DF or DHF.ConclusionsAs RPLP2 is important in DENV replication and in regulating cellular stress responses and immune responses and IL-10 is associated with severe disease, it would be important to further explore how differential expression of RPLP2 and IL-10 could lead to disease pathogenesis based on NS1 and lipid interactions.
Multimodal identification of the mouse brain using simultaneous Ca2+imaging and fMRI
Authors: Mandino Francesca , Horien Corey , Shen Xilin , Desrosiers-Grégoire Gabriel , Luo Wendy , Markicevic Marija , Todd Constable R. , Papademetris Xenophon , Chakravarty Mallar M. , Betzel Richard F. , Lake Evelyn M.R. 
Published: May 2024

ABSTRACTIndividual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the workhorse in this arena, BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI) suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.
Skeletal myotubes expressing ALS mutant SOD1 induce pathogenic changes, impair mitochondrial axonal transport, and trigger motoneuron death
Authors: Martínez Pablo , Silva Mónica , Abarzúa Sebastián , Tevy María Florencia , Jaimovich Enrique , Constantine-Paton Martha , Bustos Fernando J , van Zundert Brigitte 
Published: May 2024

ABSTRACTAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of motoneurons (MNs), and despite progress, there is no effective treatment. A large body of evidence shows that astrocytes expressing ALS-linked mutant proteins cause non-cell autonomous toxicity of MNs. Although MNs innervate muscle fibers and ALS is characterized by the early disruption of the neuromuscular junction (NMJ) and axon degeneration, there are controversies about whether muscle contributes to non-cell-autonomous toxicity to MNs. In this study, we generated primary skeletal myotubes from myoblasts derived from ALS mice expressing human mutant SOD1G93A(termed hereafter mutSOD1). Characterization revealed that mutSOD1 skeletal myotubes display intrinsic phenotypic and functional differences compared to control myotubes generated from non-transgenic (NTg) littermates. Next, we analyzed whether ALS myotubes exert non-cell-autonomous toxicity to MNs. We report that conditioned media from mutSOD1 myotubes (mutSOD1-MCM), but not from control myotubes (NTg-MCM), induced robust death of primary MNs in mixed spinal cord cultures and compartmentalized microfluidic chambers. Our study further revealed that applying mutSOD1-MCM to the MN axonal side in microfluidic devices rapidly reduces mitochondrial axonal transport while increasing Ca2+ transients and reactive oxygen species (i.e., H2O2). These results indicate that soluble factor(s) released by mutSOD1 myotubes cause MN axonopathy that leads to lethal pathogenic changes.
Deep learning and eye-tracking for accurate EOG rejection
Authors: Huberty Scott , O’Reilly Christian 
Published: May 2024

AbstractElectroencephalography (EEG) is a neuroimaging technique used to record the electrical activity generated by the brain. EEG recordings are often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Independent component analysis (ICA) is commonly applied to isolate EOG artifacts and subtract the corresponding independent components from the EEG signals. However, ICA is an unsupervised technique that contains no knowledge of the eye movements during the task or the generative process by which these movements result in EOG artifacts. It is generally difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. To address this limitation, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking. We leveraged theLarge Gridtask from the open-source EEGEyeNet dataset to develop and validate this approach. In this task, 30 participants looked at a series of dots appearing at 25 predetermined positions on the screen (810 trials/participant). EEG and eye-tracking were collected simultaneously. In this paper, we show that we can train a long short-term memory (LSTM) model to predict the component of EEG signals predictable from eye-tracking data. We further used this eye-tracking-informed evaluation of EOG artifacts to investigate the sensitivity and specificity of ICA, currently the dominant approach for EOG artifact correction. Our analysis indicates that although ICA is very sensitive to EOG, it has a comparatively low specificity.
Heartbeat perception is causally linked to frontal delta oscillations
Authors: Haslacher David , Reber Philipp , Cavallo Alessia , Rosenthal Annika , Pangratz Elisabeth , Beck Anne , Romanczuk-Seiferth Nina , Nikulin Vadim , Villringer Arno , Soekadar Surjo R. 
Published: May 2024

AbstractThe ability to accurately perceive one’s own bodily signals, such as the heartbeat, plays a vital role in physical and mental health. However, the neurophysiological mechanisms underlying this ability, termed interoception, are not fully understood. Converging evidence suggests that cardiac rhythms are linked to frontal brain activity, particularly oscillations in the delta (0.5 – 4 Hz) band, but their causal relationship remained elusive. Using amplitude-modulated transcranial alternating current stimulation (AM-tACS), a method to enhance or suppress brain oscillations in a phase-specific manner, we investigated whether frontal delta oscillations are causally linked to heartbeat perception. We found that enhancement of delta phase synchrony suppressed heartbeat detection accuracy, while suppression of delta phase synchrony enhanced heartbeat detection accuracy. These findings suggest that frontal delta oscillations play a critical role in heartbeat perception, paving the way for causal investigations of interoception and potential clinical applications.SignificanceAlthough bodily signals are known to influence perception and behavior, little is known about the underlying neurophysiological mechanisms. Here, we show that perception of the heartbeat is anticorrelated with phase synchrony of frontal delta oscillations, and that modulating these oscillations with transcranial electric stimulation influences heartbeat perception. Our results suggest that delta oscillations play a key role in processing bodily signals, with potential implications for theories of emotions and clinical neuroscience.
Estimating steady-state evoked potentials in the limit of short data duration and low stimulation frequency
Authors: Buiatti Marco , Saretta Davide 
Published: May 2024

AbstractBecause of their high signal-to-noise ratio and robustness to artifacts, Steady-State Evoked Potentials (SSEP) - the periodic responses elicited by periodic stimulation designs - are increasingly used in human neuroscience for measuring stimulus-specific brain responses in a short presentation time. While widely applied to measure sensory responses with stimulation frequencies higher than 8 Hz, they are also successful to investigate high-order processes and/or early development characterized by slower time scales, requiring very low stimulation frequencies around 1 Hz. However, applications of these low frequency paradigms on developmental or clinical populations, typically relying on very short data recordings, pose a methodological challenge for SSEP estimation. Here we tackled this challenge by investigating the method of analysis that most efficiently compute SSEP at low stimulation frequencies in the limit of short data, and by estimating the minimum data length necessary to obtain a reliable response. We compared the performance of the three most commonly used measures of SSEP (power spectrum (PS), evoked power spectrum (EPS) and inter-trial coherence (ITC)) at progressively shorter data segments both on simulated data and on EEG responses to on-off checkerboard stimulation at two ‘low’ frequencies (4 Hz and 0.8 Hz). Results, consistent between simulated and real data, show that while for long data length EPS and ITC outperform PS, for short data length the three measures are equivalent, and the crucial parameter is the length of the sliding window over which each measure is computed: the longer the better for PS and EPS, whereas the opposite occurs for ITC. For the analysed dataset, the shortest data length required to estimate a reliable SSEP is as short as 8 cycles of stimulation, independently from the stimulation frequency. This study provides practical indications for reliable and efficient application of low-frequency SSEP designs on short data recordings.
Younger adult brain utilizes interhemispheric strategy of recruiting ipsilateral dorsal premotor cortex for complex finger movement, but not aging brain
Authors: Miura Gen , Morita Tomoyo , Park Jihoon , Naito Eiichi 
Published: May 2024

AbstractThe ipsilateral sensorimotor cortices (dorsal premotor cortex [PMd], primary motor cortex [M1], primary somatosensory cortex [S1], and superior parietal cortex of Area 2) are often activated when the healthy younger and older adult brains perform complex finger movements. Prompted by clinical evidence that the ipsilateral PMd plays particularly important roles to complement the movements after stroke, we tested whether the ipsilateral PMd also plays particularly important (complementary) roles among the ipsilateral sensorimotor cortices when healthy younger adults perform a complex motor task by augmenting interhemispheric functional connectivity with the contralateral sensorimotor cortices. We also examined possible strategic difference when older adults with degraded interhemispheric inhibition perform the complex task when compared to younger adults with mature one.We addressed these questions by measuring brain activity with functional magnetic resonance imaging while healthy right-handed younger and older adults performed simple (button pressing with the right index finger) and complex (stick rotation requiring coordination between the right fingers) motor tasks.In younger group, the ipsilateral PMd, S1, Area 2 activated during the complex task, while the ipsilateral M1 remained deactivated as in the simple task, suggesting an existence of interregional difference. The ipsilateral PMd and S1/Area 2 more activated in the individuals with less dexterous performance. The anterior ipsilateral PMd enhanced interhemispheric functional coupling consistently with all of the contralateral sensorimotor cortices during the complex task. In contrast, in older group, all of the ipsilateral cortices including the M1 activated during the complex task, however, none of the cortices showed performance-related activity change. Increase in functional connectivity within the contralateral sensorimotor cortices rather than interhemispheric connectivity was observed during the complex task.The results suggest the importance and complementary role of the ipsilateral PMd for complex finger movement in the younger brains, and the strategic difference (interhemispheric vs. intrahemispheric) when the younger and aging brains perform the movement.
Complimentary vertebrateWacmodels exhibit phenotypes relevant to DeSanto-Shinawi Syndrome
Authors: Lee Kang-Han , Stafford April M , Pacheco-Vergara Maria , Cichewicz Karol , Canales Cesar P , Seban Nicolas , Corea Melissa , Rahbarian Darlene , Bonekamp Kelly E. , Gillie Grant R. , Pacheco-Cruz Dariangelly , Gill Alyssa M , Hwang Hye-Eun , Uhl Katie L , Jager Tara E , Shinawi Marwan , Li Xiaopeng , Obenaus Andre , Crandall Shane , Jeong Juhee , Nord Alex , Kim Cheol-Hee , Vogt Daniel 
Published: May 2024

AbstractMonogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments, neurobehavioral phenotypes including autism and attention deficit hyperactivity disorder (ADHD), and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in theWACgene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafishWac/wacdeletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We show that the twoWacmodels exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts to GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts ofWacloss of function that will pave the way for future molecular studies into DESSH. These studies begin to uncover some biological underpinnings of DESSH syndrome and elucidate the biology ofWac, with advantages in each model.