Biology subjects

Stern, E.

Publications and source records attributed to Stern, E..

9 recordsLinked to original sources

bioRxiv2026

Beyond power: A large-scale characterization of intrinsic brain oscillatory activity

Most of what we currently know about brain oscillations is derived from Fourier-based spectral power methods. While widely adopted, these procedures introduce methodological limitations and inherently overlook informative neurophysiological features that often remain unreported. In this study, we characterized resting-state oscillatory activity from human magnetoencephalography (MEG) recordings (N = 128) by integrating two complementary approaches. First, oscillatory episodes were detected at the source level with sBOSC. Subsequently, the ByCycle algorithm was applied to these episodes to extract individual cycle features. Results revealed that the brain engages in oscillatory activity for only [~]25% of the recording time, with occipital and parietal regions accounting for the highest temporal prevalence across canonical frequency bands. Furthermore, oscillatory episodes lasted an average of 4.6 cycles, reinforcing the view of neural oscillations as transient bursts. Region-specific duration and power measures revealed distinct anatomical organizations offering complementary physiological information. Finally, by extracting the instantaneous amplitude, period, and waveform asymmetry of individual cycles, we successfully dissociated sinusoidal occipital alpha waves from the asymmetric sensorimotor mu rhythm. By moving beyond traditional power-centric analyses, this approach provides a comprehensive characterization of spontaneous oscillatory activity, thereby offering new insights into the spatial, temporal, and spectral structure of human brain oscillations.

neuroscience↗

bioRxiv2026

Globally stable, locally flexible: Dynamic reconfiguration of brain natural frequencies during cognitive processing

Neural oscillations are fundamental to brain function and cognition. Conventional analyses often rely on predefined frequency bands to assess power modulations, which may obscure finer-grained spectral variability. In this study, we focused on frequency rather than power to investigate whether the natural frequency of each brain region, typically observed at rest, represents a stable intrinsic property or dynamically reconfigures during cognitive processing. We analysed magnetoencephalography (MEG) data from the Human Connectome Project (HCP) across motor execution, working memory, and language processing tasks. Using a multivariate, data-driven spectral clustering approach, we mapped natural frequencies on a voxel-by-voxel basis without imposing predefined bands or regional boundaries. Results indicated that, while the global spatial organization of natural frequencies remained largely preserved during task engagement, specific cortical regions exhibited systematic, task-dependent shifts. In the sensorimotor cortices, the typical resting frequency of [~]24 Hz decreased to [~]6 Hz during movement preparation and at movement onset, and shifted to high-beta rhythms ([~]30 Hz) following hand movement. Increased working memory demands accelerated parieto-occipital alpha/beta activity (from [~]11/16 Hz to [~]13/20 Hz) and recruited high-gamma oscillations (60 to 80 Hz) in medial temporal regions. Finally, arithmetic processing elicited a [~]5 to 15 Hz increase within the beta/gamma ranges across frontoparietal networks relative to semantic comprehension. Taken together, these findings demonstrate that natural frequencies reflect a hybrid architecture: globally stable, yet locally flexible in response to cognitive demands. Moreover, our results suggest that cognitive engagement tends to accelerate neural rhythms in functionally specialized regions, providing a more nuanced understanding of the spectral architecture of human brain function beyond conventional power- and band-based metrics.

neuroscience↗

bioRxiv2026

From MEG to low-density EEG: Reliable estimation of brain natural frequencies and their modulation across eyes-open and eyes-closed states

Neural oscillations are central to brain function and communication, yet they are typically characterized in terms of spectral power within predefined frequency bands, potentially obscuring their underlying functional organization. An alternative framework focuses on oscillatory frequency rather than power, revealing that each brain region exhibits a characteristic, or natural, frequency that can be estimated at the voxel level using a data-driven approach. Although this framework has been successfully applied to MEG, its broader use remains limited by cost and availability. Here, we extended this approach to EEG and validated it against MEG-derived maps, assessing its robustness across EEG channel densities (high-density, 64 channels; low-density, 32 channels) and physiological states (eyes open and closed). EEG-derived maps revealed a coherent spatial organization of natural frequencies across the cortex, reproducing the large-scale posterior-to-anterior and medial-to-lateral gradients of increasing frequency previously described with MEG. Differences between MEG and EEG were mainly confined to frontal and temporal regions, likely reflecting the differential sensitivity of the two techniques to neural source configurations, whereas posterior regions showed highly similar patterns. Importantly, this organization remained stable despite reductions in EEG sensor density and was modulated by physiological state, reproducing the well-known posterior alpha dominance during eyes-closed conditions. Together, these findings demonstrate that natural frequency mapping can be extended beyond specialized MEG research environments to low-density EEG settings, offering an accessible and scalable tool for investigating brain oscillations and their alterations in neuropsychiatric conditions.

neuroscience↗

bioRxiv2026

Large-scale exploration of protein space by automated NMR

Protein structures can now be predicted and designed at scale, yet experimental access to dynamics and conformational heterogeneity remains limited in throughput. This gap prevents a systematic understanding of how protein sequences encode motion and functional flexibility. Here, we establish a scalable experimental pipeline combining protein design, automated production, and nuclear magnetic resonance (NMR) spectroscopy to enable high-throughput characterization of protein structure and dynamics at atomic resolution. A single operator can produce and analyze hundreds of isotopically labeled proteins per week, with per-sample cost largely defined by DNA synthesis. To benchmark this approach, we experimentally characterized 384 de novo designed proteins spanning diverse regions of structure space. High-quality two-dimensional NMR spectra were obtained for 239 samples (62% of designs overall). NMR characterization confirmed that the designed proteins adopt their intended folds, and revealed unexpected local dynamics that are not captured by current computational models. Our approach establishes a foundation for data-driven modelling of sequence-structure-dynamics relationships and unlocks a new regime of statistical structural biology, where insight into protein biophysics is gained from experimental ensemble studies of suitably designed protein clusters.

biophysics↗

bioRxiv2025

pBOSC: A method for source-level identification of neural oscillations in electromagnetic brain signals

Neural oscillations are recognized as a fundamental component of brain electromagnetic activity. They are implicated in a wide range of cognitive processes and proposed as a core mechanism for brain communication. Nonetheless, detecting genuine neural oscillations remains a methodological challenge, particularly due to the difficulty of distinguishing them from aperiodic background activity. To identify episodes of oscillatory activity directly at their sources, we developed sBOSC, which extends the BOSC (Better OSCillation detection) family of algorithms. Consistent with existing approaches, sBOSC detects oscillatory episodes that exceed both a defined power threshold and a minimum duration criterion. In sBOSC, however, the detection of oscillatory episodes also relies on identifying peaks (i.e., local maxima) in the power spectra as well as throughout the brain volume (spatial peaks). Using a series of simulated signals, we tested the ability of sBOSC to detect and localize oscillations across multiple scenarios. Our results show that most oscillatory episodes were accurately detected at their sources, achieving above 95% accuracy under optimal conditions (i.e., high signal-to-noise ratio, lower frequencies, and numerous successive cycles). In addition, we validated sBOSCs performance using real magnetoencephalography (MEG) data from both resting-state and motor task recordings. From the detected oscillatory episodes, we extracted a topographic distribution of natural frequencies that is consistent with previous work, as well as the expected alpha- and beta-band modulations over sensorimotor regions during motor preparation. In conclusion, sBOSC offers a novel approach for identifying oscillatory activity in electrophysiological signals. It extends previous algorithms by operating in source space and verifying the presence of genuine spectral peaks, thereby enabling new possibilities for exploring brain dynamics.

neuroscience↗

bioRxiv2024

A Novel Cisplatin-Based Prodrug Inhibits Lysine Deacetylases, Suppresses Nucleotide Excision Repair, and Overcomes Resistance

Cisplatin [cis-diamminedichloroplatinum(II)] is a widely used chemotherapeutic agent that induces cytotoxicity primarily through DNA damage, but drug resistance severely limits its efficacy and use. Cisplatin resistance is complex and multifactorial, involving DNA repair via nucleotide excision repair (NER), and overexpression of lysine deacetylases (KDACs), which reduce chromatin accessibility and alter transcription regulation. The combination of cisplatin and KDAC inhibitors has shown promise in improving treatment efficacy. This improved efficacy has been attributed to increased drug sensitivity due to higher chromatin accessibility, however, this hypothesis has not been validated. In this study, we synthesized a novel Pt(IV) derivative, cct-[Pt(NH3)2Cl2(VPA)(PhB)] (cPVP), which combines cisplatin and two KDAC inhibitors, phenyl butyrate and valproic acid. This triple-action prodrug enabled the simultaneous targeting of multiple cancer-related pathways. Compared to cisplatin, cPVP exhibited significantly enhanced damage formation and cytotoxicity. High-resolution mapping of cisplatin damage and repair, however, does not attribute the enhanced damage sensitivity to chromatin accessibility, but rather to increased drug uptake and inhibition of nucleotide excision repair. Moreover, cPVP treatment increased survival in a mouse mesothelioma model, and prevented the development of resistance to both cisplatin and itself in cancer cells. Our findings shed new light on the effect of KDAC inhibition on cisplatin treatment, and suggest that cPVP could serve as a promising alternative to cisplatin in the clinic.

molecular biology↗

bioRxiv2023

The stress-induced lincRNA JUNI is a critical factor for cancer cell survival whose interactome is a prognostic signature in clear cell renal cell carcinoma

Cancer cells rely on adaptive mechanisms to survive the multiple stressors they encounter, including replication stress, toxic metabolic products and exposure to genotoxic drugs. Understanding the factors involved in these stress responses is crucial for developing effective treatments. Here, we describe a previously unstudied long non-coding RNA (lncRNA), JUNI (JUN-DT, LINC01135), which is regulated by MAPK and responsive to stress. JUNI positively regulates the expression of its neighboring gene JUN, a key transducer of signals that regulate multiple transcriptional outputs. Our findings reveal that silencing JUNI sensitizes cancer cells to chemotherapeutic drugs or UV radiation, and that its prolonged silencing leads to cell death regardless of stress exposure, highlighting the pro-survival importance of JUNI. We identified 57 proteins that interact with JUNI and found that the activity of one of them, the MAPK phosphatase and inhibitor DUSP14, is inhibited by JUNI. This effect results in c-Jun induction following exposure of cancer cells to UV radiation and promotes cellular survival. Although JUNI regulates c-Jun and its downstream targets, the pro-survival effects in cells not exposed to stress are only partially dependent on c-Jun regulation. JUNI expression levels significantly correlate with patients survival across 11 different types of cancer. Interestingly, the correlation of DUSP14 expression levels with patients survival in nine of these tumors is coherently inverse, indicating contradicting effects that are relevant not only for c-Jun induction and cellular survival but also in human cancer. Notably, we observed particularly significant antagonistic correlations in clear cell renal cell carcinoma (ccRCC) (p=5.7E-05 for JUNI and p=2.9E- 05 for Dusp14). In fact, the expression levels of 76% of JUNI-interacting proteins predict the prognosis of ccRCC patients significantly. Furthermore, a combined hazard ratio calculation demonstrates that this gene combination serves as a highly specific prognostic signature for ccRCC. Overall, our findings reveal a new important factor in stress signaling and cellular survival that is involved in ccRCC.

cell biology↗

bioRxiv2023

Perception of near-threshold visual stimuli is influenced by pre-stimulus alpha-band amplitude but not by alpha phase

Ongoing brain activity preceding visual stimulation has been suggested to shape conscious perception. The underlying mechanisms are still under debate, although alpha oscillations have been pointed out as the main explanatory candidate. According to the pulsed-inhibition framework, bouts of functional inhibition arise in each alpha cycle, allowing information to be processed in a pulsatile manner. Consequently, it has been hypothesized that perceptual outcome can be influenced by the specific phase of alpha oscillations prior to the stimulus onset, although empirical findings are controversial. In this study, we aimed to shed light on the role of pre-stimulus alpha oscillations in visual perception. To this end, we recorded electroencephalographic (EEG) activity while participants performed three near-threshold visual detection tasks with different attentional involvement: a no-cue task, a non-informative cue task (50% cue validity), and an informative cue task (100% cue validity). Cluster-based permutation statistics were complemented with Bayesian analyses to test the effect of pre-stimulus oscillatory amplitude and phase on visual awareness. We additionally examined whether these effects differed on trials with low and high oscillatory amplitude, as expected from the pulsed-inhibition theory. Our results show a clear effect of pre-stimulus alpha amplitude on conscious perception, but only when alpha fluctuated spontaneously and was not modulated by attention, supporting the notion that alpha-band power indexes neural excitability. In contrast, we did not find any evidence that pre-stimulus alpha phase influences the perceptual outcome, not even when differentiating between low and high amplitude trials. Furthermore, Bayesian analysis provided moderate evidence in favor of the absence of phase effects. Taken together, our results challenge the central theoretical predictions of the pulsed-inhibition framework, at least for the particular experimental conditions used here.

neuroscience↗

bioRxiv2022

Cancer cells survival is dependent on the lincRNA JUNI

Identification of key factors for cellular survival is a basis for therapy. We identified JUNI (linc01135) as a stress-regulated lncRNA, essential for cell survival and implicated in cancer. Besides regulating c-Jun expression we demonstrate c-Jun-independent, robust requirement for cell survival. Analysis of median survival of cancer patients suffering from various types of cancer reveals correlations of JUNI expression levels with alterations in patients survival. JUNIs antagonistic interaction with DUSP14, a negative regulator of the JNK pathway, underlies the regulation of c-Jun and partial effects on cellular survival. Consistently, DUSP14 expression is coherently inversely-correlated with the survival of patients suffering from the same types of cancer. Our data suggests that JUNI is a novel master regulator of cell fate. SummaryJUNI is a novel regulator of cell survival, JNK activation and c-Jun expression, implicated in survival of cancer patients

cell biology↗