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Tag: Neuroscience & Neural Engineering

  • Fuzzing detects unsafe outputs in ML neurostimulation models

    What the study found

    The study found that a coverage-guided fuzzing approach, a type of automated stress testing, can detect and describe unsafe stimulation patterns in machine learning-driven neurostimulation systems. Applied to deep stimulus encoders for the retina and cortex, it revealed stimulation outputs that exceeded established safety limits.

    Why the authors say this matters

    The authors conclude that violation-focused fuzzing can make safety assessment more empirical and reproducible. They say this creates a foundation for evidence-based benchmarking, regulatory readiness, and ethical assurance in neural interfaces.

    What the researchers tested

    The researchers adapted coverage-guided fuzzing from software testing to neural stimulation. They perturbed model inputs, treated the encoders as black boxes, and tracked whether the resulting stimulation violated limits on charge density, instantaneous current, or electrode co-activation.

    What worked and what didn't

    The method systematically found diverse stimulation regimes that violated safety limits in models for the retina and cortex. Two violation-output coverage metrics identified the highest number and diversity of unsafe outputs, and they allowed comparisons across architectures and training strategies.

    What to keep in mind

    The abstract describes testing on deep stimulus encoders for the retina and cortex, so the reported findings are limited to those systems. The summary does not provide numerical results, and it does not describe limitations beyond the scope of the tested models.

    • Coverage-guided fuzzing was adapted to test ML-driven neurostimulation systems.
    • The approach checked outputs against limits on charge density, instantaneous current, and electrode co-activation.
    • Applied to retinal and cortical stimulus encoders, it found outputs that exceeded safety limits.
    • Two violation-output coverage metrics identified the most unsafe outputs and the widest range of unsafe outputs.
    • The authors say this makes safety assessment more reproducible and measurable.

  • TPH2 genotype alters brain response to acute serotonin reduction

    What the study found

    The study found that a TPH2 genetic variant was linked to different brain responses to acute tryptophan depletion, which temporarily reduces central serotonin signaling. The findings point to a possible genetic marker of vulnerability to these serotonin-related effects in men.

    Why the authors say this matters

    The authors conclude that their results identify a potential genetic marker for increased vulnerability to the neural effects of transient serotonin variation in an anxiety- and stress-related brain circuit in men. They also suggest that serotonin helps regulate the organization of large-scale brain networks involved in emotional reactivity and regulation.

    What the researchers tested

    The researchers used a within-subject randomized placebo-controlled resting-state functional MRI design in 53 healthy men. They compared the effects of acute tryptophan depletion between two TPH2 genotypes, rs4570625 GG and TT, and analyzed spontaneous intrinsic neural activity, functional connectivity, and connectome gradients.

    What worked and what didn't

    Acute tryptophan depletion increased spontaneous neural activity in hippocampal CA1 regardless of genotype. It also increased communication between CA1 and the bilateral amygdala and ventromedial prefrontal cortex specifically in GG carriers. The depletion also sharpened intrinsic connectome gradient architecture in the salience, frontoparietal, and default mode networks.

    What to keep in mind

    The study included only healthy male participants, so the findings are limited to that group. The abstract does not describe additional limitations.

    • A TPH2 genetic variant was associated with different brain responses to acute tryptophan depletion.
    • Acute tryptophan depletion increased hippocampal CA1 activity in all participants.
    • CA1 communication with the bilateral amygdala and ventromedial prefrontal cortex increased only in GG carriers.
    • Connectome gradient architecture changed in the salience, frontoparietal, and default mode networks.
    • The study involved 53 healthy men in a randomized placebo-controlled resting-state fMRI design.

  • Most implant particles did not activate glial cells

    What the study found

    The study found that silicon nitride, cobalt oxide, and chromium oxide particles did not cause visible inflammatory activation in two types of central nervous system support cells: astrocytes and microglia. An exception was that high concentrations of silicon nitride reduced microglial viability.

    Why the authors say this matters

    The authors conclude that the release of these particles from a spinal implant would not induce an inflammatory response in surrounding glial cells. They also say the findings highlight the importance of studying silicon nitride particles further to understand safety aspects of using silicon nitride in spinal implants.

    What the researchers tested

    The researchers exposed astrocytes and microglia to particles made of silicon nitride, cobalt oxide, or chromium oxide at a range of relevant concentrations. They then assessed cell viability, astrocyte reactivity markers, microglial phagocytic activity, and the release of inflammatory cytokines.

    What worked and what didn't

    Astrocyte viability was not impaired, and the astrocyte reactivity markers GFAP and vimentin were unchanged after 24 hours of exposure. The particles also did not alter microglial phagocytic activity or stimulate release of TNF-α or IL-6. High concentrations of silicon nitride reduced microglial cell viability, which the abstract says may be related to particle agglomeration and/or dissolution rate.

    What to keep in mind

    The abstract does not describe study limitations in detail. The findings are based on cell exposures under the tested conditions and time frame, so the summary does not provide information beyond those experiments.

    • Astrocytes were not impaired by silicon nitride, cobalt oxide, or chromium oxide particles.
    • GFAP and vimentin, two astrocyte reactivity markers, were unchanged after 24 hours.
    • Microglial phagocytic activity and release of TNF-α and IL-6 were not increased by the particles.
    • High concentrations of silicon nitride reduced microglial viability.
    • The authors conclude that released particles would not induce an inflammatory response in surrounding glial cells.

  • Graph model improves depression case identification

    What the study found

    The study reports a graph-based deep learning framework, called BrainADNet, for identifying major depressive disorder (MDD, a serious mental health condition) across different depressive stages. The authors say it outperformed existing models in classifying MDD cases, and it also highlighted gender-specific brain regions and differences between single and multiple depression episodes.

    Why the authors say this matters

    The authors conclude that improving diagnostic precision for MDD may support more effective intervention. They also suggest that gender-specific and stage-wise insights could help researchers and clinicians design more personalized and targeted therapeutic strategies.

    What the researchers tested

    The researchers developed BrainADNet, a graph-based deep learning framework built on a Skip-Graph Convolutional Network, to work with limited training data by augmenting brain signal inputs. They incorporated demographic attributes—age, education, and gender—into training, and used a decorrelation regularizer to encourage non-redundant learned representations. They also carried out an ablation study to examine the contribution of each component.

    What worked and what didn't

    According to the abstract, the framework improved diagnostic accuracy for MDD and reduced feature redundancy. It also identified the top-10 brain regions influential in diagnosing MDD in males and females, and revealed distinct latent-space brain connectivity patterns between people with single versus multiple depressive episodes. The abstract does not report any specific component that failed or underperformed.

    What to keep in mind

    The abstract does not provide numerical performance values or detailed comparisons with prior models. It also does not describe the dataset, evaluation setting, or limitations beyond noting the challenge of limited training data.

    • BrainADNet is a graph-based deep learning framework for identifying MDD across depressive stages.
    • The authors say the model outperformed existing models in classifying MDD cases.
    • The method used augmented brain signal inputs, demographic attributes, and decorrelation regularization.
    • The study highlights gender-specific brain regions and differences between single and multiple depression episodes.
    • The abstract does not report numerical results or detailed limitations.

  • Fuzzing identified unsafe stimulation outputs in ML neurostimulation

    What the study found

    The study found that automated stress testing can reveal unsafe electrical stimulation outputs in machine learning (ML)-driven neurostimulation systems. Applied to deep stimulus encoders for the retina and cortex, the method exposed stimulation regimes that exceeded established safety limits.

    Why the authors say this matters

    The authors conclude that violation-focused fuzzing can make safety assessment more empirical and reproducible. They say this creates a foundation for evidence-based benchmarking, regulatory readiness, and ethical assurance in next-generation neural interfaces.

    What the researchers tested

    The researchers adapted coverage-guided fuzzing, an automated software testing method, to neural stimulation. In this framework, fuzzing perturbs model inputs and checks whether the resulting stimulation violates limits on charge density, instantaneous current, or electrode co-activation, while treating the encoders as black boxes.

    What worked and what didn't

    The method systematically found diverse stimulation regimes that violated safety limits in models for the retina and cortex. Two violation-output coverage metrics identified the highest number and diversity of unsafe outputs and allowed interpretable comparisons across architectures and training strategies.

    What to keep in mind

    The abstract does not describe the full dataset, experimental settings, or detailed limitations. It also focuses on deep stimulus encoders for the retina and cortex, so the stated findings are limited to those tested systems.

    • Coverage-guided fuzzing was adapted to test ML-driven neurostimulation systems.
    • The method found stimulation outputs that exceeded limits on charge density, instantaneous current, or electrode co-activation.
    • Tests on deep stimulus encoders for the retina and cortex revealed diverse unsafe stimulation regimes.
    • Two violation-output coverage metrics identified the most and most diverse unsafe outputs.
    • The authors say the approach can support evidence-based benchmarking and regulatory readiness.

  • Higher neural vulnerability amplified stress-related symptoms in adolescents

    What the study found

    Adolescents with higher polyconnectomic scoring for major depressive disorder, a score based on large-scale brain connectivity patterns, showed greater stress-related emotional change over time. Perceived stress was also linked to higher anxiety and depressive symptoms, and these links were stronger in adolescents with higher neural vulnerability.

    Why the authors say this matters

    The authors conclude that neural vulnerability may moderate the emotional correlates of stress in youth. They also suggest the findings underscore the potential relevance of personalized interventions targeting stress regulation in young people with elevated vulnerability profiles.

    What the researchers tested

    The researchers followed a cohort of 407 adolescents over time and measured perceived stress, emotional symptoms, and cognitive function at baseline and follow-up. They used polyconnectomic scoring for major depressive disorder (PCS-MDD) to index neural vulnerability, and they also examined an independent cohort of 80 adolescents with clinically diagnosed depressive disorders to explore generalizability.

    What worked and what didn't

    At baseline, perceived stress was strongly associated with anxiety and depressive symptoms, and it was modestly associated with lower performance across multiple cognitive domains. Over time, increases in perceived stress were robustly associated with increases in anxiety and depressive symptoms. PCS-MDD significantly moderated the stress-emotion associations, and baseline PCS-MDD was also associated with follow-up anxiety and depressive symptoms. Exploratory analyses indicated that perceived stress was most strongly associated with depressive symptoms among adolescents with clinically diagnosed depressive disorders and higher PCS-MDD.

    What to keep in mind

    The summary does not describe specific limitations, so none can be stated here beyond the study's scope. The findings are reported for adolescents in the studied cohorts, including an exploratory analysis in a clinically diagnosed group.

    • Higher PCS-MDD was linked to greater variability in stress-related emotional outcomes during adolescence.
    • Perceived stress was associated with higher anxiety and depressive symptoms at baseline and over time.
    • PCS-MDD moderated the relationship between perceived stress and later anxiety and depressive symptoms.
    • Baseline PCS-MDD predicted follow-up anxiety and depressive symptoms.
    • Perceived stress was most strongly associated with depressive symptoms in adolescents with diagnosed depressive disorders and higher PCS-MDD.

  • Low-cost FPGA system preserves electrophysiological recording fidelity

    What the study found

    The study found that a cost-effective field-programmable gate array (FPGA) platform with an embedded ARM Cortex M1 soft core processor can acquire and process electrophysiological signals while preserving measurement fidelity. The authors report that the system is accessible and extensible for resource-constrained laboratories.

    Why the authors say this matters

    The authors say flexible, low-cost platforms for high-fidelity recording of biological signals are essential for advancing health monitoring applications. They conclude that lowering reliance on proprietary, resource-intensive hardware may widen adoption in research laboratories.

    What the researchers tested

    The researchers built an FPGA-based platform around an Intan RHD2000 headstage, with custom logic for signal acquisition, signal conditioning, artifact suppression, and data management. They added on-chip routines for automatic offset calibration and gain calibration, and developed a graphical user interface with biomedical end users.

    What worked and what didn't

    Bench validation with a multichannel test generator reproducing cardiac field potentials showed stable timing, low crosstalk, and accurate amplitude reconstruction. The platform performance matched commercial multielectrode array systems, with minor deviations attributed to interconnection effects.

    What to keep in mind

    The available summary does not describe major limitations in detail. The validation described here was bench testing with biological signal simulations rather than a broader clinical or field deployment.

    • The platform combines an FPGA with an ARM Cortex M1 soft core processor.
    • It was designed for acquisition, signal conditioning, artifact suppression, and data management.
    • Automatic offset and gain calibration were built into the system.
    • Bench tests showed stable timing, low crosstalk, and accurate amplitude reconstruction.
    • Performance was reported to match commercial multielectrode array systems, with minor deviations from interconnection effects.

  • Neuronal autophagy failure promotes alpha-synuclein transfer to microglia

    What the study found

    The study found that neurons exposed to alpha-synuclein aggregates had compromised lysosomal degradation and impaired autophagic flux, while microglia showed higher lysosomal turnover, especially through lysophagy, a process that removes damaged lysosomes. The authors report that this imbalance helps drive transfer of the aggregates from neurons to microglia through tunneling nanotubes, which are thin cell connections that allow cargo exchange.

    Why the authors say this matters

    The authors conclude that dysfunctional autophagy in neurons is a key driver of outsourcing alpha-synuclein aggregates to microglia. The findings indicate that this cell-to-cell transfer may depend on how well neurons clear aggregates and how effectively microglia can degrade what they receive.

    What the researchers tested

    The researchers used human neuronal and microglial cell lines, and also tested human induced pluripotent stem cell-derived neurons and microglia, to examine how alpha-synuclein aggregates are handled after exposure. They assessed lysosomal processing, autophagic flux, lysophagy, and tunneling nanotube-mediated transfer between neurons and microglia, including the effect of autophagy inhibition.

    What worked and what didn't

    Microglia showed higher lysosomal turnover and efficient degradation of transferred aggregates, while neuronal cells showed reduced degradative capacity and impaired autophagic flux after alpha-synuclein exposure. When aggregate clearance was further disrupted by autophagy inhibition, transfer from neurons to microglia through tunneling nanotubes increased.

    What to keep in mind

    The summary describes cell-based experiments, so the findings are limited to the systems tested in the abstract. The abstract does not provide detailed limitations, quantitative results, or in vivo evidence.

    • Neurons exposed to alpha-synuclein aggregates showed impaired lysosomal degradation and autophagic flux.
    • Microglia had higher lysosomal turnover, including lysophagy.
    • Autophagy inhibition increased tunneling nanotube-mediated transfer of alpha-synuclein from neurons to microglia.
    • Human iPSC-derived neurons and microglia showed a similar response pattern.
    • The authors describe dysfunctional neuronal autophagy as a driver of aggregate transfer.

  • Temporal interference stimulation may noninvasively target deep brain circuits

    What the study found

    The review concludes that temporal interference stimulation, or TES-TI, is a promising but still exploratory way to noninvasively probe and modulate deep brain circuits relevant to psychiatric disorders. It also notes that TES-TI is rapidly developing and that more study is needed before clinical use.

    Why the authors say this matters

    The authors suggest TES-TI matters because it may allow steerable, relatively focal engagement of deep regions with reduced off-target exposure compared with conventional transcranial electrical stimulation. They also present it as a noninvasive approach inspired by deep brain stimulation for influencing pathological network activity.

    What the researchers tested

    This is a review article. The authors outline the biophysical principles and technical implementation of TES-TI, summarize safety and feasibility data in humans, and consider potential psychiatric applications and evidence for engagement of key targets.

    What worked and what didn't

    The review says TES-TI can generate a low-frequency amplitude-modulated envelope using multiple high-frequency carrier currents, and it is thought to engage neural circuits by modulating physiologically relevant oscillations between 0.5 and 80 Hz, with higher-frequency exploration around 130 Hz. However, the abstract says much remains to be investigated about parameter optimization, and about the strength, durability, and clinical relevance of its effects.

    What to keep in mind

    The available summary does not provide detailed trial outcomes or specific clinical results. It also states that parameter choices such as frequency, intensity, dose, and dosing schedules still need further study, and that the clinical translation of TES-TI remains premature.

    • TES-TI uses multiple high-frequency carrier currents to create a low-frequency amplitude-modulated envelope.
    • The authors describe TES-TI as relatively focal and steerable compared with conventional transcranial electrical stimulation.
    • The review summarizes human safety and feasibility data, but not definitive clinical effectiveness.
    • TES-TI is discussed as a noninvasive approach inspired by deep brain stimulation.
    • More research is needed on frequency, intensity, dose, dosing schedules, and effect durability.

  • Emotion regulation success tracks brain-wide gradient reconfiguration

    What the study found

    The study found that individual differences in emotion regulation success were associated with systematic reconfiguration along Gradient 1, a principal axis of large-scale brain organization that differentiates unimodal and heteromodal brain areas. The authors also report that this gradient-based pattern was associated with lower negative affect in daily life in a smaller subset of participants.

    Why the authors say this matters

    The authors suggest this provides a biologically grounded, gradient-based perspective on emotion regulation success. They conclude that these dynamics may serve as predictive biomarkers of regulatory success and could inform targeted interventions in clinical populations.

    What the researchers tested

    The researchers analyzed two large functional magnetic resonance imaging (fMRI) datasets, with 358 and 263 participants. They projected global activation patterns from a laboratory emotion regulation task onto principal gradients derived from independent resting-state fMRI data from the Human Connectome Project, and they also used smartphone-based experience sampling in a subset of 55 participants.

    What worked and what didn't

    In both datasets, regulation success was associated with reconfiguration along Gradient 1. This same gradient-based neural reconfiguration was associated with lower negative affect in daily life in the subset studied, and meta-analytic decoding linked Gradient 1 and regulation success with social cognition, memory, attention, and negative emotion. The abstract does not describe null results or failed analyses.

    What to keep in mind

    The daily-life affect finding was based on a smaller subset of participants, not the full samples. The abstract does not describe additional limitations, and it does not claim that the findings establish causation.

    • Emotion regulation success was linked to systematic reconfiguration along Gradient 1 in two fMRI datasets.
    • Gradient 1 is described as a principal axis separating unimodal and heteromodal brain areas.
    • A similar gradient-based pattern was associated with lower negative affect in daily life in a subset of 55 participants.
    • Meta-analytic decoding connected the gradient and regulation success with social cognition, memory, attention, and negative emotion.
    • The authors suggest the pattern may be useful as a predictive biomarker and for clinical interventions.