172 research outputs found

    The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data

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    Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems

    Phase Dependency of the Human Primary Motor Cortex and Cholinergic Inhibition Cancelation during Beta tACS

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    The human motor cortex has a tendency to resonant activity at about 20 Hz so stimulation should more readily entrain neuronal populations at this frequency. We investigated whether and how different interneuronal circuits contribute to such resonance by using transcranial magnetic stimulation (TMS) during transcranial alternating current stimulation (tACS) at motor (20 Hz) and a nonmotor resonance frequency (7 Hz). We tested different TMS interneuronal protocols and triggered TMS pulses at different tACS phases. The effect of cholinergic short-latency afferent inhibition (SAI) was abolished by 20 Hz tACS, linking cortical beta activity to sensorimotor integration. However, this effect occurred regardless of the tACS phase. In contrast, 20 Hz tACS selectively modulated MEP size according to the phase of tACS during single pulse, GABAAergic short-interval intracortical inhibition (SICI) and glutamatergic intracortical facilitation (ICF). For SICI this phase effect was more marked during 20 Hz stimulation. Phase modulation of SICI also depended on whether or not spontaneous beta activity occurred at ~20 Hz, supporting an interaction effect between tACS and underlying circuit resonances. The present study provides in vivo evidence linking cortical beta activity to sensorimotor integration, and for beta oscillations in motor cortex being promoted by resonance in GABAAergic interneuronal circuits

    Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

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    Introduction: Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods: This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson’s disease patients. Results: Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion: Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study’s findings can inform future design iterations of neural decoders for adaptive DBS and BCI

    Average power and burst analysis revealed complementary information on drug-related changes of motor performance in Parkinson's disease

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    In patients with Parkinson's disease (PD), suppression of beta and increase in gamma oscillations in the subthalamic nucleus (STN) have been associated with both levodopa treatment and motor functions. Recent results suggest that modulation of the temporal dynamics of theses oscillations (bursting activity) might contain more information about pathological states and behaviour than their average power. Here we directly compared the information provided by power and burst analyses about the drug-related changes in STN activities and their impact on motor performance within PD patients. STN local field potential (LFP) signals were recorded from externalized patients performing self-paced movements ON and OFF levodopa. When normalised across medication states, both power and burst analyses showed an increase in low-beta oscillations in the dopamine-depleted state during rest. When normalised within-medication state, both analyses revealed that levodopa increased movement-related modulation in the alpha and low-gamma bands, with higher gamma activity around movement predicting faster reaches. Finally, burst analyses helped to reveal opposite drug-related changes in low- and high-beta frequency bands, and identified additional within-patient relationships between high-beta bursting and movement performance. Our findings suggest that although power and burst analyses share a lot in common they also provide complementary information on how STN-LFP activity is associated with motor performance, and how levodopa treatment may modify these relationships in a way that helps explain drug-related changes in motor performance. Different ways of normalisation in the power analysis can reveal different information. Similarly, the burst analysis is sensitive to how the threshold is defined - either for separate medication conditions separately, or across pooled conditions. In addition, the burst interpretation has far-reaching implications about the nature of neural oscillations - whether the oscillations happen as isolated burst-events or are they sustained phenomena with dynamic amplitude variations? This can be different for different frequency bands, and different for different medication states even for the same frequency band

    The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data

    Get PDF
    Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems

    Targeting HIF2α-ARNT hetero-dimerisation as a novel therapeutic strategy for pulmonary arterial hypertension

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    Pulmonary arterial hypertension (PAH) is a destructive disease of the pulmonary vasculature often leading to right heart failure and death. Current therapeutic intervention strategies only slow disease progression. The role of aberrant hypoxia-inducible factor (HIF)2α stability and function in the initiation and development of pulmonary hypertension (PH) has been an area of intense interest for nearly two decades. Here we determine the effect of a novel HIF2α inhibitor (PT2567) on PH disease initiation and progression, using two pre-clinical models of PH. Haemodynamic measurements were performed, followed by collection of heart, lung and blood for pathological, gene expression and biochemical analysis. Blood outgrowth endothelial cells from idiopathic PAH patients were used to determine the impact of HIF2α-inhibition on endothelial function. Global inhibition of HIF2a reduced pulmonary vascular haemodynamics and pulmonary vascular remodelling in both su5416/hypoxia prevention and intervention models. PT2567 intervention reduced the expression of PH-associated target genes in both lung and cardiac tissues and restored plasma nitrite concentration. Treatment of monocrotaline-exposed rodents with PT2567 reduced the impact on cardiovascular haemodynamics and promoted a survival advantage. In vitro, loss of HIF2α signalling in human pulmonary arterial endothelial cells suppresses target genes associated with inflammation, and PT2567 reduced the hyperproliferative phenotype and overactive arginase activity in blood outgrowth endothelial cells from idiopathic PAH patients. These data suggest that targeting HIF2α hetero-dimerisation with an orally bioavailable compound could offer a new therapeutic approach for PAH. Future studies are required to determine the role of HIF in the heterogeneous PAH population

    Neurophysiological features of STN LFP underlying sleep fragmentation in Parkinson’s disease

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    Background: Sleep fragmentation is a persistent problem throughout the course of Parkinson’s disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear. Method: We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed. Results: Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-betaNREM: r=0.9, p=0.0001, sleep fragmentation index (SFI)-betaNREM: r=0.6, p=0.0301; SFI-gammaNREM: r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPRNREM: r=−0.8, p=0.0053; ArI-LHPRREM: r=−0.6, p=0.0373; SFI-LHPRNREM: r=−0.7, p=0.0204; SFI-LHPRREM: r=−0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (β=−0.24, p<0.001). Conclusion: Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction. Trial registration number: NCT02937727

    Cyclic intensive light exposure induces retinal lesions similar to age-related macular degeneration in APPswe/PS1 bigenic mice

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    <p>Abstract</p> <p>Background</p> <p>Intensive light exposure and beta-amyloid (Aβ) aggregates have been known as a risk factor for macular degeneration and an important component in the pathologic drusen structure involved in this disorder, respectively. However, it is unknown whether Aβ deposition mediates or exacerbates light exposure-induced pathogenesis of macular degeneration. Several studies including the one from us already showed accumulation of Aβ deposits in the retina in Alzheimer's transgenic mice. Using histopathological analysis combined with electroretinographic functional assessment, we investigated the effects of cyclic intensive light exposure (CILE) on the architecture of retina and related function in the APPswe/PS1bigenic mouse.</p> <p>Results</p> <p>Histopathological analysis has found significant loss of outer nuclear layer/photoreceptor outer segment and outer plexiform layer along with abnormal hypo- and hyper-pigmentation in the retinal pigment epithelium (RPE), remarkable choroidal neovascularization (CNV), and exaggerated neuroinflammatory responses in the outer retina of APPswe/PS1 bigenic mice following cyclic intensive light exposure (CILE), whereas controls remained little change contrasted with age-matched non-transgenic littermates. CILE-induced degenerative changes in RPE are further confirmed by transmission electron microcopy and manifest as formation of basal laminar deposits, irregular thickening of Bruch's membrane (BrM), deposition of outer collagenous layer (OCL) in the subretinal space, and vacuolation in the RPE. Immunofluorescence microscopy reveals drusenoid Aβ deposits in RPE as well as neovessels attached which are associated with disruption of RPE integrity and provoked neuroinflammatory response as indicated by markedly increased retinal infiltration of microglia. Moreover, both immunohistochemistry and Western blots detect an induction of vascular endothelial growth factor (VEGF) in RPE, which corroborates increased CNV in the outer retina in the bigenic mice challenged by CILE.</p> <p>Conclusions</p> <p>Our findings demonstrate that degenerative changes in the outer retina in the APPswe/PS1 bigenic mouse induced by CILE are consistent with these in AMD. These results suggest that an Alzheimer's transgenic animal model with accumulation of Aβ deposits might be an alternative animal model for AMD, if combined with other confounding factors such as intensive light exposure for AMD.</p

    Differential expression and correlation of immunoregulation related piRNA in rheumatoid arthritis

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    BackgroundAlthough PIWI-interacting RNAs (piRNAs) have recently been associated with germline development and many human diseases, their expression pattern and relationship in autoimmune diseases remain indistinct. This study aimed to investigate the presence and correlation of piRNAs in rheumatoid arthritis (RA).MethodsWe first analyzed the expression profile of piRNAs using small RNA sequencing in peripheral leukocytes of three new-onset untreated RA patients and three healthy controls (HCs). We then selected piRNAs related to immunoregulation by bioinformatics analysis and verified them in 42 new-onset RA patients and 81 HCs by RT-qPCR. Furthermore, a receiver operating characteristic curve was generated to quantify the diagnostic performance of these piRNAs. A correlation analysis was conducted to observe the link between piRNA expression and RA clinical characteristics.ResultsA total of 15 upregulated and 9 downregulated piRNAs among 1,565 known piRNAs were identified in peripheral leukocytes of RA patients. Dysregulated piRNAs were enriched in numerous pathways related to immunity. After selection and validation, two immunoregulation piRNAs (piR-hsa-27620 and piR-hsa-27124) were significantly elevated in RA patients and have good abilities to distinguish patients from controls, which have the potential to serve as biomarkers. PIWI and other proteins implicated in the piRNA pathway were also associated with RA
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