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Frontiers in Neuroscience Journal

Multi-view fusion of diffusion MRI microstructural models: a preterm birth study
ObjectiveHigh Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.ApproachRather than focusing on a single MRI modality, we studied on a compound of HARDI techniques in 46 preterm babies studied on a 3T scanner at term-equivalent age and in 23 control neonates born at term. Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification on skeletonized HARDI measures yielded satisfactory accuracy, particularly for highly informative parameters about fiber directionality. Assessment of the degree of overlap between the two methods in voting for the most discriminating features exhibited a good, though parameter-dependent, rate of agreement. Finally, CCA identified joint changes precisely for those measures exhibiting less correspondence between TBSS and SVM.SignificanceOur results suggest that a data-driven intramodal imaging approach is crucial for gathering deep and complementary information. The main contribution of this methodological outline is to thoroughly investigate prematurity-related white matter changes through different inquiry focuses, with a view to addressing this issue, both aiming toward mechanistic insight and optimizing predictive accuracy.
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Perioperative enriched environment attenuates postoperative cognitive dysfunction by upregulating microglia TREM2 via PI3K/Akt pathway in mouse model of ischemic stroke
Postoperative cognitive dysfunction (POCD) is a prevalent complication that significantly affects the quality of life. Notably, patients who have experienced ischemic stroke are at an increased risk of developing POCD. Exploring the underlying mechanisms of POCD is crucial for its management. Numerous studies have established neuroinflammation as an independent risk factor in POCD pathogenesis, with TREM2 emerging as a key neuroprotective factor that modulates neuroinflammatory responses through the PI3K/Akt signaling pathway. In this study, we aimed to investigate the effect of TREM2 on POCD in a mouse model of ischemic stroke, with a focus on the mechanisms involving TREM2 and the PI3K/Akt signaling pathway. Our findings indicated that mice with ischemic stroke exhibited severe cognitive impairment after surgical trauma. However, we observed that an enriched environment (EE) could ameliorate this cognitive impairment by upregulating microglia TREM2 expression in the hippocampus and suppressing neuroinflammation. Additionally, the PI3K/AKT signaling pathway was activated in the hippocampal tissue of the mice housed in EE. Importantly, the beneficial neuroprotective and anti-inflammatory effects of EE were abolished when TREM2 was knocked down, underscoring the essential role of TREM2 in mediating the effects of EE on neuroinflammation and cognitive function after ischemic stroke and surgical trauma. In general, our study has confirmed a potential molecular mechanism that led to the occurrence of POCD in individuals with ischemic stroke and provided new strategies to treat POCD.
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Case report: A de novo variant of CRMP1 in an individual with a neurodevelopmental disorder
BackgroundCRMP1 is a key protein involved in brain development.MethodsWe performed genetic testing through whole-exome sequencing (WES) in an individual with a neurodevelopmental disorder.ResultsWe identified a de novo heterozygous CRMP1 NM_001014809.3:c.1755del (p.Lys586fs) variant in the affected individual. This mutation was submitted to ClinVar (SCV005196589).ConclusionCurrently, the CRMP1 gene has no clear disease phenotype association in the Online Mendelian Inheritance in Man (OMIM) database. Our report may provide evidence for an association between the CRMP1 gene and neurodevelopmental disorders (NDDs).
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Multi-tensor fixel-based metrics in tractometry: application to multiple sclerosis
Traditional Diffusion Tensor Imaging (DTI) metrics are affected by crossing fibers and lesions. Most of the previous tractometry works use the single diffusion tensor, which leads to limited sensitivity and challenging interpretation of the results in crossing fiber regions. In this work, we propose a tractometry pipeline that combines white matter tractography with multi-tensor fixel-based metrics. These multi-tensors are estimated using the stable, accurate and robust to noise Multi-Resolution Discrete Search method (MRDS). The spatial coherence of the multi-tensor field estimated with MRDS, which includes up to three anisotropic and one isotropic tensors, is tractography-regularized using the Track Orientation Density Imaging method. Our end-to-end tractometry pipeline goes from raw data to track-specific multi-tensor-metrics tract profiles that are robust to noise and crossing fibers. A comprehensive evaluation conducted in a phantom simulating healthy and damaged tissue with the standard model, as well as in a healthy cohort of 20 individuals scanned along 5 time points, demonstrates the advantages of using multi-tensor metrics over traditional single-tensor metrics in tractometry. Qualitative assessment in a cohort of patients with relapsing-remitting multiple sclerosis reveals that the pipeline effectively detects white matter anomalies in the presence of crossing fibers and lesions.
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Distinct brain systems are involved in subjective minute estimation with eyes open or closed: EEG source analysis study
IntroductionTime perception is a fundamental cognitive function, the brain mechanisms of which are not fully understood. Recent electroencephalography (EEG) studies have shown that neural oscillations in specific frequency bands may play a role in this process. In the current study, we sought to investigate how neurophysiological activity of cortical structures relates to subjective time estimations.MethodsThe study sample included 41 healthy volunteers, who were to produce subjective minutes with eyes closed and open by pressing the response button marking the beginning and end of this time interval. High-density EEG was recorded in parallel and the activity of cortical sources within the theta, alpha, and beta frequency bands was analyzed with standardized low-resolution brain electromagnetic tomography.ResultsThe results revealed that activity of several cortical structures within the beta-band correlated with the duration of subjective minutes across participants, which highlights the role of the beta-rhythm in supra-second time perception. The sets of involved structures were different depending on eyes being open or closed, while the produced duration did not differ being around 58 s in both conditions. Individual minute correlated with beta power in the left precuneus, left superior parietal lobule, and right superior frontal gyrus (SFG) during eyes-closed sessions, and with that in the caudal anterior cingulate cortex, cuneus, posterior cingulate cortex, parahippocampal gyrus, and right lingual gyrus during the eyes-open condition. Noteworthy, some structures showed tendencies toward opposite correlations between conditions.DiscussionTaken together, our findings bridge the gap between functional magnetic resonance imaging and EEG time perception studies and suggest reliance on different aspects of subjective experience when judging about time with eyes open or closed.
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Factors impacting human-perceived visual quality on television displays
There is a growing interest in understanding the factors that influence a user's perception and preferences for video quality. This study specifically focuses on how various factors, including video content, display settings, viewer characteristics, and the ambient environment, affect the subjective video quality assessment (VQA) of TV displays. To investigate these factors, two psychophysical experiments were conducted, and the results indicate that all four factors have a significant impact on video quality perception in different ways. This study is beneficial for researchers and developers who aim to improve display and environmental settings to provide viewers with the best possible viewing experience.
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Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions
Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g., through the intelligent design of public spaces, mobility, and transportation) and (ii) improve resilience and sustainability (e.g., through better disaster management, planning of city logistics, and waste management). This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. Motivated by the potential interplay between societal challenges and these emerging technologies, we provide an overview of state-of-the-art research through a bibliometric analysis of neurochallenges within the context of smart cities using terms and data extracted from the Scopus database between 2018 and 2022. The results indicate that smart city research remains fragmented and technology-driven, relying heavily on internet of things (IoT) and artificial intelligence (AI)-based technologies. Mostly, it also lacks careful integration and adoption tailored to societal goals and human-centric concerns. In this context, the article explores key research streams and discusses how to create new synergies and complementarities in the challenge-technology intersection. We conclude that realizing the vision of smart cities at the nexus of neuroscience, technology, urban space, and society requires more than just technological progress. Integrating the human dimension alongside various technological tools and systems is crucial. This necessitates better interdisciplinary collaboration and co-production of knowledge toward a hybrid intelligence, where synergies of education and research, technological innovation, and societal innovation are genuinely built. We hope the insights from this analysis will help orient neurotechnological interventions on urban living and ensure they are more responsive to societal and environmental challenges as well as to legal and ethical concerns.
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Exploring neural mechanisms of gender differences in bodily emotion recognition: a time-frequency analysis and network analysis study
BackgroundThis study aimed to explore the neural mechanisms underlying gender differences in recognizing emotional expressions conveyed through body language. Utilizing electroencephalogram (EEG) recordings, we examined the impact of gender on neural responses through time-frequency analysis and network analysis to uncover gender disparities in bodily emotion recognition.MethodsThe study included 34 participants, consisting of 18 males and 16 females. A 2 × 2 mixed design was employed, with gender (male and female) and bodily emotion (happy and sad) as the independent variables. Both behavioral and EEG data were collected simultaneously.ResultsMales demonstrated more stable brain activity patterns when recognizing different bodily emotions, while females showed more intricate and highly interconnected brain activity networks, especially when identifying negative emotions like sadness. Differences based on gender were also observed in the significance of brain regions; males had greater importance in central brain areas, whereas females exhibited higher significance in the parietal lobe.ConclusionGender differences do influence the recognition of bodily emotions to some extent. The primary aim of this study was to explore the neural mechanisms underlying gender differences in bodily emotion recognition, with a particular focus on time-frequency analysis and network analysis based on electroencephalogram (EEG) recordings. By elucidating the role of gender in cognitive development, this study contributes to early detection and intervention.
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An all integer-based spiking neural network with dynamic threshold adaptation
Spiking Neural Networks (SNNs) are typically regards as the third generation of neural networks due to their inherent event-driven computing capabilities and remarkable energy efficiency. However, training an SNN that possesses fast inference speed and comparable accuracy to modern artificial neural networks (ANNs) remains a considerable challenge. In this article, a sophisticated SNN modeling algorithm incorporating a novel dynamic threshold adaptation mechanism is proposed. It aims to eliminate the spiking synchronization error commonly occurred in many traditional ANN2SNN conversion works. Additionally, all variables in the proposed SNNs, including the membrane potential, threshold and synaptic weights, are quantized to integers, making them highly compatible with hardware implementation. Experimental results indicate that the proposed spiking LeNet and VGG-Net achieve accuracies exceeding 99.45% and 93.15% on the MNIST and CIFAR-10 datasets, respectively, with only 4 and 8 time steps required for simulating one sample. Due to this all integer-based quantization process, the required computational operations are significantly reduced, potentially providing a substantial energy efficiency advantage for numerous edge computing applications.
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Adolescent circadian rhythm disruption increases reward and risk-taking
IntroductionCircadian rhythm disturbances have long been associated with the development of psychiatric disorders, including mood and substance use disorders. Adolescence is a particularly vulnerable time for the onset of psychiatric disorders and for circadian rhythm and sleep disruptions. Preclinical studies have found that circadian rhythm disruption (CRD) impacts the brain and behavior, but this research is largely focused on adult disruptions. Here, we hypothesized that adolescent CRD would have a greater effect on psychiatric-related behaviors, relative to adult disruption.MethodsWe determined the long-term behavioral and neurobiological effects of CRD during early adolescence by exposing mice to 12 h shifts in the light/dark cycle. Adult mice were exposed to the same CRD paradigm. Behavior testing began approximately 4 weeks later for both groups. To identify possible mechanisms, we also measured gene expression in brain regions relevant to circadian rhythms, mood and reward.ResultsCRD during early adolescence, but not adulthood, persistently increased exploratory drive (risk-taking behavior) and cocaine preference when tested later in life. Interestingly, we found sex differences when intravenous cocaine self-administration was tested. While female mice with a history of adolescent CRD had a greater propensity to self-administer cocaine, as well as increased motivation and cue-induced reinstatement, male adolescent CRD mice had reduced motivation and extinction responding. Importantly, we found that transcripts in the SCN were affected by adolescent CRD and these were largely distinct across sex.ConclusionOverall, adolescent CRD in mice caused persistent increases in risky behavior, cocaine reward and cocaine self-administration, which suggests that CRD during adolescence may predispose individuals toward substance use disorders. Future research is required to elucidate how adolescent CRD affects behaviors relevant to mood-and substance use-related disorders across the 24-h day, as well as to identify intervention strategies to alleviate disruption during adolescence and novel therapeutic approaches once symptoms have begun.
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Annao Pingchong decoction attenuates oxidative stress and neuronal apoptosis following intracerebral hemorrhage via RAGE-NOX2/4 axis
BackgroundIntracerebral hemorrhage (ICH) is a severe condition associated with high mortality and disability rates. Oxidative stress plays a critical role in the development of secondary brain injury (SBI) following ICH. Previous research has demonstrated that Annao Pingchong decoction (ANPCD) treatment for ICH has antioxidant effects, but the exact mechanism is not yet fully understood.ObjectiveThis study aimed to investigate the neuroprotective effects of ANPCD on oxidative stress and neuronal apoptosis after ICH by targeting the receptor for advanced glycation end products (RAGE)-NADPH oxidase (NOX) 2/4 signaling axis.MethodsThe research involved the creation of rat ICH models, the mNSS assay to assess neurological function, Nissl staining to evaluate neuronal damage, and biochemical assays to measure oxidative and antioxidant levels. The expression of RAGE-NOX2/4 axis proteins was analyzed using western blotting and immunofluorescence, while neuronal apoptosis was assessed with TUNEL staining. Furthermore, after performing quality control of drug-containing serum using UPLC-MS/MS, we employed an in vitro model of heme-induced injury in rat cortical neurons to investigate the neuroprotective mechanisms of ANPCD utilizing RAGE inhibitors.ResultsThe findings indicated that ANPCD improved neurological deficits, reduced neuronal damage, decreased ROS and MDA levels, and increased the activities enzymatic activities of SOD, CAT, GSH and GPX. Additionally, it suppressed the RAGE-NOX2/4 signaling axis and neuronal apoptosis.ConclusionANPCD exhibits neuroprotective effects by inhibiting the RAGE-NOX2/4 signaling axis, thereby alleviating neuronal oxidative stress and apoptosis following ICH.
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Olfactory deficits in aging and Alzheimer’s—spotlight on inhibitory interneurons
Cognitive function in healthy aging and neurodegenerative diseases like Alzheimer’s disease (AD) correlates to olfactory performance. Aging and disease progression both show marked olfactory deficits in humans and rodents. As a clear understanding of what causes olfactory deficits is still missing, research on this topic is paramount to diagnostics and early intervention therapy. A recent development of this research is focusing on GABAergic interneurons. Both aging and AD show a change in excitation/inhibition balance, indicating reduced inhibitory network functions. In the olfactory system, inhibition has an especially prominent role in processing information, as the olfactory bulb (OB), the first relay station of olfactory information in the brain, contains an unusually high number of inhibitory interneurons. This review summarizes the current knowledge on inhibitory interneurons at the level of the OB and the primary olfactory cortices to gain an overview of how these neurons might influence olfactory behavior. We also compare changes in interneuron composition in different olfactory brain areas between healthy aging and AD as the most common neurodegenerative disease. We find that pathophysiological changes in olfactory areas mirror findings from hippocampal and cortical regions that describe a marked cell loss for GABAergic interneurons in AD but not aging. Rather than differences in brain areas, differences in vulnerability were shown for different interneuron populations through all olfactory regions, with somatostatin-positive cells most strongly affected.
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Brain-like hardware, do we need it?
The brain’s ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating the brain’s processing and learning mechanisms, computing technologies strive to achieve higher levels of energy efficiency and computational performance. Although efforts to address neuromorphic solutions through hardware based on top-down CMOS-based technologies have obtained interesting results in terms of energetic efficiency improvement, the replication of brain’s self-assembled and redundant architectures is not considered in the roadmaps of data processing electronics. The exploration of solutions based on self-assembled elemental blocks to mimic biological networks’ complexity is explored in the general frame of unconventional computing and it has not reached yet a maturity stage enabling a benchmark with standard electronic approaches in terms of performances, compatibility and scalability. Here we discuss some aspects related to advantages and disadvantages in the emulation of the brain for neuromorphic hardware. We also discuss possible directions in terms of hybrid hardware solutions where self-assembled substrates coexist and integrate with conventional electronics in view of neuromorphic architectures.
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Mandarin-speaking children with different types of cochlear implant exhibit variations in the activation patterns of their central auditory processing
BackgroundCochlear implants (CIs) have the potential to facilitate auditory restoration in deaf children and contribute to the maturation of the auditory cortex. The type of CI may impact hearing rehabilitation in children with CI. We aimed to study central auditory processing activation patterns during speech perception in Mandarin-speaking pediatric CI recipients with different device characteristics.MethodsWe developed and implemented a multifeature paradigm for Mandarin pronunciation to capture mismatch negativity (MMN) responses in pediatric CI recipients, analyzed the cortical processing sources of MMN responses elicited by different stimuli, and identified significant differences in the frontal cerebral cortex activation between different types of CIs located in the corresponding brain regions according to the Anatomical Automatic Labeling (AAL) brain template. The clinical characteristics, aided hearing threshold (AHT), and speech perception accuracy (SPA) of these children were also recorded.ResultsThis study involved 32 pediatric CI recipients, with 12 (37.5%) receiving unilateral implants, 10 (31.3%) receiving bilateral implants, and 10 (31.3%) receiving bimodal stimulation. The cortical areas involved in the MMN response to various Mandarin pronunciation stimuli showed the greatest activity in the prefrontal lobe. In children with bimodal stimulation, there was noticeable activation in prefrontal cortical areas. Children with unilateral and bilateral implants also showed activation of the prefrontal cortex, but the activation strength was relatively reduced. The activation of cortical areas did not consistently appear stronger in children with bilateral implants than in those with unilateral implants. Consonant and intensity stimuli showed greater activation, whereas duration and vowel stimuli showed weaker activation. Significant differences in frontal cerebral cortex activation between different types of CIs were predominantly observed in the superior frontal gyrus.ConclusionBimodal stimulation should be considered whenever possible to maximize auditory benefits. For deaf children without any residual hearing, bilateral implantation is the best choice. Unilateral implantation is not as detrimental as previously thought for deaf children. Early cochlear implantation, comprehensive auditory training, and better adaptation to CI devices can efficiently compensate for unilateral hearing limitations.
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Neurological manifestations of encephalitic alphaviruses, traumatic brain injuries, and organophosphorus nerve agent exposure
Encephalitic alphaviruses (EEVs), Traumatic Brain Injuries (TBI), and organophosphorus nerve agents (NAs) are three diverse biological, physical, and chemical injuries that can lead to long-term neurological deficits in humans. EEVs include Venezuelan, eastern, and western equine encephalitis viruses. This review describes the current understanding of neurological pathology during these three conditions, provides a comparative review of case studies vs. animal models, and summarizes current therapeutics. While epidemiological data on clinical and pathological manifestations of these conditions are known in humans, much of our current mechanistic understanding relies upon animal models. Here we review the animal models findings for EEVs, TBIs, and NAs and compare these with what is known from human case studies. Additionally, research on NAs and EEVs is limited due to their classification as high-risk pathogens (BSL-3) and/or select agents; therefore, we leverage commonalities with TBI to develop a further understanding of the mechanisms of neurological damage. Furthermore, we discuss overlapping neurological damage mechanisms between TBI, NAs, and EEVs that highlight novel medical countermeasure opportunities. We describe current treatment methods for reducing neurological damage induced by individual conditions and general neuroprotective treatment options. Finally, we discuss perspectives on the future of neuroprotective drug development against long-term neurological sequelae of EEVs, TBIs, and NAs.
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Mapping the structure of biomarkers in autism spectrum disorder: a review of the most influential studies
BackgroundAutism spectrum disorder is a distinctive developmental condition which is caused by an interaction between genetic vulnerability and environmental factors. Biomarkers play a crucial role in understanding disease characteristics for diagnosis, prognosis, and treatment. This study employs bibliometric analysis to identify and review the 100 top-cited articles’ characteristics, current research hotspots and future directions of autism biomarkers.MethodsA comprehensive search of autism biomarkers studies was retrieved from the Web of Science Core Collection database with a combined keyword search strategy. A comprehensive analysis of the top 100 articles was conducted with CiteSpace, VOSviewer, and Excel, including citations, countries, authors, and keywords.ResultsThe top 100 cited studies were published between 1988 and 2021, with the United States led in productivity. Core biomarkers such as genetics, children, oxidative stress, and mitochondrial dysfunction are well-established. Potential trends for future research may include brain studies, metabolomics, and associations with other psychiatric disorders.ConclusionThis pioneering bibliometric analysis provides a comprehensive compilation of the 100 most-cited studies on autism, which not only offers a valuable resource for doctors, and researchers but shedding insights into current shortcomings and future endeavors. Future research should prioritize the application of emerging technologies for biomarkers, longitudinal study of biomarkers, and specificity of autism biomarkers to advance the precision of ASD diagnosis and treatment.
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Comprehensive predictive model for cerebral microbleeds: integrating clinical and biochemical markers
BackgroundCerebral Microbleeds (CMBs) serve as critical indicators of cerebral small vessel disease and are strongly associated with severe neurological disorders, including cognitive impairments, stroke, and dementia. Despite the importance of diagnosing and preventing CMBs, there is a significant lack of effective predictive tools in clinical settings, hindering comprehensive assessment and timely intervention.ObjectiveThis study aims to develop a robust predictive model for CMBs by integrating a broad range of clinical and laboratory parameters, enhancing early diagnosis and risk stratification.MethodsWe analyzed extensive data from 587 neurology inpatients using advanced statistical techniques, including Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression. Key predictive factors such as Albumin/Globulin ratio, gender, hypertension, homocysteine levels, Neutrophil to HDL Ratio (NHR), and history of stroke were evaluated. Model validation was performed through Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).ResultsThe model demonstrated strong predictive performance with significant clinical applicability. Key predictors identified include the Albumin/Globulin ratio, homocysteine levels, and NHR, among others. Validation metrics such as the area under the ROC curve (AUC) and decision curve analysis confirmed the model’s utility in predicting CMBs, highlighting its potential for clinical implementation.ConclusionThe comprehensive predictive model developed in this study offers a significant advancement in the personalized management of patients at risk for CMBs. By addressing the gap in effective predictive tools, this model facilitates early diagnosis and targeted intervention, potentially reducing the incidence of stroke and cognitive impairments associated with cerebral microbleeds. Our findings advocate for a more nuanced approach to cerebrovascular disease management, emphasizing the importance of multi-factorial risk profiling.
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The cellular and extracellular proteomic signature of human dopaminergic neurons carrying the LRRK2 G2019S mutation
BackgroundExtracellular vesicles are easily accessible in various biofluids and allow the assessment of disease-related changes in the proteome. This has made them a promising target for biomarker studies, especially in the field of neurodegeneration where access to diseased tissue is very limited. Genetic variants in the LRRK2 gene have been linked to both familial and sporadic forms of Parkinson’s disease. With LRRK2 inhibitors entering clinical trials, there is an unmet need for biomarkers that reflect LRRK2-specific pathology and target engagement.MethodsIn this study, we used induced pluripotent stem cells derived from a patient with Parkinson’s disease carrying the LRRK2 G2019S mutation and an isogenic gene-corrected control to generate human dopaminergic neurons. We isolated extracellular vesicles and neuronal cell lysates and characterized their proteomic signature using data-independent acquisition proteomics. Then, we performed differential expression analysis to identify dysregulated proteins in the mutated line. We used Metascape and gene ontology enrichment analysis on the dysregulated proteomes to identify changes in associated functional networks.ResultsWe identified 595 significantly differentially regulated proteins in extracellular vesicles and 3,205 in cell lysates. We visualized functionally relevant protein–protein interaction networks and identified key regulators within the dysregulated proteomes. Using gene ontology, we found a close association with biological processes relevant to neurodegeneration and Parkinson’s disease. Finally, we focused on proteins that were dysregulated in both the extracellular and cellular proteomes. We provide a list of ten biomarker candidates that are functionally relevant to neurodegeneration and linked to LRRK2-associated pathology, for example, the sonic hedgehog signaling molecule, a protein that has tightly been linked to LRRK2-related disruption of cilia function.ConclusionIn conclusion, we characterized the cellular and extracellular proteome of dopaminergic neurons carrying the LRRK2 G2019S mutation and proposed an experimentally based list of biomarker candidates for future studies.
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