Different Dopaminergic Dysfunctions Underlying Parkinsonian Akinesia and Tremor

Unraveling sex differences in Parkinson's disease through explainable machine learning

Sex differences affect Parkinson's disease (PD) development and manifestation. Yet, current PD identification and treatments underuse these distinctions. Sex-focused PD literature often prioritizes prevalence rates over feature importance analysis. However, underlying aspects could make a feature significant for predicting PD, despite its score. Interactions between features require consideration, as do distinctions between scoring disparities and actual feature importance. For instance, a higher score in males for a certain feature doesn't necessarily mean it's less important for characterizing PD in females. This article proposes an explainable Machine Learning (ML) model to elucidate these underlying factors, emphasizing the importance of features. This insight could be critical for personalized medicine, suggesting the need to tailor data collection and analysis for males and females. The model identifies sex-specific differences in PD, aiding in predicting outcomes as "Healthy" or "Pathological". It adopts a system-level approach, integrating heterogeneous data - clinical, imaging, genetics, and demographics - to study new biomarkers for diagnosis. The explainable ML approach aids non-ML experts in understanding model decisions, fostering trust and facilitating interpretation of complex ML outcomes, thus enhancing usability and translational research. The ML model identifies muscle rigidity, autonomic and cognitive assessments, and family history as key contributors to PD diagnosis, with sex differences noted. The genetic variant SNCA-rs356181 may be more significant in characterizing PD in males. Interaction analysis reveals a greater occurrence of feature interplay among males compared to females. These disparities offer insights into PD pathophysiology and could guide the development of sex-specific diagnostic and therapeutic approaches.

In the Rat Midbrain, SG2NA and DJ-1 have Common Interactome, Including Mitochondrial Electron Transporters that are Comodulated Under Oxidative Stress

Scaffold proteins Striatin and SG2NA assemble kinases and phosphatases into the signalling complexes called STRIPAK. Dysfunctional STRIPAKs cause cancer, cerebral cavernous malformations, etc. DJ-1, a sensor for oxidative stress, has long been associated with the Parkinson's disease, cancer, and immune disorders. SG2NA interacts with DJ-1 and Akt providing neuroprotection under oxidative stress. To dissect the role of SG2NA and DJ-1 in neuronal pathobiology, rat midbrain extracts were immunoprecipitated with SG2NA and sixty-three interacting proteins were identified. BN-PAGE followed by the LC-MS/MS showed 1030 comigrating proteins as the potential constituents of the multimeric complexes formed by SG2NA. Forty-three proteins were common between those identified by co-immunoprecipitation and the BN-PAGE. Co-immunoprecipitation with DJ-1 identified 179 interacting partners, of which forty-one also interact with SG2NA. Among those forty-one proteins immunoprecipitated with both SG2NA and DJ-1, thirty-nine comigrated with SG2NA in the BN-PAGE, and thus are bonafide constituents of the supramolecular assemblies comprising both DJ-1 and SG2NA. Among those thirty-nine proteins, seven are involved in mitochondrial oxidative phosphorylation. In rotenone-treated rats having Parkinson's like symptoms, the levels of both SG2NA and DJ-1 increased in the mitochondria; and the association of SG2NA with the electron transport complexes enhanced. In the hemi-Parkinson's model, where the rats were injected with 6-OHDA into the midbrain, the occupancy of SG2NA and DJ-1 in the mitochondrial complexes also increased. Our study thus reveals a new family of potential STRIPAK assemblies involving both SG2NA and DJ-1, with key roles in protecting midbrain from the oxidative stress.

The Neurodegenerative Elderly Syndrome (NES) hypothesis: Alzheimer and Parkinson are two faces of the same disease

Increasing evidence suggests that Alzheimer's disease (AD) and Parkinson's disease (PD) share monoamine and alpha-synuclein (αSyn) dysfunctions, often beginning years before clinical manifestations onset. The triggers for these impairments and the causes leading these early neurodegenerative processes to become AD or PD remain unclear. We address these issues by proposing a radically new perspective to frame AD and PD: they are different manifestations of one only disease we call "Neurodegenerative Elderly Syndrome (NES)". NES goes through three phases. The seeding stage, which starts years before clinical signs, and where the part of the brain-body affected by the initial αSyn and monoamine dysfunctions, influences the future possible progression of NES towards PD or AD. The compensatory stage, where the clinical symptoms are still silent thanks to compensatory mechanisms keeping monoamine concentrations homeostasis. The bifurcation stage, where NES becomes AD or PD. We present recent literature supporting NES and discuss how this hypothesis could radically change the comprehension of AD and PD comorbidities and the design of novel system-level diagnostic and therapeutic actions.

Multiple checkpoints of protein clearance machinery are modulated by a common microRNA, miR-4813-3p, through its putative target genes: Studies employing transgenic C. elegans model

In order to maintain cellular homeostasis and a healthy state, aberrant and aggregated proteins are to be recognized and rapidly cleared from cells. Parkinson's disease, known to be associated with multiple factors; presents with impaired clearance of aggregated alpha synuclein as a key factor. We endeavored to study microRNA molecules with potential role on regulating multiple checkpoints of protein quality control within cells. Carrying out global miRNA profiling in a transgenic C. elegans model that expresses human alpha synuclein, we identified novel miRNA, miR-4813-3p, as a significantly downregulated molecule. Further studying its putative downstream target genes, we were able to mechanistically characterize six genes gbf-1, vha-5, cup-5, cpd-2, acs-1 and C27A12.7, which relate to endpoints associated with alpha synuclein expression, oxidative stress, locomotory behavior, autophagy and apoptotic pathways. Our study reveals the novel role of miR-4813-3p and provides potential functional characterization of its putative target genes, in regulating the various pathways associated with PQC network. miR-4813-3p modulates ER^UPR, MT^UPR, autophagosome-lysosomal-pathway and the ubiquitin-proteasomal-system, making this molecule an interesting target for further studies towards therapeutically addressing multifactorial aspect of Parkinson's disease.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.

Development and validation of an instrument for measuring parkinsonian motor impairment: TRAPS-D

BACKGROUND

Parkinson's disease is incurable, idiopathic, degenerative, and progressive, and affects about 1% of the elderly population. Multidisciplinary clinical treatment is the best and most adopted therapeutic option, while surgical treatment is used in less than 15% of those affected. In practice, there is a lack of reliable and validated scales for measuring motor impairment, and monitoring and screening for surgical indications.

OBJECTIVE

To develop and validate an instrument for measuring parkinsonian motor impairment in candidates for neurosurgical treatment.

METHOD

The development and validation methods followed published guidelines. The first part was the choice of domains that would make up the construct: cardinal signs of disease (tremor, rigidity (stiffness), posture/balance/gait, hypokinesia/akinesia, and speech), along with pain and dyskinesia. A multi-professional working group prepared an initial pilot instrument. Ten renowned specialists evaluated, judged, and suggested modifications to the instrument. The second phase was the evaluation of the content of each domain and the respective ability to classify commitment intensity. The third phase was the correction of the main flaws detected and new submission to the board. The instrument was applied to 41 candidates for neurosurgical treatment in two situations: with and without medication RESULTS: The final form received 100% agreement from the judges. Its average time for application was 8 min. It was very responsive (p = 0.001, Wilcoxon) in different situations (On-Off).

CONCLUSION

TRASP-D is a valid instrument for measuring motor impairment in patients with Parkinson's disease who are candidates for neurosurgical treatment. It allows measurement in multiple domains with reliability and sensitivity.

Increasing Serotonin to Reduce Parkinsonian Tremor

While current dopamine-based drugs seem to be effective for most Parkinson's disease (PD) motor dysfunctions, they produce variable responsiveness for resting tremor. This lack of consistency could be explained by considering recent evidence suggesting that PD resting tremor can be divided into different partially overlapping phenotypes based on the dopamine response. These phenotypes may be associated with different pathophysiological mechanisms produced by a cortical-subcortical network involving even non-dopaminergic areas traditionally not directly related to PD. In this study, we propose a bio-constrained computational model to study the neural mechanisms underlying a possible type of PD tremor: the one mainly involving the serotoninergic system. The simulations run with the model demonstrate that a physiological serotonin increase can partially recover dopamine levels at the early stages of the disease before the manifestation of overt tremor. This result suggests that monitoring serotonin concentration changes could be critical for early diagnosis. The simulations also show the effectiveness of a new pharmacological treatment for tremor that acts on serotonin to recover dopamine levels. This latter result has been validated by reproducing existing data collected with human patients.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Neurorobotic Models of Neurological Disorders: A Mini Review

Modeling is widely used in biomedical research to gain insights into pathophysiology and treatment of neurological disorders but existing models, such as animal models and computational models, are limited in generalizability to humans and are restricted in the scope of possible experiments. Robotics offers a potential complementary modeling platform, with advantages such as embodiment and physical environmental interaction yet with easily monitored and adjustable parameters. In this review, we discuss the different types of models used in biomedical research and summarize the existing neurorobotics models of neurological disorders. We detail the pertinent findings of these robot models which would not have been possible through other modeling platforms. We also highlight the existing limitations in a wider uptake of robot models for neurological disorders and suggest future directions for the field.

A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

We propose to investigate brain electrophysiological alterations associated with Parkinson’s disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson’s disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.

A Basal Ganglia Computational Model to Explain the Paradoxical Sensorial Improvement in the Presence of Huntington's Disease

The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington's disease (HD) affects this brain area, it still remains unclear how HD patients achieve paradoxical improvement in sensorial discrimination tasks. This paper presents a computational model of the BG including the main nuclei and the typical firing properties of their neurons. The BG model has been embedded within an auditory signal detection task. We have emulated the effect that the altered levels of dopamine and the degree of HD affectation have in information processing at different layers of the BG, and how these aspects shape transient and steady states differently throughout the selection task. By extracting the independent components of the BG activity at different populations, it is evidenced that early and medium stages of HD affectation may enhance transient activity in the striatum and the substantia nigra pars reticulata. These results represent a possible explanation for the paradoxical improvement that HD patients present in discrimination task performance. Thus, this paper provides a novel understanding on how the fast dynamics of the BG network at different layers interact and enable transient states to emerge throughout the successive neuron populations.

A biologically constrained spiking neural network model of the primate basal ganglia with overlapping pathways exhibits action selection

Action selection has been hypothesized to be a key function of the basal ganglia, yet the nuclei involved, their interactions and the importance of the direct/indirect pathway segregation in such process remain debated. Here, we design a spiking computational model of the monkey basal ganglia derived from a previously published population model, initially parameterized to reproduce electrophysiological activity at rest and to embody as much quantitative anatomical data as possible. As a particular feature, both models exhibit the strong overlap between the direct and indirect pathways that has been documented in non-human primates. Here, we first show how the translation from a population to an individual neuron model was achieved, with the addition of a minimal number of parameters. We then show that our model performs action selection, even though it was built without any assumption on the activity carried out during behaviour. We investigate the mechanisms of this selection through circuit disruptions and found an instrumental role of the off-centre/on-surround structure of the MSN-STN-GPi circuit, as well as of the MSN-MSN and FSI-MSN projections. This validates their potency in enabling selection. We finally study the pervasive centromedian and parafascicular thalamic inputs that reach all basal ganglia nuclei and whose influence is therefore difficult to anticipate. Our model predicts that these inputs modulate the responsiveness of action selection, making them a candidate for the regulation of the speed-accuracy trade-off during decision-making.