Simulating combined monoaminergic depletions in a PD animal model through a bio-constrained differential equations system.

Introduction: Historically, Parkinson's Disease (PD) research has focused on the dysfunction of dopamine-producing cells in the substantia nigra pars compacta, which is linked to motor regulation in the basal ganglia. Therapies have mainly aimed at restoring dopamine (DA) levels, showing effectiveness but variable outcomes and side effects. Recent evidence indicates that PD complexity implicates disruptions in DA, noradrenaline (NA), and serotonin (5-HT) systems, which may underlie the variations in therapy effects. Methods: We present a system-level bio-constrained computational model that comprehensively investigates the dynamic interactions between these neurotransmitter systems. The model was designed to replicate experimental data demonstrating the impact of NA and 5-HT depletion in a PD animal model, providing insights into the causal relationships between basal ganglia regions and neuromodulator release areas. Results: The model successfully replicates experimental data and generates predictions regarding changes in unexplored brain regions, suggesting avenues for further investigation. It highlights the potential efficacy of alternative treatments targeting the locus coeruleus and dorsal raphe nucleus, though these preliminary findings require further validation. Sensitivity analysis identifies critical model parameters, offering insights into key factors influencing brain area activity. A stability analysis underscores the robustness of our mathematical formulation, bolstering the model validity. Discussion: Our holistic approach emphasizes that PD is a multifactorial disorder and opens promising avenues for early diagnostic tools that harness the intricate interactions among monoaminergic systems. Investigating NA and 5-HT systems alongside the DA system may yield more effective, subtype-specific therapies. The exploration of multisystem dysregulation in PD is poised to revolutionize our understanding and management of this complex neurodegenerative disorder.

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.

Non-invasive brain stimulation for patients and healthy subjects: Current challenges and future perspectives.

Non-invasive brain stimulation (NIBS) techniques have a rich historical background, yet their utilization has witnessed significant growth only recently. These techniques encompass transcranial electrical stimulation and transcranial magnetic stimulation, which were initially employed in neuroscience to explore the intricate relationship between the brain and behaviour. However, they are increasingly finding application in research contexts as a means to address various neurological, psychiatric, and neurodegenerative disorders. This article aims to fulfill two primary objectives. Firstly, it seeks to showcase the current state of the art in the clinical application of NIBS, highlighting how it can improve and complement existing treatments. Secondly, it provides a comprehensive overview of the utilization of NIBS in augmenting the brain function of healthy individuals, thereby enhancing their performance. Furthermore, the article delves into the points of convergence and divergence between these two techniques. It also addresses the existing challenges and future prospects associated with NIBS from ethical and research standpoints.

Machine learning for exploring neurophysiological functionality in multiple sclerosis based on trigeminal and hand blink reflexes.

Brainstem dysfunctions are very common in Multiple Sclerosis (MS) and are a critical predictive factor for future disability. Brainstem functionality can be explored with blink reflexes, subcortical responses consisting in a blink following a peripheral stimulation. Some reflexes are already employed in clinical practice, such as Trigeminal Blink Reflex (TBR). Here we propose for the first time in MS the exploration of Hand Blink Reflex (HBR), which size is modulated by the proximity of the stimulated hand to the face, reflecting the extension of the peripersonal space. The aim of this work is to test whether Machine Learning (ML) techniques could be used in combination with neurophysiological measurements such as TBR and HBR to improve their clinical information and potentially favour the early detection of brainstem dysfunctionality. HBR and TBR were recorded from a group of People with MS (PwMS) with Relapsing-Remitting form and from a healthy control group. Two AdaBoost classifiers were trained with TBR and HBR features each, for a binary classification task between PwMS and Controls. Both classifiers were able to identify PwMS with an accuracy comparable and even higher than clinicians. Our results indicate that ML techniques could represent a tool for clinicians for investigating brainstem functionality in MS. Also, HBR could be promising when applied in clinical practice, providing additional information about the integrity of brainstem circuits potentially favouring early diagnosis.

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.

A multi-expert ensemble system for predicting Alzheimer transition using clinical features.

Alzheimer's disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

Emotions Modulate Affordances-Related Motor Responses: A Priming Experiment.

Traditionally, research on affordances and emotions follows two separate routes. For the first time, this article explicitly links the two phenomena by investigating whether, in a discrimination task (artifact vs. natural object), the motivational states induced by emotional images can modulate affordances-related motor response elicited by dangerous and neutral graspable objects. The results show faster RTs: (i) for both neutral and dangerous objects with neutral images; (ii) for dangerous objects with pleasant images; (iii) for neutral objects with unpleasant images. Overall, these data support a significant effect of emotions on affordances. The article also proposes a brain neural network underlying emotions and affordance interplay.

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal ( https://cptac-data-portal.georgetown.edu/ ).

Motor Inhibition to Dangerous Objects: Electrophysiological Evidence for Task-dependent Aversive Affordances.

Previous work suggests that perception of an object automatically facilitates actions related to object grasping and manipulation. Recently, the notion of automaticity has been challenged by behavioral studies suggesting that dangerous objects elicit aversive affordances that interfere with encoding of an object's motor properties; however, related EEG studies have provided little support for these claims. We sought EEG evidence that would support the operation of an inhibitory mechanism that interferes with the motor encoding of dangerous objects, and we investigated whether such mechanism would be modulated by the perceived distance of an object and the goal of a given task. EEGs were recorded by 24 participants who passively perceived dangerous and neutral objects in their peripersonal, boundary, or extrapersonal space and performed either a reachability judgment task or a categorization task. Our results showed that greater attention, reflected in the visual P1 potential, was drawn by dangerous and reachable objects. Crucially, a frontal N2 potential, associated with motor inhibition, was larger for dangerous objects only when participants performed a reachability judgment task. Furthermore, a larger parietal P3b potential for dangerous objects indicated the greater difficulty in linking a dangerous object to the appropriate response, especially when it was located in the participants' extrapersonal space. Taken together, our results show that perception of dangerous objects elicits aversive affordances in a task-dependent way and provides evidence for the operation of a neural mechanism that does not code affordances of dangerous objects automatically, but rather on the basis of contextual information.

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.

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.

Computational Modeling of Catecholamines Dysfunction in Alzheimer's Disease at Pre-Plaque Stage.

BACKGROUND: Alzheimer's disease (AD) etiopathogenesis remains partially unexplained. The main conceptual framework used to study AD is the Amyloid Cascade Hypothesis, although the failure of recent clinical experimentation seems to reduce its potential in AD research. OBJECTIVE: A possible explanation for the failure of clinical trials is that they are set too late in AD progression. Recent studies suggest that the ventral tegmental area (VTA) degeneration could be one of the first events occurring in AD progression (pre-plaque stage). METHODS: Here we investigate this hypothesis through a computational model and computer simulations validated with behavioral and neural data from patients. RESULTS: We show that VTA degeneration might lead to system-level adjustments of catecholamine release, triggering a sequence of events leading to relevant clinical and pathological signs of AD. These changes consist first in a midfrontal-driven compensatory hyperactivation of both VTA and locus coeruleus (norepinephrine) followed, with the progression of the VTA impairment, by a downregulation of catecholamine release. These processes could then trigger the neural degeneration at the cortical and hippocampal levels, due to the chronic loss of the neuroprotective role of norepinephrine. CONCLUSION: Our novel hypothesis might contribute to the formulation of a wider system-level view of AD which might help to devise early diagnostic and therapeutic interventions.

How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning.

Several data have demonstrated that during the widely used experimental paradigm for studying associative learning, trace eye blinking conditioning (TEBC), there is a strong interaction between cerebellum and medial prefrontal cortex (mPFC). Despite this evidence, the neural mechanisms underlying this interaction are still not clear. Here, we propose a neurophysiologically plausible computational model to address this issue. The model is constrained on the basis of two critical anatomo-physiological features: (i) the cerebello-cortical organization through two circuits, respectively, targeting M1 and mPFC; (ii) the different timing in the plasticity mechanisms of these parallel circuits produced by the granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC. In particular, a greater trace interval produces greater plasticity changes at the slow path synapses involving mPFC with respect to plasticity changes at the fast path involving M1. As a consequence, the greater is the trace interval, the stronger is the mPFC involvement. The model has been validated by reproducing data collected through recent real mice experiments.

Different Dopaminergic Dysfunctions Underlying Parkinsonian Akinesia and Tremor.

Although the occurrence of Parkinsonian akinesia and tremor is traditionally associated to dopaminergic degeneration, the multifaceted neural processes that cause these impairments are not fully understood. As a consequence, current dopamine medications cannot be tailored to the specific dysfunctions of patients with the result that generic drug therapies produce different effects on akinesia and tremor. This article proposes a computational model focusing on the role of dopamine impairments in the occurrence of akinesia and resting tremor. The model has three key features, to date never integrated in a single computational system: (a) an architecture constrained on the basis of the relevant known system-level anatomy of the basal ganglia-thalamo-cortical loops; (b) spiking neurons with physiologically-constrained parameters; (c) a detailed simulation of the effects of both phasic and tonic dopamine release. The model exhibits a neural dynamics compatible with that recorded in the brain of primates and humans. Moreover, it suggests that akinesia might involve both tonic and phasic dopamine dysregulations whereas resting tremor might be primarily caused by impairments involving tonic dopamine release and the responsiveness of dopamine receptors. These results could lead to develop new therapies based on a system-level view of the Parkinson's disease and targeting phasic and tonic dopamine in differential ways.

The super-learning hypothesis: Integrating learning processes across cortex, cerebellum and basal ganglia.

Despite wide evidence suggesting anatomical and functional interactions between cortex, cerebellum and basal ganglia, the learning processes operating within them --often viewed as respectively unsupervised, supervised and reinforcement learning-- are studied in isolation, neglecting their strong interdependence. We discuss how those brain areas form a highly integrated system combining different learning mechanisms into an effective super-learning process supporting the acquisition of flexible motor behaviour. The term "super-learning" does not indicate a new learning paradigm. Rather, it refers to the fact that different learning mechanisms act in synergy as they: (a) affect neural structures often relying on the widespread action of neuromodulators; (b) act within various stages of cortical/subcortical pathways that are organised in pipeline to support multiple sensation-to-action mappings operating at different levels of abstraction; (c) interact through the reciprocal influence of the output compartments of different brain structures, most notably in the cerebello-cortical and basal ganglia-cortical loops. Here we articulate this new hypothesis and discuss empirical evidence supporting it by specifically referring to motor adaptation and sequence learning.

Action Observation With Dual Task for Improving Cognitive Abilities in Parkinson's Disease: A Pilot Study.

Action observation therapy (AOT) has been recently proposed as a new rehabilitation approach for treatment of motor deficits in Parkinson's disease. To date, this approach has never been used to deal with cognitive deficits (e.g., deficits in working memory, attention), which are impairments that are increasingly recognized in Parkinsonian patients. Typically, patients affected by these dysfunctions have difficulty filtering out irrelevant information and tend to lose track of the task goal. In this paper, we propose that AOT may also be used to improve cognitive abilities of Parkinsonian patients if it is used within a dual task framework. We articulate our hypothesis by pivoting on recent findings and on preliminary results that were obtained through a pilot study that was designed to test the efficacy of a long-term rehabilitation program that, for the first time, uses AOT within a dual task framework for treating cognitive deficits in patients with Parkinson's disease. Ten Parkinson's disease patients underwent a 45-min treatment that consisted in watching a video of an actor performing a daily-life activity and then executing it while performing distractive tasks (AOT with dual task). The treatment was repeated three times per week for a total of 4 weeks. Patients' cognitive/motor features were evaluated through standard tests four times: 1 month before treatment, the first and the last day of treatment and 1 month after treatment. The results show that this approach may provide relevant improvements in cognitive aspects related to working memory (verbal and visuospatial memory) and attention. We discuss these results by pivoting on literature on action observation and recent literature demonstrating that the dual task method can be used to stimulate cognition and concentration. In particular, we propose that using AOT together with a dual task may train the brain systems supporting executive functions through two mechanisms: (i) stimulation of goal setting within the mirror neuron system through action observation and (ii) working memory and persistent goal maintenance through dual task stimuli.

Dysfunctions of the basal ganglia-cerebellar-thalamo-cortical system produce motor tics in Tourette syndrome.

Motor tics are a cardinal feature of Tourette syndrome and are traditionally associated with an excess of striatal dopamine in the basal ganglia. Recent evidence increasingly supports a more articulated view where cerebellum and cortex, working closely in concert with basal ganglia, are also involved in tic production. Building on such evidence, this article proposes a computational model of the basal ganglia-cerebellar-thalamo-cortical system to study how motor tics are generated in Tourette syndrome. In particular, the model: (i) reproduces the main results of recent experiments about the involvement of the basal ganglia-cerebellar-thalamo-cortical system in tic generation; (ii) suggests an explanation of the system-level mechanisms underlying motor tic production: in this respect, the model predicts that the interplay between dopaminergic signal and cortical activity contributes to triggering the tic event and that the recently discovered basal ganglia-cerebellar anatomical pathway may support the involvement of the cerebellum in tic production; (iii) furnishes predictions on the amount of tics generated when striatal dopamine increases and when the cortex is externally stimulated. These predictions could be important in identifying new brain target areas for future therapies. Finally, the model represents the first computational attempt to study the role of the recently discovered basal ganglia-cerebellar anatomical links. Studying this non-cortex-mediated basal ganglia-cerebellar interaction could radically change our perspective about how these areas interact with each other and with the cortex. Overall, the model also shows the utility of casting Tourette syndrome within a system-level perspective rather than viewing it as related to the dysfunction of a single brain area.

Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex.

Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. This consensus paper gathers diverse recent views on a variety of important roles played by the cerebellum within the cerebello-basal ganglia-thalamo-cortical system across a range of motor and cognitive functions. The paper includes theoretical and empirical contributions, which cover the following topics: recent evidence supporting the dynamical interplay between cerebellum, basal ganglia, and cortical areas in humans and other animals; theoretical neuroscience perspectives and empirical evidence on the reciprocal influences between cerebellum, basal ganglia, and cortex in learning and control processes; and data suggesting possible roles of the cerebellum in basal ganglia movement disorders. Although starting from different backgrounds and dealing with different topics, all the contributors agree that viewing the cerebellum, basal ganglia, and cortex as an integrated system enables us to understand the function of these areas in radically different ways. In addition, there is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions. The paper reports the most advanced perspectives on the role of the cerebellum within the cerebello-basal ganglia-thalamo-cortical system and illustrates other elements of consensus as well as disagreements and open questions in the field.

The architecture challenge: Future artificial-intelligence systems will require sophisticated architectures, and knowledge of the brain might guide their construction.

In this commentary, we highlight a crucial challenge posed by the proposal of Lake et al. to introduce key elements of human cognition into deep neural networks and future artificial-intelligence systems: the need to design effective sophisticated architectures. We propose that looking at the brain is an important means of facing this great challenge.

Action observation and motor imagery for rehabilitation in Parkinson's disease: A systematic review and an integrative hypothesis.

This article discusses recent evidence supporting the use of action observation therapy and motor imagery practice for rehabilitation of Parkinson's disease. A main question that emerges from the review regards the different effectiveness of these approaches and the possibility of integrating them into a single method to enhance motor behaviour in subjects with Parkinson's disease. In particular, the reviewed studies suggest that action observation therapy can have a positive effect on motor facilitation of patients and that a long-term rehabilitation program based on action observation therapy or motor imagery practice can bring some benefit on their motor recovery. Moreover, the paper discusses how the research on the combined use of action observation and motor imagery for motor improvements in healthy subjects may encourage the combined use of action observation therapy and motor imagery practice for therapeutic aims in Parkinson's disease. To date, this hypothesis has never been experimented.