research paper about parkinson's disease

Hot Topics in Recent Parkinson's Disease Research: Where We are and Where We Should Go

Affiliations.

1 Liaoning Provincial Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China.
2 Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China.
3 Liaoning Provincial Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China. [email protected].
4 Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, 116021, China. [email protected].
5 Institute of Neurology, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, Medical School of the University of Electronic Science and Technology of China, Chengdu, 610072, China. [email protected].
PMID: 34313916
PMCID: PMC8643373
DOI: 10.1007/s12264-021-00749-x

Parkinson's disease (PD), the second most common neurodegenerative disease, is clinically characterized by both motor and non-motor symptoms. Although overall great achievements have been made in elucidating the etiology and pathogenesis of PD, the exact mechanisms of this complicated systemic disease are still far from being clearly understood. Consequently, most of the currently-used diagnostic tools and therapeutic options for PD are symptomatic. In this perspective review, we highlight the hot topics in recent PD research for both clinicians and researchers. Some of these hot topics, such as sleep disorders and gut symptoms, have been neglected but are currently emphasized due to their close association with PD. Following these research directions in future PD research may help understand the nature of the disease and facilitate the discovery of new strategies for the diagnosis and therapy of PD.

Keywords: Biomarkers; Ferroptosis; Genetics; Gut-brain axis; Neuroinflammation; Parkinson’s disease; Sleep disorder.

Publication types

Neurodegenerative Diseases*
Parkinson Disease* / therapy
Sleep Wake Disorders* / etiology
Sleep Wake Disorders* / therapy

Open access
Published: 23 May 2023

A detailed review of pathophysiology, epidemiology, cellular and molecular pathways involved in the development and prognosis of Parkinson's disease with insights into screening models

Ayesha Sayyaed 1 ,
Nikita Saraswat ORCID: orcid.org/0000-0001-6009-6700 1 ,
Neeraj Vyawahare 1 &
Ashish Kulkarni 1

Bulletin of the National Research Centre volume 47 , Article number: 70 ( 2023 ) Cite this article

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Parkinson's disease is a neurodegenerative disorder of the central nervous system that is one of the mental disorders that cause tremors, rigidity, and bradykinesia. Many factors determine the development of disease. A comprehensive physical examination and medical history of the patient should be part of the differential diagnosis for Parkinson’s disease (PD). According to epidemiology, Parkinson’s disease majorly affects elderly persons and frequency of affecting men is more as compared to women where the worldwide burden of Parkinson’s disease (PD) increased more than twice in the past 20 years.

Main body of the abstract

In this review paper, we discussed screening models, recent clinical trials, cellular and molecular pathways, and genetic variants (mutations) responsible for induction of Parkinson’s disease. The paper also aims to study the pathophysiology, epidemiology, general mechanism of action, risk factors, neurotoxin models, cellular and molecular pathway, clinical trials genetic variants of Parkinson’s disease. These models correspond to our research into the pathogenesis of Parkinson’s disease. The collected data for the review have been obtained by studying the combination of research and review papers from different databases such as PubMed, Elsevier, Web of Science, Medline, Science Direct, Medica Database, Elton B. Stephens Company (EBSCO), and Google open-access publications from the years 2017–2023, using search keywords such as “Cellular and molecular pathways, Clinical trials, Genetic mutation, Genetic models, Neurotoxin, Parkinson’s disease, Pathophysiology.”

Short Conclusion

Microglia and astrocytes can cause neuroinflammation, which can speed the course of pathogenic damage to substantia nigra (SN). The mechanism of Parkinson’s disease (PD) that causes tremors, rigidity, and bradykinesia is a decrease in striatal dopamine. Genes prominently CYP1A2 (Cytochrome P450 A2), GRIN2A , and SNCA are Parkinson’s disease (PD) hazard factor modifiers. The most well-known neurotoxin is 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), which destroys dopaminergic neurons, resulting in the development of Parkinson’s disease (PD). Dopamine auto-oxidation in dopaminergic (DA) neurons is a significant source of reactive oxygen species (ROS) that causes neuronal oxidative stress. Most common genes which when affected by mutation lead to development and progression of Parkinson’s disease (PD) are LRRK2 , SNCA (alpha-synuclein protein) , DJ-1, PRKN (Parkin protein), PINK1 , GBA1 , and VPS35 . The commonly used neurotoxin models for inducing Parkinson's disease are 6-hydroxydopamine (6-OHDA), rotenone, paraquat, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), and genetic models. Anti-apoptic drugs, gene mutation therapy, cell-based therapy, and plasma therapy were all discontinued due to insufficient efficacy. Because it is unclear how aging affects these molecular pathways and cellular functions, future research into these pathways and their interactions with one another in healthy and diseased states is essential to creating disease-specific therapeutics.

A neurodegenerative condition known as Parkinson’s disease (PD) causes tremors, stiffness, and lack of motion. As the patient becomes older, molecular changes in the substantia nigra (SN) exhibit signs of increasing neuronal loss. Particularly in the late stage of PD, non-motor symptoms are very rare, such as confusion and dysautonomia. Some individuals utilize time as a precise scientific diagnostic and can get distinctive pathologic substrates underlying the condition. Some people will reserve the word for people who suffer from idiopathic Parkinsonism brought on by Lewy body (LB) framework inclusion in SN and cells from other parts of the brain. The diagnosis of PD responds to dopaminergic medication because decreases in dopamine levels and LB are present within the remaining neurons. Those suffering with the typical fundamental signs have an incredible response to levodopa for the clinical diagnosis of PD. However, there are different types of PD in the early stage of the disease. Motor signs are challenging. There is a 24% error rate in clinical and pathological series. The difficulties in detecting this PD in its initial stages are highlighted by two studies. Researchers found that the first clinical diagnosis was appropriate in 65% of patients within 5 years of the disease's development in a prospective clinical and pathological study. Like this, 8–9% of 800 individuals with mild early-onset PD were later found to have an alternate diagnosis based on multidimensional, clinical diagnostic criteria in the tocopherol potent antioxidant treatment for PD analysis (Tolosa et al. 2021 ). The UK PD brain bank criteria are the standard clinical criteria that will increase the specificity greatly of a clinical diagnosis of the disease. However, up to 10% of people who are diagnosed with the disease during their lifetime may still require categorization at the time of death (Sonustun et al. 2022 ).

Population-dependent research has found that about 20% of PD patients who have already received treatment have not yet been diagnosed with the condition, while about 15% of patients diagnosed with PD within a community don’t know the criteria which will be strong for a diagnosis for the disease (Bai et al. 2021 May). The most frequent misdiagnosis in clinical morphological research concerns different types of degenerative Parkinsonism, such as multisystem atrophy or degeneration, degenerative supranuclear palsy. Recent studies in clinical PD have shown that extensive tremors, (visual) hallucinations, and cognitive fluctuations are among the other common features to distinguish between dementia and PD with LB (Perren et al. 2020 ).

Here, we critically evaluate the capability of further investigation for the diagnosis and therapy for patient’s with PD by reviewing published data on the clinical differential diagnosis for different types of Parkinsonism. Further, craniocerebral trauma and exposure to pesticide and fungicides, which include paraquat and rotenone, as well as imperative frightening device infection seem to be related to the pathogenic nature of PD (Senturk 2020 ).

However, we have recognized that nearly 10% of genetic cases lead to the development of PD. We have also discussed some of the more common genetic PD rodent models in this paper. Since numerous scientists thought herbicides and pesticides could increase the symptoms of PD, lots of research was conducted to evaluate several elements of paraquat and rotenone in animal models (Liu et al. 2020 ). Levodopa is the gold-standard medication for treating PD. It is a precursor for dopamine that can cross the blood–brain barrier (BBB). There are several medications that are frequently used in combination with L-dopa, and they are classified based on how they work to increase dopamine production; these medications include monoamine oxidase-B (MAO-B), catechol-O-methyl transferase (COMT) inhibitors as well as dopaminergic agonists, such as amantadine (Koga et al. 2021 ). The motor symptoms of PD can be recovered through pharmacological treatment. However, in addition to several motor control elements being resistant to pharmacological treatment, the effectiveness of dopaminergic medicines diminishes with time (Mylius et al. 2021 ). Moreover, current therapies only work to relieve symptoms and cannot prevent the further development of disease (Pereira et al. 2019 ).

In recent years, neurotrophic element therapy and cellular transplantation have become innovative therapies for those suffering from PD. However, the common of these methods involves extremely invasive localization surgery, which has risks. Neuropharmacological remedies and workout are complementary, and it generates more interest as a PD method of treatment. Ultimately, a slew of large-scale epidemiological research indicated that exercise is good for PD. Lau et al. revealed that workout might reduce chances of developing neurological impairments in PD (Feng et al. 2020 ). Exercise can improve motor and nonmotor signs of individuals with PD as a supplementary and alternative therapy. Different types of workout training have been included in scientific research, including gait training, cardio exercise, complementary exercise, innovative resistance training, and balance training. This might slow the disease's course and enhance its quality of life, helping a growing number of PD patients (Silva et al. 2021 ).

Materials and methods

In this paper, we have studied recent research on PD, neurotoxicity-induced models, techniques for the induction of disease, molecular pathways, therapeutic clinical trials, genetic mutation for PD. We thoroughly used search engines like PubMed, Elsevier, Web Science, Google Scholar, Science Direct, Medline Plus, Google Open Access, Europe PMC, Hub Med, Scopus, Semantic Scholar, Shodhaganga, Science Open, and ScienceDirect. Keywords search during the review were "Parkinson's disease, Neurotoxicity models, Pathophysiology in PD, Clinical trials in PD, Genetic mutation in PD, Cellular and molecular pathways in PD, Neurodegenerative disease, Epidemiology of PD, Central nervous system, Oxidative stress in PD, Diagnosis of PD." In addition, articles were also obtained from authentic online websites and official magazines. The review contained information from published sources on PD and its models.

Data abstraction and analysis

Literature research was made on database abstractions like PubMed and Medline Plus by using keywords like "Cellular and molecular pathways, Clinical trials, Neurotoxin and genetic models, PD, Pathophysiology." We have attempted to review the published research and reviews on PD, including its pathophysiology, epidemiology, risk factors, mechanism of action, models observed, and cellular and molecular pathways, genetic mutation. This paper also focuses on the research conducted from 2017 to 2023 on patients suffering from PD.

Epidemiology

Since the early 1800s, PD has become widely recognized and, when the disease is reported, physicians gave PD its name (Skidmore et al. 2022 ). Sometimes PD, known as "paralysis agitans," is rare in young adults, particularly individuals under 40 (Xu et al. 2020 Feb). Around 60,000 new instances of PD are reported each year, with an estimated one million Americans suffering from the condition. According to estimates, 7–10 million people worldwide have PD, which affects men 1.5 times more frequently than women. In accordance with a population-based analysis of Medicare users, those 65 and older had an average frequency of PD of 1.6% (Draoui et al. 2020 ).

Pathophysiology of Parkinson’s disease and role of Lewy bodies in dopaminergic neurons

Pathophysiology

Lewy body (LB), a pathologic characteristic of dopaminergic neurons, is improved in PD, which is described as pathophysiological as degradation or dopaminergic neuronal loss located in the SN. Several years may pass before there is any sign of a pathologic change. This lack of dopamine-producing neurons impairs motor function significantly. Aggregation of LB contains a wide range of proteins including ubiquitin alpha-synuclein and ubiquitin, which impair optimal neuron function. Aging and environmental stress, according to new guidelines, both contribute to neuropathology. Environmental contamination (e.g., pesticides), the strain of the growing-old process, or misuse of pills causes a low-stage illness inside the mind ("inflammation"), persistent. Cellular aging in neurons in the brain over time is caused by this inflammatory process (Crowley et al. 2019 ). Details about the pathophysiology of PD are shown in Fig. 1 .

Pathophysiology PD. (Parkinson’s disease is mainly characterized by the neuronal loss within the SN of the brain, which causes motor and non-motor signs such as tremors, bradykinesia, and stiffness.) (Feng et al. 2020 )

Degradation of neurons is triggered by gene mutations that encode for central nervous system (CNS) proteins. In particular, SNCA (alpha-synuclein protein) turns self-aggregates and abnormal. This inflexible alpha-synuclein is a crucial element of LB, the cellular accumulation that characterizes PD (Sun and Armstrong 2021 ). Atypical protein-disrupting systems, like the ubiquitin–proteasome device, are also made more difficult. PD can result from a variety of dysfunctional processes, such as mitochondrial disease or unique oxidative stress caused by reactive oxygen species (ROS), which results in neuronal degeneration (Roeh et al. 2019 ).

Role of substantia nigra, dopaminergic transmission, and D1, D2 receptors in Parkinson’s disease

A dopaminergic imbalance causes the novel neurodegenerative disease PD to cause mobility deficits (inhibitory D2 and excitatory D1 receptors). However, K + channels enhance these. Dopamine: In PD, the substantia nigra degenerates, destroying the nigrostriatal pathway. The neurochemical basis of PD is the ensuing reduction in striatal dopamine. The impairment in striatal dopaminergic transmission seems to depend on and be sufficient for the emergence of PD motor symptoms. Dopamine is the precursor of levodopa. Individual dopamine does not cross the BBB. Levodopa is actively transported into the brain, where levodopa is converted into dopamine in the brain. In the periphery of the brain, medication decarboxylated dopamine. Because of that, it requires a large dose of levodopa (Ishiguro et al. 2021 ). In the peripheral tissues and gastrointestinal tract (GIT), the metabolism of levodopa decreases and enhances with carbidopa and increases the bioavailability of levodopa in the CNS. Because of that, levodopa administered with carbidopa should enhance the effect of levodopa on the CNS (Jaiswal et al. 2021 ).

Clinical features in the development and progression of Parkinson’s disease

Since James PD in the nineteenth century, the important component of the disease has been motor symptoms of PD, which was later improved by Jean-Martin Charcot (Flynn et al. 2023 ). These PD signs encompass molecular stress, bradykinesia, rest tremor, gait, and postural impairment. The patients are categorized as a subtype of disease which, in having patients with PD motor actions, are heterogeneous (Marchetti 2020 ). The average time between the beginning of Parkinsonian and Parkinsonian motor signs occurrence is 12–14 years. It is an example of how that premature stage can be increased (Greener 2021 ). The pathology of PD is thought to be ongoing throughout the motor period, including dopaminergic neurons as well as the CNS and peripheral system areas in the substantia nigra paras compacta (SNpc) (Wuthrich and Rapee 2019 ).

The development of PD is described by impairment of motor function, which can primarily be treated with symptomatic treatment options. However, headaches associated with prolonged durations of symptomatic therapy, like dyskinesia, fluctuations, psychosis, motor and non-motor symptoms, dyskinesia, and psychosis, may arise as the disease progresses (Islam et al. 2021 ). Treatment-resist motor and non-motor symptoms in the last stage of PD are differentiated, with axial motor signs including movement problems, falls, gait freezing, speech difficulties, and swallowing. In the last stage of PD, non-motor signs such as symptomatic postural hypotension are frequent, constipation needing regular laxatives and urine incontinence (Neag et al. 2020 ). After 20 years with the disease, 83% of PD patients have dementia. These levodopa-resistant late-stage PD signs and symptoms significantly increase impairment and are reliable indicators of death and the necessity for hospitalization (Bjørklund et al. 2019 ).

Role of environmental, genetic, and epigenetic factors in causing Parkinson’s disease

Age is the potential risk of PD. This pattern has significant implications for public health: By 2030, the number of patients of PD is predicted to rise by more than 50% because of an aging population, as well as a rise in life expectancy globally (Masato et al. 2021 ). Environmental exposures are also risk factors for PD. These factors have been demonstrated that significantly alter the risk of PD in a meta-analysis of individual capability threat elements (Borghammer et al. 2022 ). The hypothesis that smoking may provide protection against the disease has arisen because of the factors that lower the risk of PD with smoking. The results of extensive case–control research and modern research, however, indicated that PD patients can avoid smoking more rapidly and that the correlations with smoking may be brought on by a reduced reactivity to nicotine during the prodromal stage of PD. The consequences of at least five potential population-based studies showed a negative correlation between blood urate attention and PD risk, a finding that is possibly more resolute in men than in women (Gao et al. 2020 ). Heating and manganese exposures were not related to an elevated risk of PD, according to a comparable meta-analysis. Single epidemiologic results show that exposure to solvents, especially trichloroethylene, and the use of antipsychotics by elderly people, particularly benzamides, phenothiazines, risperidone, or haloperidol, would likely increase the risk of PD (Smeyne et al. 2021 ).

Although there are multiple factors that might increase the possibility of developing PD, their complex interactions are increasing to be recognized. For instance, circumstantial findings of this study showed that exposure to brain trauma and Paraquat both increased the chances of PD (Xicoy et al. 2021). Further research has found genetic factors on environmental risk factors. For example, single-nucleotide polymorphisms in CYP1A2, that encode the isoform of Cytochrome P450 that causes metabolism of GRIN2A , that codes for a component of the N-methyl-D-aspartate (NMDA) receptor, affect the threat caused by drinking coffee. Moreover, the shape of a polymorphism blended with a repeat dinucleotide within the gene promoter of SNCA (alpha-synuclein protein) affects the risk of PD correlated with head trauma (Rocha et al. 2022 ). Environmental, genetic, epigenetic, and other risk factors for PD are shown in Fig. 2 .

Risk factors for PD (Parkinson’s disease is a central nervous system disorder that affects the movement, often including tremors, bradykinesia, and rigidity.) (Adams et al. 2023 )

Screening rodent models for induction of Parkinson’s disease

Many neurotoxin animal models are currently used in rodents and mice, such as 6-Hydroxydopamine (6-OHDA) and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP), but pesticides are primarily used. They have increased some events and symptoms that may result in PD by inducing neurotoxicity. These toxin-based PD models have some advantages and disadvantages (Tran et al. 2021 ). Table 1 shows the required dose and route of administration of neurotoxin.

Conventional 6-hydroxydopamine model in induction of Parkinson’s disease

6-Hydroxydopamine (6-OHDA) is a conventional and classical animal model for PD. Inject 6- 6-OHDA directly into the SNpc of the brain because this compound does not cross the BBB (Kayis et al. 2023 ). In the region of the mouse or rat brain, it has approximately 60% of the tyrosine hydroxylase-containing neurons present, with the lack of striatum containing the tyrosine hydroxylase-positive terminals. It is widely believed and has been tested that the tyrosine hydroxylase-advantageous terminals were dead before the tyrosine hydroxylase-advantageous neuronal cells within the SNpc, which reflect PD symptoms. Hence, most researchers have injected this 6-OHDA immediately within the SN to observe retrograde of degeneration (Belvisi et al. 2022 ). 6-OHDA enters the cytosol via the dopaminergic neuron transporter, where it may self-oxidize and induce oxidative pressure inside the cell. It has been shown the 6-OHDA and interaction, although neither leading to nor producing clumps or LB clusters like those found in PD (Fabbri et al. 2019 ). The bilateral injection of 6-OHDA into the SNpc causes not only the most severe aphasia, seizures; moreover, it is more common for people to turn to apomorphine or amphetamine after unilateral 6-OHDA can measure the severity of the precipitated striatal loss or SNpc, and this behavior to enhance the efficacy of treatments for PD (Kambey et al. 2021 ). 6-OHDA is produced in the metabolism of endogenous dopamine; hence, 6-OHDA is a neurotoxin compound; it causes lesions within the dopaminergic neurons which makes it potential for the endogenous toxin in the initiation of the PD neurodegeneration (Park et al. 2019 ). 6-OHDA induced neurotoxicity produces symptoms of PD, as shown in Fig. 3 .

6-OHDA induced PD in a specific way. It has been suggested that oxidative stress causes neuroinflammation (Luca et al. 2020 )

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-based model for inducing oxidative stress

Originally, MPTP became an unintentional visitor in the catalytic process, and while it may cause some concern in certain areas, it turned into ROS. Oxidative pressure, energy failure, infection, and energy failure have been are shown symptoms of PD (Dumurgier and Tzourio 2020 ). MPTP is the popular animal model of PD. MPTP has induced neurotoxicity in PD and shows all of the symptoms of PD in guinea pigs, monkeys, and other animal models, as well as a specific range of signs and symptoms observed in mice models, but there is no longer in rodents because rats were resistant to the MPTP (Neshige et al. 2021 ).

Role of Rotenone in Parkinson’s disease induction by inducing the synthesis of Lewy bodies, inflammation, and alpha-synuclein aggregation

Rotenone is an insecticide as well as herbicide; as compared to paraquat it is a pure herbicide. It easily crosses BBB as well as is also highly lipophilic. Rotenone induces all the symptoms of PD, including behavioral changes, inflammation, complex-I blockage, α-synuclein aggregation, development of LB, digestive issues, and oxidative stress (Jia et al. 2020 ). This model's apparent strength is that it has been shown to produce α-synuclein aggregation and LB formation. While using rotenone as a PD model enhances dopaminergic neuron (DA) oxidation, there is little proof that it leads to degradation of the dopaminergic pathway (Yin et al. 2021 ). The mechanism of rotenone as a neurotoxicity inducer in PD is shown in Fig. 4 .

Rotenone-induced ROS generation and cell death are depicted schematically as the causes of PD (Adamson et al. 2022 )

Methamphetamine in substantia nigra paras compacta neurodegeneration

Methamphetamine is a derivative of amphetamine; some effects such as induced neurotoxic effects on the CNS lead to some structural changes. Numerous research studies have shown that selective damage to serotonergic nerves or dopaminergic nerve endings leads to neuronal loss in rodents after hypothermia (Guo et al. 2022 ) though it was not a universally accepted hypothesis. These genes ( LRRK2 and SNCA , autosomal-dominant PD; PRKN, PINK1, DJ - 1 , and autosomal–recessive PD) are potential and prominent therapeutic targets in animal models. We first need to understand how these animal models work to that extent. For example, neither of the above mutations are knocked out or overexpressed in humans (Hamed et al. 2019 ). In accordance with this approach, a protein's degree of expression might contain the key to understanding the nature of that protein. Research has demonstrated that wiping out alpha-synuclein has no effect on DA retention or development (Sitzia 202 2).

Autosomal–recessive PD is caused by several mutations. These are PINK1 (mitochondrial-localized enzyme and new kinase 1 that are stimulated by tensin isoforms), Parkin (20% of individuals with early-onset PD and about 50% of gene mutations of PD), and DJ-1 (an oxidation–reduction reaction-sensitive antioxidant regulator and molecular stress). Rodent models of these genes do not show neurodegeneration. Recent reports show that exogenous Parkin depletion within adult mice is associated with the SNpc neurodegeneration. Therefore, the lack of neurotoxicity in rodents may be because rodents may have protective mechanisms that prevent the development of PD symptoms in these models (Palasz et al. 2019 ).

Pesticide paraquat and its damage to DNA

Epidemiological studies indicate that using pesticides increases the symptoms of PD, but since only 95 cases of PD have been associated with paraquat poisoning, this association may be very hypothetical in the case of paraquat (Agnihotri and Aruoma 2020 ). In agriculture, paraquat is frequently employed. Pesticide is used as a weed killer because paraquat causes damage to deoxyribonucleic acid (DNA), proteins, ribonucleic acid (RNA), and lipids through oxidative stress caused by redox reaction. This process also produces ROS, including the superoxide radical, hydrogen peroxide, and radical. Recent research on paraquat's effects on the nigrostriatal DA system is somewhat contradictory (Martínez-Chacón et al. 2021 ). Diagrammatic illustration mechanism of induction of neurotoxicity by paraquat in PD is shown in Fig. 5 .

Illustration of the paraquat-induced neurotoxicity, ROS production, and c-Jun N-terminal kinase (JNK) activation that led to the dopaminergic cells' neuronal loss and PD-like symptoms (Colle and Farina 2021 )

Mutation-based genetic models for inducing Parkinson’s disease

The "new kids on the block" are certainly genetic models of PD. Even though PD was once thought to be a "sporadic" non-genetic condition, genetic alterations are uncommon and only account for roughly 10% of PD patients. Furthermore, DJ-1 , alpha-synuclein, LRRK2 autosomal-dominant PD and PINK1 -recessive PD, are significant genes which undergo mutations to cause PD thus are potential targets for therapy. The complexity of this PD is becoming more apparent, so we must first comprehend how these animal models function. For example, neither of the mentioned mutations above are completely absent or overexpressed within humans. However, model of PD in animals use overexpression and knockout techniques. The idea behind this is that understanding a protein's behavior may depend on how much of it is expressed. Consider alpha-synuclein as an example. Moreover, it was demonstrated that knocking down alpha-synuclein does not have an impact on dopaminergic neuron development or maintenance (Calabresi et al. 2023 ).

This suggests that the degradation of dopaminergic neurons found in PD is not likely to be caused by the loss of alpha-synuclein. The precise role of alpha-synuclein, however, is unknown; it is difficult to determine its relationship to PD. LRRK2 is restricted to mucosal tissue, in contrast with the ubiquitous alpha-synuclein. Moreover, although LRRK2 knockout mice have been shown to not affect the LRRK2 animal model, it is not particularly useful in investigating DA nerve cell development and preservation. Melanogaster models have limited generalizability for the human state. Autosomal–recessive PD is caused by several mutations. These include PRKN (20% of instances of onset of PD and 50% cases of familial), DJ-1 (a redox-sensitive regulator of antioxidants and molecular chaperone) and PINK1 (phosphatase and tensin homolog-induced kinase 1; confined to the mitochondria). Animal models of these genes that are constitutively knocked out do not exhibit neurodegeneration. Meanwhile, a scientific study demonstrates that SNpc neurodegeneration is correlated with Parkin conditional deletion in adult mice (Aryal and Lee 2019 ).

Genetic studies on PD have shown a variety of monogenic variants of the disease and several genetic risk factors that raise the possibility of developing neuron degeneration (Tran et al. 2020 ). The most often advised method for people to diagnose the disease is molecular testing. Few genes that are significant in both the autosomal–recessive forms and autosomal dominant of PD have been reported in the last ten years (Jia et al. 2022 ). It has determined that mutations in the loci PARK1 to PARK13 (loci on 13 chromosomes) indicate linkage to PD by whole genome linkage screening to differentiate between chromosomal areas linked to the risk of PD or the period of PD onset (Selvaraj and Piramanayagam 2019 ).

Monogenic forms, which are pervasive but only make up around 30% of related cases, were brought on by a single mutation in a gene that was passed down either recessively or dominantly. Most of the gene mutations leading to increased ROS production, mitochondrial DNA damage (mtDNA damage), reduced mitochondrial membrane potential (MMP), decreased ATP levels, structural defects in the organelle, and mitochondrial network are related to mitochondrial dysfunction; these various phases of mitochondrial dysfunction have been responsible for of the development of PD (Liu et al. 2017 ). Parkinsonism is caused by the autosomal-dominant gene transformation of the UCHL1 , SNCA , LRRK2 , and GIGYF2 , and mutations in the, DJ-1 , PRKN , PINK1 , FBXO7 , PLA2G6 , and ATP13A2 , genes. ( Table 2 ) About 27% of those with early-onset PD (EOPD) have a mutation in one of the three genes ( LRRK2 , glucocerebrosidase or Parkin )(Papagiannakis et al. 2018 ).

Cellular and molecular pathways involved in the initiation and progression of Parkinson’s disease

Different genetic, epigenetic, environmental, molecular, cellular, and intracellular dysfunctional symptoms can be seen in this condition. The main molecule that makes up the LB at the molecular level is alpha-synuclein. Significant pathogenic correlation, pathogenesis of Ca 2+ , is linked to an oxidation–reduction imbalance in cells and an increment in reactive oxygen species (ROS) generation. There are seven most common PD-related genes ( VPS35 , DJ-1 , GBA1 , LRRK2 , PINK1 , PRKN and SNCA ). In the cerebral cortex of PD patients, various cellular and molecular biomarkers, such as neuroinflammation, autophagy, and oxidative stress, were detected. Factors that cause oxidative stress promote alpha-synuclein aggregation. In the nigrostriatal neuronal cell, in which it initially aggregates alpha-synuclein deposited, it appears in the GIT or enteric nervous system (ENS), olfactory bulb, and the LB (Fraint et al. 2018 ).

The earliest symptoms of PD are mitochondrial dysfunction and mitophagy. Melanin-concentrating hormone is essential for ATP synthesis, but it also affects calcium storage, cellular metabolism, the generation of damage-associated molecular patterns, damaged associated molecular pathways (DAMPs), the balance of ROS, programmed cell death, inflammatory processes, and immunity to programmed cell death. The loss of dopamine pathways by i) loss of the dopaminergic neuronal cells currently available for synaptic transmission in the SNpc is neuropathological characteristics of PD. ii) Alpha-synuclein, LB, clumps containing neurofibrillary tangles that contain microfibrils are developing (Camargo et al. 2019 ). Lack of dopamine neurotransmitters in the SNpc disrupts the circuitry that controls posture and movement, resulting in symptoms consisting of relaxed shaking and sluggish movement. PD non-motor symptoms include difficulties with sleep, anxiety, memory, autonomic nervous system, and the senses (Zampese and Surmeier 2020 ).

Buildup of oxidative stress due of presence of reactive oxygen species and its effects on generation of Parkinson’s disease

Reactive oxygen species in PD such as hydroxyl radical (OH•), superoxide anion (O 2 ), and hydrogen peroxide (H 2 O 2 ) are synthesized because within the mitochondria there is physiological metabolism of molecular oxygen. In ETS (electron transport chain) the mitochondrial complexes I and III produce Superoxide anion which are very reactive and can easily cross the mitochondrial membrane where it is reduced to H 2 O 2 . Additionally, various nitric oxide synthases (NOS) create nitric oxide (NO), a transient reactive nitrogen species (RNS), which combines with thiols and reduced glutathione (GSH) to form disulfides, sulfenic, sulfonic, and s-nitrosothiols. Additionally, peroxynitrite (ONOO) can be created when oxygen (O 2 ) and nitric oxide (NO) are combined (Hollville et al. 2020 ) shown in Fig. 6 . An increase in ROS production in PD has shown failure in mitochondrial complex I, according to studies utilizing the paraquat and MPTP-like toxins, which are known to cause PD-like symptoms including dopaminergic neuronal cells to die and protein clusters are produced. A complex I impairment can result in a decrease in energy production as well as an increase in the synthesis of free radicals (Mailloux 2020 ).

Radical species development. ROS are produced by a variety of metabolic processes, including oxidative phosphorylation, superoxide anion (O 2 •), Singlet oxygen (O 2 ), hydrogen peroxide (H 2 O 2 ), hydroxyl radical (OH•) nitric oxide (NO•) and mitochondrial-derived reactive oxygen species (mtROS), hydroxyl ion (OH-) (Trist et al. 2019 )

Although the specific causes of mitochondrial complex-I failure in PD are not fully recognized yet, it is reported that a GSH-to-oxidized glutathione (GSSG) ratio increases the formation of RNS as well as ROS species. However, the pathway by which the highest levels of GSSG might rise RNS as well as ROS generation was not discovered; it was demonstrated that glutathione redox state is necessary for the opening of the transition pore of mitochondrial permeability. For instance, GSSG causes the MPTP to open, which then triggers a Ca 2+ basis reduction within the potential of the inner membrane of Wang and Kang ( 2020 ). The reduced glutathione/ oxidized glutathione ratio can increase the generation of ROS or RNS by preventing mitochondrial complex-I from functioning and lowering the potential of the mitochondria. The protein’s sulfhydryl portion of the enzymes having thiol oxidation, which are involved in electron transport of mitochondria, is another way that low amounts of GSH may harm mitochondrial complex-I. In addition, high quantities of these reactive species can also damage crucial complex I residues and decrease the activity of the enzyme glutathione reductase, which is responsible for decreasing GSSG (Teleanu et al. 2022 ).

Recent clinical trials involved in evaluation of possible treatments for Parkinson’s disease

Clinical studies closely monitor the evaluation of novel medications. The US Food and Drug Administration states that the objective of phase-I is dose as well as safety; about 70% of drugs and therapies advance to phase II. About 33% of medications transfer to phase III after completing phase II, which examines the efficacy as well as adverse effects. Phase III is used to monitor adverse effects and investigate their potency. The ‘United States National Library of Medicine’ established the ‘web-based’ registry "clinical trials. gov" for ease in availability of data and information related to the clinical trials, such as the methodology, study design, outcomes, anticipated finish dates, etc. Worldwide sponsors of trial update and maintain the data (Nakamura et al. 2021 ). Clinical trial endpoints are related to the subject of comparing the impact of research, and results may be obtained by a number of means, including behavioral tests, positron emission tomography, magnetic resonance imaging (MRI), biological biomarkers, or electrophysiological monitoring (Jiménez-Gómez et al. 2023 ; Choudhury et al. 2022 ). Each clinical trial is assessed and planned for the advancement to reduce the possibility of negative outcomes (Bouchez and Devin 2019 ). For comparison research in clinical trials, post-approval is necessary. This allows safety, tolerance, and better quality of life, to be taken into account when obtaining effective data from a broader patient group (Nunes and Laranjinha 2021 ). In clinical trials, primary endpoints are necessary and sufficient to determine a drug's or therapy's effectiveness. The primary endpoints serve as the foundation for secondary endpoints, which are sufficient for claiming the efficacy of clinical trial study, and the tertiary endpoints, which provide detailed information (Braidy et al. 2019 ). To investigate PD treatments, we have searched for “clinical trials.gov” clinical trial pipeline data. These clinical studies are shown below among those identified (Table 3 ).

Based on the recent study status, which indicates updated/ongoing or stopped as of 2023, we selected 10 registered intervention clinical trials in phases I, II, and III as novel PD medicines after reviewing the data gathered from “clinical trials.gov.” The phase I/II or phase II/III trials in clinical trials.gov are regarded as being in phase I and II, respectively. The 10 trials, (41%) were in phase I and in phase II (53%), (6%) were in phase III in Fig. 7 . Stem cells have shown the potential of providing a huge supply of dopaminergic neurons which could be beneficial in treatment. Stem cells have also shown differentiation into dopaminergic neurons which will benefit post their transplantation in models of PD (Asemi-Rad et al. 2022 ).

Clinical trial phases and treatment plans for treating PD. The relative contribution of phase I, phase II, and phase III trials to the total is depicted in ( A ) using a pie chart. In Clinical trials. gov, the phase I or phase II trials, respectively, are displayed. B A pie chart showing the percentages of every therapeutic approach to all the clinical trials for PD (Masato et al. 2019 ; Millichap et al. 2021 ; Clinical Research 2021 ; Merkow et al. 2020 ; Merchant et al. 2019 ; Ivanova 2020 ; Mullin et al. 2020 ; Parker et al. 2020 ; Barker 2019 ; Ghosh et al. 2021 ; Asemi-Rad et al. 2022 ; Desai et al. 2021 ; Bryson 2020 )

Neurological disorders have been popularly being treated using herbal and ayurvedic remedies since ages. Hence, it is crucial to isolate bioactive compounds to potentially alleviate these conditions (Staff et al. 2019 ; Saraswat et al. 2020a , 2020b ). In our current research by our laboratory, we are focusing on herbal extracts and their bioactive active compounds for treating PD in animal models (Sachan et al. 2022 ).

Conclusions

Parkinson’s disease is a progressive neurodegenerative disease condition that develops both motor and non-motor symptoms. The motor signs like tremors, resting, bradykinesia, and stiffness which have been determined to be striatal dopamine deficiency and nonmotor symptoms include disorders of sleep, sadness, and cognitive abnormalities. Unfortunately, there are no conclusive tests to support a Parkinson’s disease diagnosis, but identifying conditions with symptoms like Parkinson’s disease is a crucial first step in the diagnostic process.

In this paper, we reviewed recent researches and came to following conclusions. Improvement in both motor and non-motor symptoms for enhancing the lifestyle of patients is the main objective of the Parkinson’s disease treatment.

In the pathophysiology, it was concluded that the slow degradation of dopaminergic neuronal cells in the brain's substantia nigra is Parkinson’s disease main pathophysiological cause. There are many other risk factors associated with Parkinson’s disease, including age-related, genetic, epigenetic, and environmental variables. Single-nucleotide polymorphism in CYP1A2 (Cytochrome P450 A2) or GRIN2A strikes the major threat for Parkinson’s disease which is associated with coffee consumption and falls into the category of genetic modifiers for the environmental risks.

Parkinson’s disease has a significant mortality rate and is the widespread neurodegenerative disease. Induction of the disease by various models has been successfully studied for understanding the genesis, propagation and treatment. Hence, substances like 6-hydroxydopamine, paraquat, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rotenone, and methamphetamine are successfully used for inducing neurotoxicity to develop signs and symptoms like Parkinson’s disease as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine targets mitochondrial cells and serves as an excellent model for how aberrant mitochondrial function can result in symptoms like those of Parkinson’s disease. Rotenone impairs motor function, depletes catecholamines, destroys nigral dopamine, and develops Lewy bodies. Among the neurotoxin models discussed in this review paper, pesticides like parquet and rotenone are commercially available and exhibit many of the symptoms of Parkinson’s disease, including motor impairment, a reduction in Lewy bodies, and the destruction of dopaminergic neurons.

Several geographically specific cellular and molecular mechanisms are actively involved in the development of Parkinson’s disease. In comparison with previous clinical trials for the treatment of Parkinson’s disease, small molecule such as alpha-synuclein aggregation therapy, and monoclonal antibody gene therapy, may show promise in the future. Dopamine auto-oxidation in dopaminergic neurons is a significant source of reactive oxygen species that causes neuronal oxidative stress. LRRK2 , SNCA (alpha-synuclein protein), DJ-1 , PRKN (Parkin protein), PINK1 , GBA1 , and VPS35 are the seven most common Parkinson’s disease-related genes which when affected by mutations leads to development and progression of disease.

According to our opinion, the purpose of clinical studies should be to postpone motor difficulties before they manifest ever lasting effects. Finding new multitarget medications or therapies without side effects is becoming more difficult, whereas the rate of Parkinson’s disease occurrence globally is rising quickly. Future investigations of these molecular pathways will be essential for designing disease-specific therapeutics.

Availability of data and material

Web: http://pubmed.ncbi.nlm.nih.gov/ .

Abbreviations

Blood–brain barrier

Cellular and molecular pathway

Central nervous system

Catechol-o-methyltransferase

Cytochrome P450A2

Damaged associated molecular pathway

Enteric nervous system

Glutamic acid decarboxylase

Glucocerebrosidase

Glutamate ionotropic receptor NMDA type subunit 2A

Oxidized glutathione

Hydrogen peroxide

Intracerebral route

Intraperitoneal route

C-Jun N-terminal kinase

Leucine-rich repeat kinase

Monoamine oxidase-B

Mechanism of action

1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine

Nicotinamide adenine dinucleotide hydrogen

Nitric oxide

Nitric oxide synthase

Superoxide anion

6-Hydroxydopamine

Hydroxyl radical

Parkinson's disease

Reactive oxygen species

Subcutaneous route

Substantia nigra paras compacta

Tyrosine hydroxylase

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Sayyaed, A., Saraswat, N., Vyawahare, N. et al. A detailed review of pathophysiology, epidemiology, cellular and molecular pathways involved in the development and prognosis of Parkinson's disease with insights into screening models. Bull Natl Res Cent 47 , 70 (2023). https://doi.org/10.1186/s42269-023-01047-4

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Accepted : 13 May 2023

Published : 23 May 2023

DOI : https://doi.org/10.1186/s42269-023-01047-4

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Cellular and molecular pathways
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research paper about parkinson's disease

Researchers target neurogenesis in new approach to treat Parkinson's disease

Researchers at the University of Toronto have found a way to better control the preclinical generation of key neurons depleted in Parkinson's disease, pointing toward a new approach for a disease with no cure and few effective treatments.

The researchers used an antibody to selectively activate a receptor in a molecular signaling pathway to develop dopaminergic neurons. These neurons produce dopamine, a neurotransmitter critical to brain health.

Researchers around the world have been working to coax stem cells to differentiate into dopaminergic neurons, to replace those lost in patients living with Parkinson's disease. But efforts have been hindered in part by an inability to target specific receptors and areas of the brain.

"We used synthetic antibodies that we had previously developed to target the Wnt signaling pathway," said Stephane Angers, principal investigator on the study and director of the Donnelly Centre for Cellular and Molecular Biology.

"We can selectively activate this pathway to direct stem cells in the midbrain to develop into neurons by targeting specific receptors in the pathway," said Angers, who is also a professor in the Leslie Dan Faculty of Pharmacy and the Temerty Faculty of Medicine, and holds the Charles H. Best Chair of Medical Research at U of T. "This activation method has not been explored before."

The study was recently published in the journal Development .

Parkinson's disease is the second-most common neurological disorder after Alzheimer's, affecting over 100,000 Canadians. It particularly impacts older men, progressively impairing movement and causing pain as well as sleep and mental health issues.

Most previous research efforts to activate the Wnt signaling pathway have relied on a GSK3 enzyme inhibitor. This method involves multiple signaling pathways for stem cell proliferation and differentiation, which can lead to unintended effects on the newly produced neurons and activation of off-target cells.

"We developed an efficient method for stimulating stem cell differentiation to produce neural cells in the midbrain," said Andy Yang, first author on the study and a PhD student at the Donnelly Centre. "Moreover, cells activated via the FZD5 receptor closely resemble dopaminergic neurons of natural origin."

Another promising finding of the study was that implanting the artificially-produced neurons in a rodent model with Parkinson's disease led to improvement of the rodent's locomotive impairment.

"Our next step would be to continue using rodent or other suitable models to compare the outcomes of activating the FZD5 receptor and inhibiting GSK3," said Yang. "These experiments will confirm which method is more effective in improving symptoms of Parkinson's disease ahead of clinical trials."

This research was supported by the University of Toronto Medicine by Design program, which receives funding from the Canada First Research Excellence Fund, and the Canadian Institutes of Health Research.

Parkinson's Research
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Story Source:

Materials provided by University of Toronto . Original written by Anika Hazra. Note: Content may be edited for style and length.

Journal Reference :

Andy Yang, Rony Chidiac, Emma Russo, Hendrik Steenland, Quinn Pauli, Robert Bonin, Levi L. Blazer, Jarrett J. Adams, Sachdev S. Sidhu, Aleksandrina Goeva, Ali Salahpour, Stephane Angers. Exploiting spatiotemporal regulation of FZD5 during neural patterning for efficient ventral midbrain specification . Development , 2024; 151 (5) DOI: 10.1242/dev.202545

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Researchers target neurogenesis in new approach to treat Parkinson's disease

by Anika Hazra, University of Toronto

The study was published in the journal Development .

"We developed an efficient method for stimulating stem cell differentiation to produce neural cells in the midbrain," said Andy Yang, first author on the study and a Ph.D. student at the Donnelly Center. "Moreover, cells activated via the FZD5 receptor closely resemble dopaminergic neurons of natural origin."

Another promising finding of the study was that implanting the artificially-produced neurons in a rodent model with Parkinson's disease led to improvement of the rodent's locomotive impairment.

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Published: 02 May 2024

Structural underpinnings and long-term effects of resilience in Parkinson’s disease

Verena Dzialas ORCID: orcid.org/0000-0001-7226-6674 1 , 2 ,
Merle C. Hoenig 1 , 3 ,
Stéphane Prange 1 , 4 ,
Gérard N. Bischof 1 , 3 ,
the Parkinson’s Progression Marker Initiative ,
Alexander Drzezga ORCID: orcid.org/0000-0001-6018-716X 1 , 3 , 5 &
Thilo van Eimeren ORCID: orcid.org/0000-0002-6951-2325 1 , 6

npj Parkinson's Disease volume 10 , Article number: 94 ( 2024 ) Cite this article

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Motor control
Parkinson's disease

Resilience in neuroscience generally refers to an individual’s capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer’s disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson’s disease is limited. Our study involved 151 Parkinson’s patients from the Parkinson’s Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson’s patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.

Frontoparietal network resilience is associated with protection against cognitive decline in Parkinson’s disease

Longitudinal brain connectivity changes and clinical evolution in Parkinson’s disease

ROS networks: designs, aging, Parkinson’s disease and precision therapies

Introduction.

Bradykinesia is the cardinal symptom of Parkinson’s disease and, therefore, essential for diagnosing this complex movement disorder 1 . Notably, the clinical stage is preceded by a long prodromal phase, as at least 50% of the nigrostriatal dopaminergic neurons have already degenerated at diagnosis 2 , 3 . Yet, the impact of dopaminergic loss on clinical expression varies across individuals. The observed disparity between the clinical expression and the extent of pathophysiological burden has fuelled concepts on resilience. Resilience refers to the individual’s ability to counteract disease-related detrimental alterations to a certain degree 4 .

Given that the concept of resilience has just emerged in the Parkinson’s disease research field, we recently aimed to harmonize terminologies and methods to study resilience mechanisms, in particular motor reserve, in this movement disorder 5 . The conceptualization of the construct of motor reserve has been aligned with the well-established cognitive reserve framework of the Alzheimer’s Association working group 4 . Briefly, resilience encompasses the concepts of brain, cognitive, and motor reserve. Brain reserve relies on the neuronal substrate, such as grey matter volume or the number of synapses. Cognitive and motor reserve, in contrast, are based on the more efficient use or restructuring of distinct functional and structural networks that permit relative maintenance of either cognitive or motor function. These active adaptations of brain networks have been linked to lifestyle 6 , genetic 7 , and other premorbid factors 8 , 9 . Studies utilizing dopamine transporter single photon emission computed tomography (SPECT) 10 or longitudinal cognitive assessments 11 , for example, reported that higher education or premorbid physical activity was linked to greater tolerance against dopamine transporter loss and the risk of developing dementia in Parkinson’s disease.

However, using a single value may not be sufficient to capture the complex nature of the build-up and extent of individual resilience levels. Therefore, residuals of a linear regression have more recently been used to study resilience in Parkinson’s disease. This, so-called, residual approach was originally introduced to study cognitive reserve in Alzheimer’s disease 12 . It defines resilience as the variance in a clinical outcome variable of a regression model that is not explained by neuropathological burden and other explanatory variables, such as demographic and genetic factors. Importantly, a linear relationship between neuropathological burden and clinical symptom severity is crucial for the proper application of the residual approach. In Parkinson’s disease, the gradual loss of dopamine transporter signal has consistently been related to a predominantly linear increase in motor disabilities in early disease stages, although nonlinear effects cannot be ruled out completely. These motor disabilities can be quantified by the Unified-Parkinson’s-Disease-Rating-Scale motor-score (UPDRS-III score) 13 , 14 , 15 . Deviations from this linear relationship may thus provide information on the underlying individual resilience capacity. Lower observed than predicted motor disabilities (i.e., negative deviations from the regression model) thereby represent higher resilience levels, while positive deviations are associated with lower resilience levels. Using the residual approach, recent studies identified functional and white matter structural networks associated with motor reserve 16 , 17 . Particularly, these networks were associated with a slower longitudinal dose increase in dopamine replacement therapy over two to three years. Due to the short follow-up period, but long duration of the disease, the question of long-lasting resilience effects on quality of life remains, however, open. Nevertheless, these studies provided initial indications of the applicability of the residual approach to study resilience mechanisms in Parkinson’s disease.

Notably, since the residual approach relies on the meaningfulness of errors in the model, determining the optimal model fit by maximizing the explainable degree of variance is necessary to obtain a reliable measure of resilience 18 . Therefore, a systematic investigation of the relationship between regional dopamine transporter signal and the items of the UPDRS-III score is highly relevant. Possible influencing factors are symptom category (i.e., tremor, rigour, and akinesia), side of symptom and pathology onset, and region-specific contributions of the dopamine transporter signal loss. While some studies investigated isolated aspects of this using the former UPDRS-III score 2 , 14 , 15 , 19 , a holistic and systematic assessment of the updated Movement Disorder Society (MDS)-UPDRS-III score is currently lacking.

Moreover, brain networks involved in resilience mechanisms are likely influenced by a multitude of factors like genetic and environmental circumstances. However, the identified networks can only represent the mechanistic pathways that the imaging technique is capable of investigating. Hence, resilience structures based on white matter or functional networks only provide broad insights into the underlying anatomical and functional connections given the spatial and temporal resolution of current Magnetic Resonance Imaging (MRI) techniques 20 , 21 . Structural covariance networks may overcome these limitations by reflecting not only anatomical connections and functional interactions, but also by accounting for developmental dependencies and genetic influences 22 . Thus, structural covariance networks can reflect changes across the lifespan, like in healthy and unhealthy aging 23 and changes induced by distinct lifestyle factors 22 . Particularly, graph theoretical considerations enable investigators to visualize and translate complex covariance patterns into graphs (networks) and meaningful biological parameters such as path length. Moreover, the parameters can be computed at distinct levels, ranging from individual brain regions and sub-networks of interest to the entirety of the brain network, facilitating comprehensive group comparisons across various scales 22 , 24 , 25 . Therefore, the combination of structural covariance networks and graph theoretical analysis offers the opportunity to study both deleterious disease-related network changes and beneficial resilience mechanisms 26 .

Given the current gaps and limitations of the residual approach and the still limited knowledge on the structural underpinnings of resilience in Parkinson’s disease, the aims of this study were three-fold:

First, we aimed to identify an optimal prediction model between the MDS-UPDRS-III score and regional dopamine transporter signal to derive the most suitable residuals as a measure of resilience. Variables for this model were identified using a multiple correlational approach in a cohort of de novo Parkinson’s disease patients. Second, resilience-related structural differences in grey matter volume and regional properties of structural covariance networks were investigated. To achieve this, structural MRI scans were employed in voxel-wise grey matter volume comparisons and graph theoretical considerations of the structural covariance networks. Third, we investigated if the level of resilience directly interacts with the rate of disease progression by using a linear mixed model of the most extended follow-up period (i.e., 7 years) to date. We hypothesized that higher resilience is associated with increased grey matter volume in motor-associated regions and differences in regional properties of structural covariance networks. These differences could be due to a more active lifestyle, so we assumed that patients with high resilience report higher daily physical activity levels than patients with low resilience. Moreover, we expected that motor progression would differ significantly between patients with high and low resilience levels.

Patient characteristics

151 patients were included, out of which n = 50 were categorized as high and n = 45 as low resilience patients based on the residual split at ±0.5 standard deviations (SD). The groups did not differ regarding age, sex, years of education, Montreal Cognitive Assessment (MoCA) test, total intracranial volume and the dopamine transporter signal of the putamen and caudate nucleus. At least in part by design, the groups differed in MDS-UPDRS-III total score. For more information, see detailed group demographics and statistics in Table 1 .

Association of regional dopamine transporter signal and MDS-UPDRS-III sub-scores

Investigating the relationship between regional dopamine transporter signal and MDS-UPDRS-III sub-scores revealed:

Correlations, including the putaminal dopamine transporter signal (τ = –0.19, p < 0.001) are more strongly associated with the MDS-UPDRS-III total score than the dopamine transporter signal of the caudate nucleus (τ = –0.11 p = 0.06, t = –2.35, p = 0.01).

Correlations including the axial and limb-akinetic-rigid (LAR) MDS-UPDRS-III sub-scores are more strongly associated with the dopamine transporter signal of the putamen than the tremor sub-score (t axial_vs_tremor = –4.73, p axial_vs_tremor < 0.001, t LAR_vs_tremor = –3.74, p LAR_vs_tremor < 0.001). See Supplementary Information for respective correlation coefficients.

Both regression models predicting either the more (F(3,147) = 8.0, p < 0.001, r = 0.38, R² = 0.14) or less affected axial-LAR sub-score (F(3,147) = 11.4, p < 0.001, r = 0.43, R² = 0.19) were significant. In both models, the contralateral putaminal dopamine transporter signal emerged as a significant predictor (ß more_affected = −1.1, p more_affected < 0.001, 95% CI more_affected = –1.6:–0.6; ß less_affected = –1.0, p less_affected < 0.001, 95% CI less_affected = –1.4:–0.6). Further, in the less affected model, sex showed a trend toward significance (ß less_affected = –0.3, p less_affected = 0.05, 95% CI less_affected = 0:–0.6). Despite both models being significant, we observed a 26% better model fit for the regression using the less affected hemisphere and bodyside than for the model using the more affected hemisphere and bodyside. Therefore, modelling the less affected axial-LAR MDS-UPDRS-III sub-score was regarded as more accurate. See Supplementary Information and Supplementary Fig. 1 for detailed regression model comparison.

Collectively, the closest correlation between dopamine transporter signal and MDS-UPDRS-III score was found between the less affected putaminal dopamine transporter signal and contralateral axial-LAR MDS-UPDRS-III sub-score (Fig. 1 ). The resulting regression model, which was used to derive the residuals, was statistically significant (F(3147) = 11.4, p < 0.001, r = 0.43, R² = 0.19).

Correlation matrix (non-parametric Kendall partial rank tau-b correlations, τ) of regional dopamine transporter signal and MDS-UPDRS-III sub-scores, with the size representing the significance level and the colour visualizing the correlation strength. A reciprocal transformation of the p values was performed to show the most significant correlations with the largest circles. MDS-UPDRS-III = Movement Disorder Society – Unified-Parkinson’s-Disease-Rating-Scale motor-score.

Association between resilience and daily physical activity

Resilience at baseline was negatively correlated with the Physical Activity Scale for the Elderly (PASE) at year one (ρ = –0.4, p < 0.05, Fig. 2 ), indicating that higher resilience is associated with greater self-reported physical activity scores. The correlations performed at later time points yielded similar trends, but only years four and seven reached the significance threshold (ρ 4 = –0.2, ρ 7 = –0.3, p < 0.05, see Supplementary Information and Supplementary Fig. 2 for correlation coefficients and significance levels).

Partial Spearman correlation between baseline resilience values and the Physical Activity Scale for the Elderly at year one, corrected for age and sex is depicted. Low, intermediate, and high resilience patients are indicated by blue-filled squares, grey-filled circles, and green-filled diamonds, respectively. The plots display 95% confidence intervals as error bars, estimated using bootstrapping with 1000 iterations.

Morphometric differences based on resilience levels

The comparison of grey matter volume between high and low resilience patients yielded greater volume in motor-associated brain regions in the high resilience group (Fig. 3 ). The regions consisted of the postcentral gyrus and central operculum on the left hemisphere. The second analysis, including all patients without applying the ±0.5 SD cut-off value to group the residuals, additionally showed that the right posterior cerebellum and the right postcentral gyrus together with the central operculum were associated with higher resilience. In both analyses, no clusters of increased grey matter volume were found in the low resilience group. Considering the dominant affected bodyside as a covariate did not change the analyses results. However, the extent of the clusters varied slightly, as shown in Supplementary Fig. 3 .

The high compared to the low resilience group showed greater grey matter volume in a cluster comprising the left postcentral gyrus (PoCGy) and central operculum (CO). An additional analysis, including all patients without applying the ±0.5 SD cut-off value to group the residuals, revealed the same cluster and additionally clusters in the right posterior cerebellum (Cbe) and right POCGy/CO. No significant clusters of increased grey matter volume were found with reversed contrasts. All clusters shown here were significant at cluster-level after FWE-correction ( p < 0.05) with an initial p value set at p < 0.001.

Structural covariance networks of high and low resilience groups

The regional covariance network analysis yielded robust betweenness centrality hubs in the right insula and precentral gyrus in high resilience patients (Fig. 4a ). In contrast, hubs in the low resilience groups were located in the left medial occipital and postcentral gyrus, the left and right inferior temporal gyrus, and right putamen (Fig. 4b ). All hubs were verified by leave-one-out cross-validation.

Structural covariance networks and corresponding betweenness centrality hubs of the high ( a —green nodes) and low resilience ( b —blue nodes) groups, respectively. All coloured nodes show a node betweenness of >2 standard deviations of the average betweenness centrality. All hubs were verified by leave-one-out cross-validation. INS INSula, PrCGy PreCentral Gyrus, PoCGy PostCentral Gyrus, MOGy Medial Occipital Gyrus, ITGy inferior temporal gyrus, PUT PUTamen.

Mitigating effects of resilience on disease progression

The linear mixed model analyses predicted MDS-UPDRS-III off-medication (UPDRS-III-OFF) scores of high, low, and intermediate resilience patients over a seven-year follow-up period. From these models, we achieved resilience group-specific average decline rates and mean effects of the resilience groups on the UPDRS-III-OFF score. The results for predictions regarding either the total UPDRS-III-OFF score, or the more or less affected axial and LAR UPDRS-III-OFF sub-scores, showed comparable results. Consequently, high resilience patients could, on average, benefit for more than a decade (11.9 years, CI 1.4:22.5, Fig. 5 ) from the initial lower motor disabilities (UPDRS-III-OFF score at year 0). This extrapolation assumed that the steeper decline rates of high resilience patients remained constant even after the seven-year follow-up interval.

Depicted are the original square root transformed MDS-UPDRS-III values in the off-medication state over time for each patient. In the background, the individual lines for each patient are shown in pale colours. In addition, the group-specific decline over time is shown (blue=low, grey=intermediate and green=high resilience). MDS-UPDRS-III = Movement Disorder Society – Unified-Parkinson’s-Disease-Rating-Scale motor-score.

When comparing the decline rates in the more and less affected axial-LAR UPDRS-III-OFF sub-score models, higher decline rates could be observed in the less (ß_slope high_resilience = 0.164, CI = 0.08:0.25, p < 0.001) compared to the more affected side model (ß_slope high_resilience = 0.103, CI = 0.03:0.18, p < 0.05). This might point towards flooring effects that potentially dampen the steeper decline rates of high resilience patients over time. Therefore, the time interval might even be prolonged till they catch up to the impairment level of the low resilience patients. See Supplementary Table 1 for details on fixed and random unstandardized ß-coefficients. The results of the analyses did not change when adding the levodopa equivalent daily dose (LEDD) at each follow-up time point and MoCA baseline scores as covariates (for details, see Supplementary Information and Supplementary Table 2 ).

Analyses regarding the time until the onset of levodopa-induced dyskinesias showed a trend of the high resilience groups presenting a slight prolongation of symptom onset. However, the results did not reach statistical significance (α = 0.05 for detailed statistical analyses see Supplementary Information and Supplementary Fig. 4 ).

Resilience and longitudinal dopamine signal loss

We investigated the dopamine transporter signal decline over time to exclude differences in the trajectories of dopaminergic neuron degeneration as the underlying reason for the observed longitudinal effects of resilience. Again, the mean, contra-, and ipsilateral putaminal dopamine transporter signal decline was modelled separately to consider possible laterality-related differences. The mean effects of the resilience group on the dopamine transporter signal as well as the time by resilience group interaction terms (slope), were not significant ( p > 0.05 for details, see Supplementary Information and Supplementary Table 3 ). These results indicate that the differences in symptom severity and decline rates are not based on diverging dopamine transporter availability in the putamen. The only variables significantly contributing to the model were time and quadratic time in years ( p < 0.001). Further, a significant ( p < 0.05) covariance between the two random effects, subject (intercept) and time (slope), was observed. This covariance indicates faster dopamine transporter signal decline in patients with higher initial dopamine transporter levels independent of the resilience group.

Research on resilience in Parkinson’s disease is still at an early stage. Therefore, methodological aspects for the quantification of resilience, as well as its neurobiological underpinnings and moderating effects in the longitudinal disease trajectory, are highly relevant. Using the systematically-derived residuals as resilience estimates, this study identified key brain regions for motor information processing involved in the mitigation of detrimental disease effects. Higher resilience (i.e., negative residual value) was associated with hubs in the right precentral gyrus and insula and increased grey matter volume in the bilateral postcentral gyrus and right cerebellum. Importantly, even though higher resilience levels were associated with increased functionality at baseline, longitudinal analysis revealed steeper decline rates in this group. Nonetheless, extrapolations indicated that high resilience patients uphold relatively high motor function for up to a decade before deteriorating to lower resilience performance levels. The strong correlation between self-reported physical activity and resilience levels further supports the beneficial effects of higher resilience on motor functions. Notably, compared to other studies, we closely examined the relationship between the variables from which the resilience estimates were derived. The implications of determining the optimal model for quantifying resilience estimates will be discussed in the next section.

To determine resilience by means of the residual approach, a non-invasive neuropathological measure (i.e., dopamine transporter SPECT) is required to predict the clinically overt motor disabilities (i.e., MDS-UPDRS-III score) as accurately as possible. Our systematic assessment found the strongest association between the putaminal dopamine transporter signal anatomically contralateral to the less affected axial-LAR MDS-UPDRS-III sub-score.

Previous studies using the residual approach to determine individual resilience levels focused on the UPDRS-III total score and putaminal dopamine transporter signal and neglected symptom laterality and symptom categories 16 , 17 , 27 , 28 . However, there is an increasing body of evidence reporting that tremor is not associated with nigrostriatal degeneration 29 , while the putaminal dopamine transporter signal is most strongly related to certain sub-scores of the UPDRS-III score 14 , 15 , 19 . In addition, patients with left-sided symptom onset showed greater symptom progression over time 30 , suggesting that laterality should be considered when estimating resilience.

In line with previous studies investigating the association between the dopamine transporter signal and UPDRS-III items, we found significant differences in the correlation strength regarding striatal sub-regions, MDS-UPDRS-III items and symptom laterality. Still, one might have expected a closer association between the more affected bodyside and contralateral hemisphere. However, flooring effects can limit the variance in dopamine transporter signals in correlation and regression models reducing both the correlation strength and power to determine inter-individual differences in resilience levels. As demonstrated in previous studies, these flooring effects influence the more affected side earlier and more intensely 31 , while the general relationship between radiotracers and dopaminergic cell loss dropped when neurodegeneration exceeded 50% 32 . Therefore, using the symptom severity of the less affected bodyside and anatomically contralateral dopamine transporter signal may potentially provide a more precise measure of resilience. Nonetheless, early preclinical stages may exhibit reversed effects, yet accurately assessing this possibility is challenging due to the limited sensitivity of the MDS-UPDRS-III score in detecting subtle motor dysfunction. As a proof of concept for our model, residuals showed a strong positive association with self-reported daily physical activity levels. Physical activity, therefore, may provide an essential factor in enhancing neuroprotective and -plasticity mechanisms, enabling the relative preservation of motor function despite striatal dopamine loss 33 , 34 . Together with the resilience-dependent differences in grey matter volume and structural connectivity networks, this points towards an interplay between more passive (brain reserve) and active (motor reserve) mechanisms, leading to combined resilience effects, which will be further discussed below.

Brain reserve and motor reserve are closely intertwined domains of resilience. While brain reserve provides the more or less robust basis for structural and functional connectivity, motor reserve uses this biological basis to adapt brain networks to task demands in face of disease-related conditions 5 . This close interconnection makes it necessary to attempt to decipher the specific contribution of each domain to the clinical phenotype, while also considering their interdependence. In our study, we observed that patients with higher resilience were characterized by greater grey matter volume in motor-associated brain regions (i.e., brain reserve) but also differences in betweenness centrality relating to the efficiency of information processing in (sub)cortical networks (i.e., motor reserve).

In particular, patients with higher resilience levels showed greater grey matter volume in bilateral postcentral gyri and the right cerebellum. The increased grey matter volume in these motor-associated regions likely provides greater tolerance towards impending neurodegeneration until symptom onset and the breakdown of networks involving these brain regions. In line with this, resilience estimates (residual approach) and brain reserve estimates (deformation-based morphometry) were recently shown to correlate positively with local striatal volumes 35 and negatively with clinical measures of disease progression 35 , 36 . However, the results of these studies were restricted to subcortical regions, which are known to undergo severe brain atrophic processes in early stages of Parkinson’s disease 37 . Here, we used a whole-brain voxel-wise method to identify more global effects of brain reserve in cortical and cerebellar regions, which may not have yet been affected by the neurodegenerative process. Thereby, greater grey matter volume in these regions may support the actual preservation of neuronal networks.

The hub analysis of our structural covariance network analysis yielded the precentral gyrus and insula as hubs in the high resilience group. These hubs were based on betweenness centrality, a measure of the importance of a brain region for information transmission and distribution inside a network. Therefore, the hubs resemble connectors of different sub-networks integrating high amounts of information. The identified precentral gyrus is an essential part of the human motor system for movement control and decision-making, while the role of the insula in motor control and Parkinson’s disease has just recently gained more attention. Research efforts revealed insular activation to be crucial in body awareness and perception of time for complex movements 38 . Further, the insula is associated with aspects of motivation, which have recently been argued to carry an important role in voluntary movement and akinesia in Parkinson’s disease 39 . While the discussed network hubs of the high resilience group are more confined to motor regions, the low resilience group showed a more widespread pattern of significant hubs, including the left postcentral-, middle occipital and inferior temporal gyrus as well as the right putamen and inferior temporal gyrus.

Supporting the role of the insula and cerebellum in resilience mechanisms in Parkinson’s disease, a study recently identified these regions in a functional motor reserve network 16 . Additionally, the importance of the cerebellum in the maintenance of motor functioning is underpinned by its role in a resilience-related white matter network 17 . Higher connectivity in the identified networks was associated with slower disease progression. Together with our observation that the insular and precentral gyrus are the only hubs in the high resilience network, this points toward a more segregated motor network in patients with higher resilience. Segregation describes highly connected sub-networks for localized task performance, while integration refers to the cooperation between sub-networks. Given cost-efficiency, not all brain regions are equally interconnected, leading to clusters of highly connected (segregated) sub-networks which are only interconnected (integrated) by a few links 40 . Importantly, network segregation has been shown to be crucial for the execution of motor tasks, while higher cognitive functions seem to be associated with a more integrated topology 41 . Indeed, it was shown that training of specific movements leads to higher segregation of brain regions involved in motoric functioning and visual perception 42 . Although this indicates that differences in motor network segregation might be a potential explanation for performance differences of high and low resilience patients, higher segregation may also pose a liability. The benefits of independent and automatized motor task performance in early disease stages come with the cost of only a few brain regions as connectors, which makes them more vulnerable.

Overall, the results of this study may provide insights into the importance of network segregation in the relative maintenance of motor function. This, however, does not exclude the possibility that higher integration also serves as motor reserve mechanism, as recently shown 8 . To distinguish disease-induced changes from those related to reserve, a longitudinal assessment of changes in network segregation and integration is required. Such analyses could also examine the specific effects of genetic determinants of brain anatomy (i.e., brain reserve) and lifestyle factors on network stability and plasticity (i.e., motor reserve). Investigating genetic and lifelong influences on resilience could provide valuable insights into the heterogeneity of the disease pattern and thus support early diagnosis and prognosis.

Given the heterogeneity across Parkinson’s disease patients, accurate prognosis regarding the patient-specific disease course remains difficult 43 . The disease itself is influenced by a multitude of factors, such as genetics, demographics and lifestyle, which to varying degrees contribute to the build-up of the individual resilience capacity. Therefore, it seems crucial to consider potential effects of resilience on the timing of clinical diagnosis and clinical progression. Yet, in Parkinson’s disease, only few studies are available investigating the longitudinal effect of resilience mechanisms. Our longitudinal analysis of seven-year follow-up data showed that patients with higher resilience had less overall motor disabilities but steeper decline rates. However, extrapolations, assuming constant decline rates, indicate that it would take up to a decade for high resilience patients to be on par with motor performance levels of low resilience patients.

In contrast to our results, higher resilience was recently linked to slower progression in clinical scores 36 , lower risk of developing levodopa-induced dyskinesia, freezing of gait 27 or dementia 28 and slower dose increase in levodopa therapy 16 , 17 . However, another study could not find any resilience-related differences in motor performance decline rates, but similar to our study, it associated higher resilience with lower motor disabilities at baseline 44 . Differences in outcome variables, surrogate measures for resilience, and varying follow-up periods might cause these conflicting results regarding the effect of resilience on disease progression. Especially the follow-up period seems to influence the results, suggesting less beneficial long-term effects of resilience given longer follow-up periods. While short-term studies associated slower decline rates with higher resilience, our long-term study showed sustained beneficial effects that may wane after several years.

Our study differs from previous ones in terms of methodological aspects, which might explain the deviations regarding long-term effects of resilience. First, our cohort had a restricted age range and younger patients, reducing the influence of age-related network alterations and differences in dopamine transporter availability. Second, we addressed laterality-induced flooring effects in the dopamine transporter signal and ceiling effects in clinical symptom assessment. We also excluded items from the MDS-UPDRS-III score unrelated to the severity of dopaminergic deficit, increasing the validity of the residuals. Furthermore, our study investigated long-term effects of resilience over a seven-year follow-up interval, unlike previous studies, which only followed up for two to three years. Finally, we used a direct measure of disease severity (MDS-UPDRS-III score) instead of indirect measures like the LEDD dose, which might not provide an objective measure of disease progression 45 .

Noteworthy, no study to date has been able to show that patients with higher resilience in Parkinson’s, like in Alzheimer’s disease 46 , can tolerate more pathology till symptom onset. While resilience in Parkinson’s disease primarily relies on differences in clinical symptom severity, Alzheimer’s patients differ regarding the pathological load 47 . This difference might be related to differences in the pathological measure. In Alzheimer’s disease, the accumulation of harmful proteins (amyloid and tau) is used, while in Parkinson’s disease, neuronal loss serves as the neuropathological measure.

Despite our systematic investigation of the correlation between dopamine transporter signal and MDS-UPDRS-III score, several limitations must be considered. Namely, the residuals are still based on the error in the model 18 , 48 and are highly correlated with the dependent variable 18 . However, our systematic model selection and restriction to young, de novo patients maximized the explainable variance in the residual approach and minimized noise caused by flooring effects. Further, we validated our resilience estimates by correlation analysis with an independent measure of daily physical activity. However, the correlation between dopamine transporter availability and MDS-UPDRS-III score remained at the lower end of the expected spectrum. The rather moderate correlation strength is likely linked to the study design. First, the multi-centre data acquisition introduces random variation. Second, the early disease state of the studied cohort limits the variability in dopamine transporter availability and, consequently, the strength of the correlation. Further, we cannot rule out that our age restriction or other factors like genetics might influence the results. It will thus be interesting to assess whether comparable neuronal imprints exist in older individuals, how they evolve as the disease advances, and to further disentangle the domains (i.e., brain and motor reserve) of resilience. Moreover, in our grey matter volume analysis, we refrained from further subdividing groups based on the dominant affected side and handedness due to the limited sample size and the resulting power issues. However, investigating these effects might provide valuable insights into laterality mechanisms related to resilience. Additionally, examining the mediating role of premorbid and current lifestyle factors explaining the derived residuals and identified network structures warrants future studies. These studies may provide novel insights for the development of interventional strategies targeting these networks.

In sum, this study demonstrated the residual approach’s usefulness in identifying resilience-related structural differences and the influence of different resilience levels on disease progression. The relative maintenance of motor function in Parkinson’s disease patients is likely driven by brain reserve, potentially allowing greater tolerance against neurodegenerative processes through motor reserve-associated network restructuring. Network restructuring may, in turn, lead to a higher segregated motor network that supports more efficient motor performance for up to a decade. However, it remains to be elucidated which factors can influence the build-up and maintenance of resilience, as the residuals used as approximation of resilience represent the sum of disease and mitigating factors after disease onset. As indicated by our correlational approach, current physical activity may play an important role in the maintenance of relative motor function. Moreover, modifiable early and midlife lifestyle parameters are of high interest but also (epi)genetic, metabolic, and proteomic factors. Future investigations may further examine the interlinkage of motor and cognitive reserve networks. Understanding whether primary motor circuitries are more efficient in mitigating motor decline rather than cognition-relevant structures or vice versa will be especially relevant for patients with mild cognitive impairment or dementia.

Participants

Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database ( www.ppmi-info.org/access-data-specimens/download-data ), RRID:SCR_006431. For up-to-date information on the study, visit www.ppmi-info.org . Inclusion criteria were: (1) de novo, unmedicated Parkinson’s disease patients (at baseline) diagnosed according to clinical criteria from Postuma et al. 1 ; (2) age 50–66.5 years to avoid inclusion of familiar cases and age-related differences in the integrity of the dopaminergic system 49 ; (3) available baseline dopamine transporter SPECT scan ([ 123 I]β-CIT); (4) available baseline structural MRI scan; (5) at least one available PASE score. This led to the inclusion of 151 patients (see Fig. 6 for details of the filtering process and Supplementary Table 4 for the PPMI identifiers). Ethical approval and written informed consent according to the Declaration of Helsinki for all patients were obtained from the respective PPMI sites. The PPMI trial was registered under NCT01141023.

The filtering pipeline was started with 423 patients and included five filtering steps as described on the right side of the flowchart. On the left side the number of patients excluded in each step are stated. From the resulting 151 patients, those with a seven-year MDS-UPDRS-III score follow-up in the off-medication state ( n = 70) and those with four-year Dopamine transporter follow-up ( n = 115) were included in the cohorts for the longitudinal analyses. MDS-UPDRS-III Movement Disorder Society—Unified-Parkinson’s-Disease-Rating-Scale motor-score; DaT SPECT Dopamine transporter Single Photon Emission Computed Tomography.

Processing of neuroimaging data

Structural T1-weighted scans were acquired following the PPMI protocol (for detailed information, see https://www.ppmi-info.org/study-design/research-documents-and-sops ), with a total scan time of seven minutes on 3 T MRI machines. The structural MRI images were segmented into grey, white, and cerebrospinal fluid compartments and normalized to the Montreal Neurological Institute space using the Computational Anatomy Toolbox in SPM12 50 . The resulting grey matter volume maps were used for subsequent analyses.

SPECT imaging using 123 I Ioflupane ([ 123 I]β-CIT) was performed according to the PPMI standardized protocol to quantify dopamine transporters in the striatum. In this study, the specific binding ratios for putamen and caudate nucleus for both hemispheres, as provided in the PPMI database, were used. Specific details about the image acquisition parameters, attenuation correction, pre-processing, and predefined variables by PPMI are available at https://www.ppmi-info.org/study-design/research-documents-and-sops .

The dopamine transporter values of the right and left caudate nucleus and putamen were extracted, using the occipital lobe as reference region. Based on the predominantly affected bodyside at onset (DOMSIDE in the PPMI datasheet), the contralateral hemisphere was labelled as more affected, while the ipsilateral hemisphere was regarded as less affected.

Motor assessment and clinical evaluation

Due to the study design of PPMI, two MDS-UPDRS-III assessment dates were available, namely screening and baseline, which were only 1.5 months apart. To account for non-disease-related fluctuations in daily performance, we averaged the scores of both assessments. Side-specific MDS-UPDRS-III sub-scores were calculated for LAR, tremor and axial sub-scores, as done previously 15 , 51 . In total, five MDS-UPDRS-III sub-scores were defined, namely an axial and two for the less and more affected LAR and tremor sub-scores, respectively. For patients without a predominantly affected side at onset, the average score of both sides was computed.

Correlations between dopamine transporter signals and MDS-UPDRS-III sub-scores

We performed a hierarchical hypotheses-driven analysis to determine which model most reliably predicts motor impairments as a function of regional dopamine transporter signal. First, we performed pairwise correlations between hemispheric dopamine transporter signals in the putamen or caudate nucleus and the MDS-UPDRS-III sub-scores. We used non-parametric Kendall partial rank tau-b correlations and adjusted the analyses for age and sex. Secondly, hypotheses were formulated by visually assessing the correlation strength between the studied pairs (Fig. 1 ). As shown in Fig. 7 , these hypotheses were then tested in a hierarchical cascade to determine the most predictive correlation. All results regarding the hypotheses testing are reported with one-tailed p values given the clearly defined hypotheses (for detailed information about the hypothesis testing procedure see Supplementary Information). All analyses described thus far were calculated using RStudio version 1.3.959 52 .

Schematic illustration of the statistical analysis approach. Left column: The hypotheses are illustrated as decision tree; Middle column: The tested hypotheses; Right column: Performed statistical analyses to test the three hypotheses. The red path in the decision tree points to the statistically significantly stronger associations. DaT Dopamine Transporter, LAR limb-akinetic-rigid, MDS-UPDRS-III Movement Disorder Society—Unified-Parkinson’s-Disease-Rating-Scale motor-score.

Calculation of resilience proxy using the residual approach

Subsequently, we calculated resilience levels as the standardized linear regression residuals 16 , 17 , 27 , 28 , using the square root transformed combined axial and LAR MDS-UPDRS-III sub-score of the less affected bodyside as dependent variable and the contralateral putaminal dopamine transporter signal as predictor, correcting for age and sex. Negative residual values in this model indicate high resilience, while positive deviations capture individuals with low resilience. As residuals close to the regression line may mostly relate to noise, we discarded residual values around zero ( n = 56) and grouped the remaining individuals into high (<–0.5 SD) and low resilience (>+0.5 SD from the regression line) patients. For group-specific characteristics see Table 1 .

Correlation analysis between resilience and PASE score

To determine whether higher resilience is associated with greater daily physical activity, a partial Spearman correlation analysis between the PASE scores (see https://meetinstrumentenzorg.nl/wp-content/uploads/instrumenten/PASE-handl.pdf for computation) and residual values, corrected for age and sex, was performed. Given that the baseline PASE score was only available for eight individuals, separate correlations (i.e., seven) were performed for each time point with sufficient data available ( n > 50, for detailed information about patient per time point availability, see Supplementary Table 5 ). This analysis was conducted in Python version 3.8 53 .

Voxel-wise whole brain group comparison of grey matter volume

To examine differences in grey matter volume as a function of resilience, the high resilience group was compared against the low resilience group, using voxel-wise whole-brain comparison in SPM12. The comparison was corrected for age, sex, and total intracranial volume. A whole-brain cortical mask was employed. The p value was set at p < 0.001 (uncorrected), and the voxel extent was set to k = 100. Resulting clusters significant at FWE-corrected p < 0.05 were then considered in the results. Reverse contrasts (low vs. high resilience) were also assessed. Arguably, the above-mentioned analyses only consider participants with strong deviations from the expected MDS-UPDRS-III score. Therefore, the analysis was repeated with a residual split at a residual value of 0 (for group characteristics, see Supplementary Table 6 ). Moreover, to account for a potential bias that might arise from differences in the absolute numbers of patients with left and right dominant affected sides in the high and low resilience groups, we repeated the analyses, including “dominant side” as a covariate.

Structural brain network analysis

To compare resilience level-dependent structural network properties, we further performed structural covariance network analyses. This type of analysis can generate a graphical representation of how brain regions are structurally interconnected and estimate whole-brain or region-specific morphological measures that reflect, for example, the network’s effectiveness in information transmission. In our analysis, we investigated differences in the structural covariance networks between the two resilience groups, using the Graph Theoretical Analysis (GAT https://www.nitrc.org/projects/gat/ ) toolbox 24 . First, the grey matter volume-maps were corrected for age, sex, and total intracranial volume and parcellated using the Automatic Anatomical Labelling atlas resulting in 90 regions of interest. Next, association matrices (90 × 90 regions of interest) were computed, which comprised the corrected covariance values for every pair of regions of interest using leave-one-out cross-validation. For the computation of network properties, like betweenness centrality, the matrices were then thresholded at the minimum density (D min ) , resulting in non-fragmented networks of full connectivity (with D min ranging from 0.1 to 0.19 for the cross-validation analyses). Betweenness centrality measures the importance of a brain region (node) for information transmission and distribution inside a network by bridging different network clusters 25 . Based on this measure, hubs were identified as nodes with a betweenness centrality greater than two standard deviations of the regular nodes within the same network 24 . Next, we compared the regional distribution of the identified hubs between the resilience groups. Given that the difference in hub distribution may be linked to network restructuring and, thus, more active forms of reserve, we refer to them as high and low motor reserve networks. By comparing the regional distribution of the identified hubs in the high and low motor-reserve networks, it is possible to detect alterations in regional network structures. Network hubs identified in over 80% of the cross-validation analyses were considered stable.

Linear mixed modelling to assess longitudinal resilience effects

Next, we assessed if disease trajectories differ depending on baseline resilience estimates. Therefore, a linear mixed model was calculated to track longitudinal changes (seven-year follow-up) in motor performance (MDS-UPDRS-III off-medication score; i.e., UPDRS-III-OFF) in relation to the resilience group using SPSS (IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY) . The off-medication score was used to avoid medication influences on the MDS-UPDRS-III score, especially later in the disease course. Further, only patients with a seven-year follow-up period were included in this analysis ( n = 70; for cohort and group information, see Supplementary Table 7 ). The scores were averaged if multiple assessment dates were available within one year. Individuals close to the regression line within ±0.5 SD were included as an intermediate reference group. The model included the following fixed effects: time in years (continuous); (2) quadratic time in years (continuous); (3) resilience category (categorical with a high, intermediate, and low resilience category); (4) interaction term time (1) and category (3); (5) covariates of no interest (age (continuous), sex (categorical) and the putaminal dopamine transporter signal of the less affected hemisphere). The dopamine transporter signal was used to correct for potential group differences in overall dopaminergic degeneration, while the quadratic time effect accounted for non-linear decline processes. In addition, the model included two random effects (subject and time) that allowed individual intercepts and slopes. Results are reported as unstandardized beta-coefficients (ß) with the corresponding confidence intervals (CI) and p values.

The same modelling was repeated for the more and less affected axial-LAR UPDRS-III-OFF sub-score to investigate possible laterality-related differences in decline rates. Based on Akaike and Bayesian information criteria, the baseline dopamine transporter signal of the respective contralateral side was used for the more and less affected UPDRS-III-OFF sub-score model. We repeated the mixed model analyses, including LEDD at each follow-up time point and the baseline MoCA scores, to account for possible confounding effects of cognition and medication (for details see Supplementary Information).

As a complementary analysis, Kaplan-Meier survival curves were employed to examine resilience-related differences in the time until the onset of levodopa-induced dyskinesias. Additional analysis and data availability details can be found in Supplementary Information and Supplementary Table 8 .

Linear mixed model-based extrapolation of long-term effects

Considering the more rapid decline in cognitive function in patients with high cognitive reserve from the time of diagnosis of Alzheimer’s disease 54 , we assumed faster motor decline of high resilience patients in Parkinson’s disease as well. The time they need to catch up to the level of motor disabilities of low resilience patients can be estimated via formula 1 (1). Next, we extrapolated the time interval during which patients can benefit from higher resilience. We did this by dividing the difference between the initial motor disabilities of high and low resilience patients (UPDRS-III-OFF score year 0 of the low resilience group (a) and the high resilience group (b)) by the difference in decline rates (slopes of the low resilience group (c) and the high resilience group (d)).

Confidence intervals were computed via error propagation using the Eq. ( 2 ), based on the standard errors (δ) of the unstandardized beta coefficients:

Resilience category-dependent longitudinal dopamine transporter signal decline

To exclude resilience category-dependent differences in the pace of neuropathological changes, an additional linear mixed model was performed, investigating the dopamine transporter signal decline over time. The model was set up using the same fixed and random effects, except for the baseline putaminal dopamine transporter signal. In contrast to the MDS-UPDRS-III score, dopamine transporter imaging was only assessed till year four, not seven. Therefore, in this analysis, all patients with less than four-year follow-up data were excluded (for cohort and group information, see Supplementary Table 9 ). Again, to account for side related differences, the model was computed three times, for the more affected, mean and less affected putamen.

All statistical tests report two-sided p values unless otherwise stated.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data used for this study are publicly available via the PPMI website ( https://www.ppmi-info.org/ ). Unique identifiers of the subjects included in this study can be found in Supplementary Table 4 .

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Acknowledgements

Funding was provided by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): project ID 233886668/RTG 1960; project-ID 431549029 - C03 – CRC1451 –. The funders had no role in the study conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. The corresponding author had full access to all of the data and the final responsibility to submit it for publication. Funding: PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson’s, AskBio, Avid Radiopharmaceuticals, BIAL, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics.

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University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, 50937, Cologne, Germany

Verena Dzialas, Merle C. Hoenig, Stéphane Prange, Gérard N. Bischof, Alexander Drzezga & Thilo van Eimeren

University of Cologne, Faculty of Mathematics and Natural Sciences, 50923, Cologne, Germany

Verena Dzialas

Molecular Organization of the Brain, Institute for Neuroscience and Medicine II, Research Center Juelich, 52428, Juelich, Germany

Merle C. Hoenig, Gérard N. Bischof & Alexander Drzezga

Université de Lyon, Institut des Sciences Cognitives Marc Jeannerod, CNRS, UMR, 5229, Bron, France

Stéphane Prange

German Center for Neurodegenerative Diseases, 53127, Bonn, Germany

Alexander Drzezga

University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, 50937, Cologne, Germany

Thilo van Eimeren

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the Parkinson’s Progression Marker Initiative

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V.D.: analyses, visualization and manuscript preparation; M.C.H.: analyses, manuscript preparation; S.P.: statistical analyses; A.D., G.N.B., T.v.E.: conception and design. All authors contributed to data interpretation as well as reviewed and commented on the manuscript. Further, all Authors read, approved and take responsibility for the final manuscript.

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Dzialas, V., Hoenig, M.C., Prange, S. et al. Structural underpinnings and long-term effects of resilience in Parkinson’s disease. npj Parkinsons Dis. 10 , 94 (2024). https://doi.org/10.1038/s41531-024-00699-x

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