Global case studies and collaborative frameworks.

Parkinson's disease (PD) is a central nervous system neurodegenerative illness. Its symptoms include poor motor skills, speech, cognition, and memory. The condition is incurable, although evidence shows that early identification and therapy reduce symptoms. A lack of medical facilities and personnel hinders PD identification. PD is a common chronic degenerative neurological dyskinesia that threatens the elderly. Multi-modal data fusion may reveal more about PD pathophysiology. This study aims to contribute to the evaluation of PD by introducing a novel multimodal deep-learning technique for distinguishing individuals with PD from those without PD. This study utilizes resting functional magnetic resonance imaging (rfMRI) and gene data obtained from the Parkinson's Progression Markers Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) databases. The primary objective is to predict the specific pathological brain regions and identify the risk genes associated with PD. The authors want to learn more about the genetic components and underlying procedures by analyzing these datasets. Contributing to the development and progression of PD. In this study, we present our findings that demonstrate the superior recital of our proposed multimodal method compared to both unimodal approaches and other existing multimodal methods. Our evaluation is based on an extensive dataset consisting of real patients. Specifically, our proposed method stacked deep learning classifiers (SDLC) achieves an impressive F1-score of 0.99 and an accuracy of 99.4%, surpassing the performance of both unimodal approaches and other multimodal methods. These results highlight the efficiency and potential of our method in enhancing the accuracy and reliability of patient data analysis. In this study, we demonstrate that our proposed method consistently surpasses alternative approaches in terms of performance, as indicated by a higher average increase in F1-score. This finding highlights the advantage of training on multiple modalities, even when a particular modality is absent during inference.

To investigate hippocampal volume changes in moderate to severe traumatic brain injury (TBI) patients compared to healthy controls and assess their association with post-traumatic epilepsy (PTE), focusing on age-related effects. Imaging and demographic data for TBI patients were obtained from the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) database; healthy controls matched by age and sex were sourced from Alzheimer's Disease Neuroimaging Initiative (ADNI), the National Institute of Mental Health (NIMH) Intramural Healthy Volunteer Dataset, the Parkinson's Progression Markers Initiative (PPMI), and the Autism Brain Imaging Data Exchange (ABIDE). MRI images for TBI subjects were obtained within 14-32 days post-injury. MRI data were preprocessed and segmented using FreeSurfer's recon-all pipeline and the hippocampal subfield segmentation module. TBI patients showed significantly smaller average hippocampal volumes than controls (β = -191.0, p = 8.33e-04, FDR p = 1.47e-03, Cohen's d = -0.36), notably in the right and left CA1 head regions. No significant hippocampal volume differences were found between TBI+ and TBI- patients overall. In patients aged 60+, TBI+ patients had significantly larger volumes in the right CA1 head than TBI- patients (β = 57.43, p = 0.0300, FDR p = 0.040; Cohen's d = 1.06). Hippocampal atrophy is evident in TBI patients but not directly linked to PTE development in most age groups. In patients over 60, hypertrophy in the right CA1 head may be associated with PTE, suggesting age and regional hippocampal changes influence epilepsy risk post-TBI. These findings highlight the complexity of hippocampal involvement in PTE and suggest potential shared mechanisms with neurodegenerative disorders such as Alzheimer's disease.

The Parkinson's Progression Marker Initiative is an international multicenter study whose main goal is investigating markers for Parkinson's disease (PD) progression as part of a path to a treatment for the disease. This manuscript describes the baseline genetic architecture of this study, providing not only a catalog of disease-linked variants and mutations, but also quantitative measures with which to adjust for population structure. Three hundred eighty-three newly diagnosed typical PD cases, 65 atypical PD and 178 healthy controls, from the Parkinson's Progression Marker Initiative study have been genotyped on the NeuroX or Immunochip arrays. These data are freely available to all researchers interested in pursuing PD research within the Parkinson's Progression Marker Initiative. The Parkinson's Progression Marker Initiative represents a study population with low genetic heterogeneity. We recapitulate known PD associations from large-scale genome-wide association studies and refine genetic risk score models for PD predictability (area under the curve, ∼0.74). We show the presence of six LRRK2 p.G2019S and nine GBA p.N370S mutation carriers. The Parkinson's Progression Marker Initiative study and its genetic data are useful in studies of PD biomarkers. The genetic architecture described here will be useful in the analysis of myriad biological and clinical traits within this study.

For 25 years, The Michael J. Fox Foundation has made it our mission to harness the Parkinson's community's grit and generosity and turn it into tangible progress toward better treatments and cures. People and families living with Parkinson's, academic and industry researchers, treating physicians and allied health professionals, and regulators all have jumped in feet-first to get this job done. I am endlessly grateful for the vast wisdom and energy they have poured into our shared cause. This combined strength is nowhere more evident than in the Parkinson's Progression Markers Initiative (PPMI). Fifteen years in, two sets of experts — those who choose to be part of taking down Parkinson's and those who, like me, are here involuntarily — have formed a powerful partnership that is steadily dismantling the secrets of this complex disease. Quite frankly, there's nothing more rock-and-roll than PPMI: out of the gates a pioneering, bold, and astronomically expensive undertaking. “While there is no guarantee that validated biomarkers will result from PPMI,” we wrote in the 2010 press release announcing the study's launch, “their importance to therapeutic progress requires that we accept this risk.” Plenty of voices told us it would never work. Others underestimated the degree of difficulty, like the science reporter who patiently listened to us describing PPMI's ambitious goals, then wrapped up the conversation with a breezy “Call me when you find the biomarker!” That call didn't happen the next day, year, or decade. There were gaps — human-sized gaps — in our understanding of Parkinson's. But while we couldn't be certain when or even if the science would pan out, we were pretty sure we could motivate people with Parkinson's, those at risk, and control volunteers to join our study and be part of the solution they seek. Sure enough, the earliest participants to enroll in PPMI — those first 400 de novo patients and 200 age- and gender-matched controls — would eagerly blaze a path for tens of thousands to come. Scientists now had real-time access to deep, rich, and highly standardized clinical data and precious biosamples from a cohort that would come to span the entire continuum of Parkinson's risk, onset, and progression. Liberated from the challenge of assembling and funding small, ad hoc cohorts, they could turn their full attention to answering longstanding questions about the insidious biology of the human disease. Visit by visit, test by test, study participants helped us build an incredible data set. Researchers avidly leveraged it, with millions of data downloads powering thousands of individual studies worldwide. In addition, in 2023, the Foundation's CEO and co-founder Debi Brooks and founding Board member Nelle Fortenberry came to my home for an urgent meeting. Todd Sherer, chief mission officer, joined us remotely. Together, they shared the news that PPMI had done what it set out to do: validated a test to objectively and accurately detect Parkinson's biology in living people, even years before they had experienced a single symptom of the disease. Outside of milestone life events like my wedding or the births of my children, I'm not sure I have ever been so moved. I remember leaning forward to kiss Todd's forehead on my laptop screen. This was a game-changing moment for the field, not least for patients (myself included) who have had the singularly devastating experience of receiving a bolt-from-the-blue diagnosis and learning that by that point, more than half the dopamine cells in our brains have already been laid waste. The biomarker kicked down the door to a totally different way forward. From detecting Parkinson's before symptoms develop, to testing next-generation drugs against a wide range of biological targets, to the dream of precision medicine and even disease prevention, we are on the threshold of big things. Fueling PPMI on its path are the efforts of our nonprofit and philanthropic partners, scores of biotech and pharmaceutical companies, and hundreds of thousands of individual donors. For 15 years, alongside study volunteers and leadership, this incredible crew has built toward a cure that is now tantalizingly within reach. Their dedication runs deep — just like my gratitude for them. PPMI was once a bold risk. It's now widely recognized as the premier study in Parkinson's, a transformative resource that researchers can leverage to accelerate measurable scientific progress. But the contributions of strength and courage that got us here are impossible to measure. Thanks to the phenomenal Parkinson's community, PPMI is truly the study that is changing everything — and creating unprecedented opportunity for more breakthroughs. So, let's keep rocking and rolling until Parkinson's is only a memory. Michael J. Fox: Writing – original draft; writing – review and editing.

Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders substantially impact the quality of life and burden healthcare systems and society. The demographic characteristics, and machine learning approaches have now been employed to diagnose these illnesses; however, they possess accuracy limitations. Therefore, the authors have developed ranking-based ensemble approach based on the weighted strategy of deep learning classifiers. The whole modeling procedure of the proposed approach incorporates three phases. In phase I, preprocessing techniques are applied to clean the noise in datasets to make it standardized according to deep learning models as it significantly impacts their performance. In phase II, five deep learning models are selected for classification and calculation of prediction results. In phase III, a ranking-based ensemble approach is proposed to ensemble the results of the five models after calculating the ranks and weights of them. In addition, the Magnetic Resonance Imaging (MRI) datasets named Alzheimer's Disease Neuroimaging Initiative (ADNI) for AD classification and Parkinson's Progressive Marker Initiative (PPMI) for PD classification are selected to validate the proposed approach. Furthermore, the proposed method achieved the classification accuracy on AD- Cognitive Normals (CN) at 97.89%, AD- Mild Cognitive Impairment (MCI) at 99.33% and CN-MCI at 99.44% and on PD-CN at 99.22%, PD- Scans Without Evidence of Dopaminergic Effect (SWEDD) at 97.56% and CN-SWEDD at 98.22% respectively. Also, the multi-class classification shows the promising accuracy of 97.18% for AD and 97.85% for PD for the proposed framework. The findings of the study show that the proposed deep learning-based ensemble technique is competitive for AD and PD prediction in both multiclass and binary class classification. Furthermore, the proposed approach enhances generalization performance in diagnosing neurodegenerative diseases and performs better than existing approaches.

Clinical and dopamine transporter imaging characteristics of non-manifest LRRK2 and GBA mutation carriers in the Parkinson's Progression Markers Initiative (PPMI): a cross-sectional study

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.

The Parkinson's Progression Marker Initiative (PPMI), sponsored by The Michael J. Fox Foundation for Parkinson's Research, is designed to develop disease biomarkers to accelerate Parkinson's disease (PD) therapeutics. During the past 15 years PPMI has acquired comprehensive clinical, imaging, biomarker and genetic data on thousands of participants that has guided clinical therapeutic studies for PD and related synucleinopathies. The study has established standardized strategies for acquisition and analysis of data and set a standard for data sharing and open-source publication. PPMI has by design consistently evolved to incorporate new knowledge and science. A key example is that PPMI data has enabled the transition from clinical to biologic definition of synucleinopathy. The study continues to expand our understanding of disease biology particularly in precise PD subsets in both in-clinic and a rapidly expanding remote participant cohort, using myPPMI, an online portal. This special issue reviews PPMI's continuing contribution to early detection, understanding disease biologic and staging, and innovative therapeutic strategies in PD and related synucleinopathies. ANN NEUROL 2026.

Studies reporting alpha-synuclein seed amplification assay (aSyn-SAA) results are often cross-sectional. Here we investigated the intra-individual consistency of aSyn-SAA results over time from participants in the Parkinson's Progression Marker Initiative (PPMI). A total of 1238 participants had >1 CSF aSyn-SAA result for analysis (Parkinson's disease [PD]=633, prodromal =563, healthy control [HC]=42) which were collected over a median (min, max) of 2.0 (0.4, 11.4) years. Emphasis was placed on evaluating consistency in less common results such as aSyn-SAA- PD participants, aSyn-SAA+ HC and conversion rates from aSyn-SAA negative to positive results prodromal participants. Of aSyn-SAA+ PD participants, 96% (474/493, 95%CI 94-98%) remained positive in subsequent samples, and 92% (116/126, 95%CI 86-96%) of aSyn-SAA- PD participants remained negative. 99% (303/307, 95%CI 97-99%) of aSyn-SAA+ prodromal participants remained positive, and 95% (234/247, 95%CI 91-97%) of aSyn-SAA- prodromal participants remained negative. 89% (16/18, 95%CI 67-97%) of aSyn-SAA+ HC participants remained positive, and 87% (20/23, 95%CI 68-95%) of aSyn-SAA- HC participants remained negative. These results confirm a high consistency of aSyn-SAA results over time, even in less expected results.

Pathology in the Parkinson's Progression Markers Initiative; a Finale but also a start

The Path to Prevention (P2P) platform trial is a multicenter, multi-regimen, proof of concept, phase 2A, randomized clinical trial evaluating the safety and early efficacy of investigational products for the treatment of early-stage Neuronal Alpha-Synuclein disease (NSD) populations. The P2P trial is nested within the Parkinson's Progression Markers Initiative (PPMI), which means that its conceptualization, design, and implementation are based on learnings from the PPMI study. P2P will recruit eligible participants from the PPMI cohort. P2P will also leverage PPMI infrastructure including PPMI study sites and PPMI study cores including the site management, data management, biorepository, imaging, statistical, and data science cores for study execution. This paper reviews the conceptual design of P2P focusing on its relationship with PPMI to demonstrate how the connectivity between PPMI and P2P is essential for the success of the P2P study. P2P is an important step forward in clarifying the clinical trial design and regulatory path for interventions in the early-stage NSD population. ANN NEUROL 2026.

BackgroundParkinson’s Disease (PD) psychosis refers to the spectrum of illusions, formed hallucinations and delusions that occur in PD. Visual hallucinations and illusions are thought to be caused by specific cognitive...

Serum neurofilament light chain (NfL) levels, a marker of axonal damage, are generally elevated in neurodegenerative conditions, but results in idiopathic Parkinson's disease (iPD) have been inconsistent. The p.A53T SNCA mutation usually leads to a severe form of PD. The aim of this work was to assess the possible use of serum NfL as a marker of ongoing neurodegeneration in A53T-PD. Using a propensity score matching-based technique with data from the Parkinson's Progression Markers Initiative, we matched, in a 3:1 ratio, A53T-PD participants to iPD patients and healthy control subjects (HCs) by age and disease duration, where applicable. After matching, 18 A53T-PD, 54 iPD, and 54 HC subjects were analyzed. Serum NfL was higher in A53T-PD (9.82 [8.02-18.1] pg/ml) compared with HCs (7.54 [5.76-10.1], P = 0.008) or iPD patients (8.59 [6.86-11.1], P = 0.077) (overall P = 0.049). A53T-PD patients have high NfL levels, which could potentially be used as an outcome in clinical trials with disease-modifying agents. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A history of traumatic brain injury (TBI) is associated with an increased risk of developing neurodegenerative disorders, including Parkinson's Disease (PD). However, TBI's influences on disease progression remain underassessed. This study explored whether a history of TBI influences the progression of pathological and clinical outcomes up to 5 years of follow-up in individuals with early PD. Longitudinal data were extracted from the Parkinson's Progression Markers Initiative (PPMI) and the PostCEPT observational study. Participants in PostCEPT had complete head injury data, while PPMI participants were eligible if they completed the head injury section of the PD Risk Factor Questionnaire (n = 208). Principal component analysis was used to derive composite scores of cognitive ability and mood dysfunction, with motor outcomes calculated using the Movement Disorders Society Unified Parkinson's Disease Rating Scale. Progression of clinical and pathological outcomes up to 5 years and 4 years following study entry were compared, including subset analyses in PPMI examining injury severity. Individuals with a history of TBI in the PPMI dataset exhibited a younger age of onset; however, a history of TBI did not affect progression rates of any assessed variables across both cohorts. Exploratory analysis determined that injury severity significantly predicted striatal dopamine transporter binding but accounted for only a small portion of outcome variance. While the history of TBI was associated with earlier PD onset, it did not correspond to a differential disease course. However, given differences in TBI characterisation between cohorts, additional research must be conducted to validate these findings.

The Parkinson's Progression Markers Initiative (PPMI) has remained at the vanguard of Parkinson's disease (PD) research through evolving recruitment strategies that mirror advances in PD biology. PPMI was initially focused on untreated, early-stage PD. Over time, it has expanded to include genetic cohorts, prodromal participants identified via olfactory testing and risk factor questionnaires targeting people with rapid eye movement (REM) sleep behavior disorder (RBD). Most recently, PPMI has focused on the biology underlying PD and sought to recruit individuals defined by neuronal alpha-synuclein disease (NSD) biomarkers. This evolution from clinical to biological enrollment demonstrates the feasibility of biomarker-driven cohort design and establishes a scalable model for early detection and prevention research in neurodegenerative disease. ANN NEUROL 2026.