Sequence of clinical and neurodegeneration events in Parkinson's disease progression

Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions

A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data

The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.

Data-driven sequence of cognitive decline in people with Parkinson's disease

BACKGROUND

Understanding the sequential progression of cognitive impairments in Parkinson's disease (PD) is crucial for elucidating neuropathological underpinnings, refining the assessment of PD-related cognitive decline stages and enhancing early identification for targeted interventions. The first aim of this study was to use an innovative event-based modeling (EBM) analytic approach to estimate the sequence of cognitive declines in PD. The second aim was to validate the EBM by examining associations with EBM-derived individual-specific estimates of cognitive decline severity and performance on independent cognitive screening measures.

METHODS

This cross-sectional observational study included 99 people with PD who completed a neuropsychological battery. Individuals were classified as meeting the criteria for mild cognitive impairment (PD-MCI) or subtle cognitive decline by consensus. An EBM was constructed to compare cognitively healthy individuals with those with PD-MCI or subtle cognitive disturbances. Multivariable linear regression estimated associations between the EBM-derived stage of cognitive decline and performance on two independent cognitive screening tests.

RESULTS

The EBM estimated that tests assessing executive function and visuospatial ability become abnormal early in the sequence of PD-related cognitive decline. Each higher estimated stage of cognitive decline was associated with approximately 0.24 worse performance on the Dementia Rating Scale (p<0.001) and 0.26 worse performance on the Montreal Cognitive Assessment (p<0.001) adjusting for demographic and clinical variables.

CONCLUSION

Findings from this study will have important clinical implications for practitioners, on specific cognitive tests to prioritise, when conducting neuropsychological evaluations with people with PD. Results also highlight the importance of frontal-subcortical system disruption impacting executive and visuospatial abilities.

Hypoperfusion in Supramarginal and Orbital Gyrus, Position Discrimination Test, and Microsaccades as a Predictor of Pisa Syndrome in Parkinson's Disease

Patients with Parkinson's disease (PD) experience significantly reduced quality of life when PD is complicated with Pisa syndrome (PS). PS is a postural abnormality associated with a lateral bending of the trunk, causing the patient to lean to one side. Microsaccades during fixation are transmitted to the visual cortex, and this gaze movement may be impaired in PD. We aimed to detect presymptomatic signs of PS. We enrolled 50 patients with PD without dementia and investigated the visual systems in patients with concurrent PD and PS based on a Romberg ratio of<1.0. Gaze analysis, pupil diameter, stabilization tests, neuropsychological tests, and cerebral perfusion scintigraphy were reviewed and statistically analyzed. Two years later, we divided the patients into three groups as follows: PISA++ (patients who had PS at enrollment), PISA-+ (patients without PS that developed PS during the 2-year period), and PISA-- (patients without PS that did not develop PS during the 2-year period). The PISA-+ group exhibited a significantly higher daily levodopa dose and longer fixations, as well as lower position discrimination, Wechsler Adult Intelligence Scale-Third Edition blocking, and blood flow in the left supramarginal and orbital gyri than that in the PISA-- group. The PISA++ group showed a significantly longer fixation time and lower Mini-Mental State Examination score, Romberg ratio of area, amplitude, velocity of microsaccades, and blood flow in the left precuneus and cuneus than that in the PISA-+ group. Before the onset of PS, hypoperfusion occurred in the correlative visual cortex and the position discrimination test. Patients with PS have reduced saccades and slow microsaccades.

Longitudinal Changes in the Retinal Nerve Fiber Layer Thickness in Amyotrophic Lateral Sclerosis and Parkinson's Disease

BACKGROUND AND PURPOSE

There is increasing evidence that the anterior visual pathways are involved in neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD). This study investigated longitudinal changes in retinal nerve fiber layer (RNFL) thickness in patients with ALS and PD with the aim of better understanding their roles as biomarkers of disease progression.

METHODS

This study recruited 21 ALS patients, 19 age-matched PD patients, and 21 agematched healthy controls. Patient demographics and clinical scores relating to the respective diseases were documented. The RNFL thickness was measured using optical coherence tomography at baseline and after 6 months.

RESULTS

At baseline, the RNFL in the superior quadrant was significantly thinner in the patients with ALS than in healthy controls (109.90±22.41 µm vs. 127.81±17.05 µm [mean±standard deviation], p=0.008). The RNFL thickness did not differ significantly between the ALS and PD patients or between the PD patients and healthy controls. At 6 months, there was further significant RNFL thinning in patients with ALS, for both the overall thickness (baseline: median=94.5 µm, range=83.0-106.0 µm; follow-up: median=93.5 µm, range=82.5-104.5 µm, p=0.043) and the thickness in the inferior quadrant (median=126 µm, range=109.5-142.5 µm; and median=117.5 µm, range=98.5-136.5 µm; respectively, p=0.032). However, these changes were not correlated with the ALS functional scores. In contrast, the patients with PD did not demonstrate a significant change in RNFL thickness between the two time points.

CONCLUSIONS

The RNFL thickness is a promising biomarker of disease progression in patients with ALS but not in those with PD, which has a slower disease progression.

Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome

Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.

Identification of four biotypes in temporal lobe epilepsy via machine learning on brain images

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.

Distinct spatiotemporal patterns of cortical thinning in Alzheimer's disease-type cognitive impairment and subcortical vascular cognitive impairment

Previous studies on Alzheimer's disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders) underwent brain magnetic resonance imaging (MRI), amyloid positron emission tomography, and neuropsychological tests. Using MRI, this study measures cortical thickness in five brain regions (medial temporal, inferior temporal, posterior medial parietal, lateral parietal, and frontal areas) and utilizes the Subtype and Stage Inference (SuStaIn) model to predict the most probable subtype and stage for each participant. SuStaIn identifies two distinct cortical thinning patterns in ADCI (medial temporal: 65.8%, diffuse: 34.2%) and SVCI (frontotemporal: 47.1%, parietal: 52.9%) patients. The medial temporal subtype of ADCI shows a faster decline in attention, visuospatial, visual memory, and frontal/executive domains than the diffuse subtype (p-value < 0.01). However, there are no significant differences in longitudinal cognitive outcomes between the two subtypes of SVCI. Our study provides valuable insights into the distinct spatiotemporal patterns of cortical thinning in patients with ADCI and SVCI, suggesting the potential for individualized therapeutic and preventive strategies to improve clinical outcomes.

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

Neurodegeneration with Brain Iron Accumulation Disorders and Retinal Neurovascular Structure

BACKGROUND

The unique neurovascular structure of the retina has provided an opportunity to observe brain pathology in many neurological disorders. However, such studies on neurodegeneration with brain iron accumulation (NBIA) disorders are lacking.

OBJECTIVES

To investigate NBIA's neurological and ophthalmological manifestations.

METHODS

This cross-sectional study was conducted on genetically confirmed NBIA patients and an age-gender-matched control group. The thickness of retinal layers, central choroidal thickness (CCT), and capillary plexus densities were measured by spectral domain-optical coherence tomography (SD-OCT) and OCT angiography, respectively. The patients also underwent funduscopy, electroretinography (ERG), visual evoked potential (VEP), and neurological examination (Pantothenate-Kinase Associated Neurodegeneration-Disease Rating Scale [PKAN-DRS]). The generalized estimating equation model was used to consider inter-eye correlations.

RESULTS

Seventy-four patients' and 80 controls' eyes were analyzed. Patients had significantly decreased visual acuity, reduced inner or outer sectors of almost all evaluated layers, increased CCT, and decreased vessel densities, with abnormal VEP and ERG in 32.4% and 45.9%, respectively. There were correlations between visual acuity and temporal peripapillary nerve fiber layer (positive) and between PKAN-DRS score and disease duration (negative), and scotopic b-wave amplitudes (positive). When considering only the PKAN eyes, ONL was among the significantly decreased retinal layers, with no differences in retinal vessel densities. Evidence of pachychoroid was only seen in patients with Kufor Rakeb syndrome.

CONCLUSION

Observing pathologic structural and functional neurovascular changes in NBIA patients may provide an opportunity to elucidate the underlying mechanisms and differential retinal biomarkers in NBIA subtypes in further investigations. © 2023 International Parkinson and Movement Disorder Society.

Primary progressive aphasia: six questions in search of an answer

Here, we review recent progress in the diagnosis and management of primary progressive aphasia-the language-led dementias. We pose six key unanswered questions that challenge current assumptions and highlight the unresolved difficulties that surround these diseases. How many syndromes of primary progressive aphasia are there-and is syndromic diagnosis even useful? Are these truly 'language-led' dementias? How can we diagnose (and track) primary progressive aphasia better? Can brain pathology be predicted in these diseases? What is their core pathophysiology? In addition, how can primary progressive aphasia best be treated? We propose that pathophysiological mechanisms linking proteinopathies to phenotypes may help resolve the clinical complexity of primary progressive aphasia, and may suggest novel diagnostic tools and markers and guide the deployment of effective therapies.

Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease

BACKGROUND

Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers.

OBJECTIVE

To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).

METHODS

Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation).

RESULTS

PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms.

CONCLUSIONS

Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.

Estimating the sequence of biomarker changes in Parkinson's disease

OBJECTIVE

To estimate the sequence of several common biomarker changes in Parkinson's disease (PD) using a novel data-driven method.

METHODS

We included 374 PD patients and 169 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI). Biomarkers, including the left putamen striatal binding ratio (SBR), right putamen SBR, left caudate SBR, right caudate SBR, cerebrospinal fluid (CSF) α-synuclein, and serum neurofilament light chain (NfL), were selected in our study. The discriminative event-based model (DEBM) was utilized to model the sequence of biomarker changes and establish the disease progression timeline. The estimated disease stages for each subject were obtained through cross-validation. The associations between the estimated disease stages and the clinical symptoms of PD were explored using Spearman's correlation.

RESULTS

The left putamen is the earliest biomarker to become abnormal among the selected biomarkers, followed by the right putamen, CSF α-synuclein, right caudate, left caudate, and serum NfL. The estimated disease stages are significantly different between PD and HC and yield a high accuracy for distinguishing PD from HC, with an area under the curve (AUC) of 0.98 (95% confidence interval 0.97-0.99), a sensitivity of 0.95, and a specificity of 0.92. Moreover, the estimated disease stages correlate with motor experiences of daily living, motor symptoms, autonomic dysfunction, and anxiety in PD patients.

CONCLUSION

We determined the sequence of several common biomarker changes in PD using DEBM, providing data-driven evidence of the disease progression of PD.

Symptom-led staging for semantic and non-fluent/agrammatic variants of primary progressive aphasia

INTRODUCTION

Here we set out to create a symptom-led staging system for the canonical semantic and non-fluent/agrammatic variants of primary progressive aphasia (PPA), which present unique diagnostic and management challenges not well captured by functional scales developed for Alzheimer's disease and other dementias.

METHODS

An international PPA caregiver cohort was surveyed on symptom development under six provisional clinical stages and feedback was analyzed using a mixed-methods sequential explanatory design.

RESULTS

Both PPA syndromes were characterized by initial communication dysfunction and non-verbal behavioral changes, with increasing syndromic convergence and functional dependency at later stages. Milestone symptoms were distilled to create a prototypical progression and severity scale of functional impairment: the PPA Progression Planning Aid ("PPA-Squared").

DISCUSSION

This work introduces a symptom-led staging scheme and functional scale for semantic and non-fluent/agrammatic variants of PPA. Our findings have implications for diagnostic and care pathway guidelines, trial design, and personalized prognosis and treatment for PPA.

HIGHLIGHTS

We introduce new symptom-led perspectives on primary progressive aphasia (PPA). The focus is on non-fluent/agrammatic (nfvPPA) and semantic (svPPA) variants. Foregrounding of early and non-verbal features of PPA and clinical trajectories is featured. We introduce a symptom-led staging scheme for PPA. We propose a prototype for a functional impairment scale, the PPA Progression Planning Aid.

Molecular mechanisms underlying human spatial cognitive ability revealed with neurotransmitter and transcriptomic mapping

Mental rotation, one of the cores of spatial cognitive abilities, is closely associated with spatial processing and general intelligence. Although the brain underpinnings of mental rotation have been reported, the cellular and molecular mechanisms remain unexplored. Here, we used magnetic resonance imaging, a whole-brain spatial distribution atlas of 19 neurotransmitter receptors, transcriptomic data from Allen Human Brain Atlas, and mental rotation performances of 356 healthy individuals to identify the genetic/molecular foundation of mental rotation. We found significant associations of mental rotation performance with gray matter volume and fractional amplitude of low-frequency fluctuations in primary visual cortex, fusiform gyrus, primary sensory-motor cortex, and default mode network. Gray matter volume and fractional amplitude of low-frequency fluctuations in these brain areas also exhibited significant sex differences. Importantly, spatial correlation analyses were conducted between the spatial patterns of gray matter volume or fractional amplitude of low-frequency fluctuations with mental rotation and the spatial distribution patterns of neurotransmitter receptors and transcriptomic data, and identified the related genes and neurotransmitter receptors associated with mental rotation. These identified genes are localized on the X chromosome and are mainly involved in trans-synaptic signaling, transmembrane transport, and hormone response. Our findings provide initial evidence for the neural and molecular mechanisms underlying spatial cognitive ability.

Identification of different MRI atrophy progression trajectories in epilepsy by subtype and stage inference

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional multicentre structural MRI dataset of 696 people with epilepsy and 118 control subjects. We use an innovative machine-learning algorithm, Subtype and Stage Inference, to develop a novel data-driven disease taxonomy, whereby epilepsy subtypes correspond to distinct patterns of spatiotemporal progression of brain atrophy.In a discovery cohort of 814 individuals, we identify two subtypes common to focal and idiopathic generalized epilepsies, characterized by progression of grey matter atrophy driven by the cortex or the basal ganglia. A third subtype, only detected in focal epilepsies, was characterized by hippocampal atrophy. We corroborate external validity via an independent cohort of 254 people and confirm that the basal ganglia subtype is associated with the most severe epilepsy.Our findings suggest fundamental processes underlying the progression of epilepsy-related brain atrophy. We deliver a novel MRI- and AI-guided epilepsy taxonomy, which could be used for individualized prognostics and targeted therapeutics.

Discovery of Small Molecule PARKIN Activator from Antipsychotic/Anti-neuropsychiatric Drugs as Therapeutics for PD: an In Silico Repurposing Approach

Although there is presently no cure for Parkinson's disease (PD), the available therapies are only able to lessen symptoms and preserve the quality of life. Around 10 million people globally had PD as of 2020. The widely used standard drug has recently been revealed to have several negative effects. Additionally, there is a dearth of innovative compounds entering the market as a result of subpar ADMET characteristics. Drug repurposing provides a chance to reenergize the sluggish drug discovery process by identifying new applications for already-approved medications. As this strategy offers a practical way to speed up the process of developing alternative medications for PD. This study used a computer-aided technique to select therapeutic agent(s) from FDA-approved neuropsychiatric/psychotic drugs that can be adopted in the treatment of Parkinson's disease. In the current work, a computational approach via molecular docking, density functional theory (DFT), and pharmacokinetics were used to identify possible (anti)neuropsychiatric/psychotic medications for the treatment of PD. By using molecular docking, about eight (anti)neuropsychiatric/psychotic medications were tested against PARKIN, a key protein in PD. Based on the docking score, the best ligand in the trial was determined. The top hits were compared to the reference ligand levodopa (L-DOPA). A large proportion of the drugs displayed binding affinity that was relatively higher than L-DOPA. Also, DFT analysis confirms the ligand-receptor interactions and the molecular charge transfer. All the compounds were found to obey Lipinski's rule with acceptable pharmacokinetic properties. The current study has revealed the effectiveness of antineuropsychiatric/antipsychotic drugs against PARKIN in the treatment of PD and lumateperone was revealed to be the most promising candidate interacting with PARKIN.

Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease

BACKGROUND

Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence.

METHODS

We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods.

RESULTS

Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12).

CONCLUSIONS

In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials.

The temporal event-based model: Learning event timelines in progressive diseases

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.

Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease

Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.

Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation

Background

Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method

Participants were 48 well-characterized PD patients (M_{baseline age} = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.

Results

An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.

Conclusion

Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.

Impact of the Dopamine System on Long-Term Cognitive Impairment in Parkinson Disease: An Exploratory Study

Background

Little is known about the impact of the dopamine system on development of cognitive impairment (CI) in Parkinson disease (PD).

Objectives

We used data from a multi-site, international, prospective cohort study to explore the impact of dopamine system-related biomarkers on CI in PD.

Methods

PD participants were assessed annually from disease onset out to 7 years, and CI determined by applying cut-offs to four measures: (1) Montreal Cognitive Assessment; (2) detailed neuropsychological test battery; (3) Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) cognition score; and (4) site investigator diagnosis of CI (mild cognitive impairment or dementia). The dopamine system was assessed by serial Iodine-123 Ioflupane dopamine transporter (DAT) imaging, genotyping, and levodopa equivalent daily dose (LEDD) recorded at each assessment. Multivariate longitudinal analyses, with adjustment for multiple comparisons, determined the association between dopamine system-related biomarkers and CI, including persistent impairment.

Results

Demographic and clinical variables associated with CI were higher age, male sex, lower education, non-White race, higher depression and anxiety scores and higher MDS-UPDRS motor score. For the dopamine system, lower baseline mean striatum dopamine transporter values (P range 0.003-0.005) and higher LEDD over time (P range <0.001-0.01) were significantly associated with increased risk for CI.

Conclusions

Our results provide preliminary evidence that alterations in the dopamine system predict development of clinically-relevant, cognitive impairment in Parkinson's disease. If replicated and determined to be causative, they demonstrate that the dopamine system is instrumental to cognitive health status throughout the disease course.

TRIAL REGISTRATION

Parkinson's Progression Markers Initiative is registered with ClinicalTrials.gov (NCT01141023).

Biomimetic Biomembrane Encapsulation and Targeted Delivery of a Nitric Oxide Release Platform for Therapy of Parkinson's Disease

The existence of the blood-brain barrier (BBB) and the complex inflammatory environment in the brain are two major obstacles in the treatment of Parkinson's disease (PD). As a target group, we modified the red blood cell membrane (RBCM) on the surface of upconversion nanoparticles (UCNPs) in this study to effectively target the brain. Mesoporous silicon, coated with UCNPs (UCM), was loaded with S-nitrosoglutathione (GSNO) as the nitric oxide (NO) donor. Then, UCNPs were excited to emit green light (540 nm) by 980 nm near-infrared (NIR). In addition, it produced a light-responsive anti-inflammatory effect by promoting the release of NO from GSNO and lowering the brain's level of proinflammatory factors. A series of experiments demonstrated that this strategy could effectively mitigate the inflammatory response damage of neurons in the brain.

Presence but not the timing of onset of REM sleep behavior disorder distinguishes evolution patterns in Parkinson's disease

BACKGROUND

Rapid eye movement (REM) sleep behavior disorder (RBD) could develop preceding or come after motor symptoms during Parkinson's disease (PD). It remains unknown that whether PD with different timing of RBD onset relative to motor symptoms suggests different spatiotemporal sequence of neurodegeneration. This study aimed to explore the sequence of disease progression in crucially involved brain regions in PD with different timing of RBD onset.

METHOD

We recruited 157 PD, 16 isolated RBD (iRBD), and 78 healthy controls. PD patients were identified as (1) PD with RBD preceding motor symptoms (PD-preRBD, n = 50), (2) PD with RBD posterior to motor symptoms (PD-postRBD, n = 31), (3) PD without RBD (PD-nonRBD, n = 75). The volumes of crucial brain regions, including the basal ganglia and limbic structures in T1-weighted imaging, and the contrast-noise-ratios of locus coeruleus (LC) and substantia nigra (SN) in neuromelanin-sensitive magnetic resonance imaging, were extracted. To simulate the sequence of disease progression for cross-sectional data, an event-based model was introduced to estimate the maximum likelihood sequence of regions' involvement for each group. Then, a statistical parameter, the Bhattacharya coefficient (BC), was used to evaluate the similarity of the sequence.

RESULTS

The model predicted that SN occupied the highest likelihood in the maximum likelihood sequence of disease progression in the all PD subgroups, while LC was specifically positioned earlier to SN in iRBD, a prodromal phase of PD. Subsequent early involvement of LC was observed in the both PD-preRBD and PD-postRBD. In contrast, atrophy in the para-hippocampal gyrus but relatively intact LC in the early stage was demonstrated in PD-nonRBD. Then, the similarity comparisons indicated higher BC between PD-postRBD and PD-preRBD (BC = 0.76) but lower BC between PD-postRBD and PD-nonRBD group (BC = 0.41). iRBD had higher BC against PD-preRBD (BC = 0.66) and PD-postRBD (BC = 0.63) but lower BC against PD- nonRBD (BC = 0.48).

CONCLUSION

The spatiotemporal sequence of neurodegeneration between PD-pre and PD-post were similar but distinct from PD-nonRBD. The presence of RBD may be the essential factor for differentiating the degeneration patterns of PD, but the timing of RBD onset has currently proved to be not.

Redox Modulation of Mitochondrial Proteins in the Neurotoxicant Models of Parkinson's Disease

Significance: Mitochondrial proteins regulate the oxidative phosphorylation, cellular metabolism, and free radical generation. Redox modulation alters the mitochondrial proteins and instigates the damage to dopaminergic neurons. Toxicants contribute to Parkinson's disease (PD) pathogenesis in conjunction with aging and genetic factors. While oxidative modulation of a number of mitochondrial proteins is linked to xenobiotic exposure, little is known about its role in the toxicant-induced PD. Understanding the role of redox modulation of mitochondrial proteins in complex cellular events leading to neurodegeneration is highly relevant. Recent Advances: Many toxicants are shown to inhibit complex I or III and elicit free radical production that alters the redox status of mitochondrial proteins. Implication of redox modulation of the mitochondrial proteins makes them a target to comprehend the underlying mechanism of toxicant-induced PD. Critical Issues: Owing to multifactorial etiology, exploration of onset and progression and treatment outcomes needs a comprehensive approach. The article explains about a few mitochondrial proteins that undergo redox changes along with the promising strategies, which help to alleviate the toxicant-induced redox imbalance leading to neurodegeneration. Future Directions: Although mitochondrial proteins are linked to PD, their role in toxicant-induced parkinsonism is not yet completely known. Preservation of antioxidant defense machinery could alleviate the redox modulation of mitochondrial proteins. Targeted antioxidant delivery, use of metal chelators, and activation of nuclear factor erythroid 2-related factor 2, and combinational therapy that encounters multiple free radicals, could ameliorate the redox modulation of mitochondrial proteins and thereby PD progression.

Development, wiring and function of dopamine neuron subtypes

The midbrain dopamine (mDA) system is composed of molecularly and functionally distinct neuron subtypes that mediate specific behaviours and are linked to various brain diseases. Considerable progress has been made in identifying mDA neuron subtypes, and recent work has begun to unveil how these neuronal subtypes develop and organize into functional brain structures. This progress is important for further understanding the disparate physiological functions of mDA neurons and their selective vulnerability in disease, and will ultimately accelerate therapy development. This Review discusses recent advances in our understanding of molecularly defined mDA neuron subtypes and their circuits, ranging from early developmental events, such as neuron migration and axon guidance, to their wiring and function, and future implications for therapeutic strategies.

Efficacy of rehabilitation robot-assisted gait training on lower extremity dyskinesia in patients with Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

Robot-assisted training is used as a new rehabilitation training method for the treatment of motor dysfunction in neurological diseases. Robot-assisted gait training (RAGT) has been reported to treat motor dysfunction in patients with Parkinson's disease (PD). The purpose of this study was to summarize previous clinical studies comparing the effectiveness of RAGT and conventional training for lower extremity dyskinesia in PD patients.

METHODS

PubMed, Cochrane library, Scopus, Embase, EBSCO, Web of Science, CNKI, and Wanfang databases were searched. This study included all randomized controlled trials (RCTs) compared lower extremity RAGT with conventional training on motor impairment in PD patients. The retrieval time limit is from the establishment of the database to October 2022. Two researchers independently screened the literature, extracted data, assessed the risk of bias of included studies, and then used RevMan 5.3 software for meta-analysis.

RESULTS

A total of 14 RCTs with 572 patients were included. The results showed that compared with the control group, RAGT significantly improved the motor function evaluation-related indicators 10MWT, 6MWT, TUG and UPDRS III, 10MWT [MD= 0.08, 95 % CI (0.01, 0.14), P = 0.03], 6MWT [MD= 42.83, 95 % CI (22.05, 63.62), P < 0.0001], TUG[MD= -1.81, 95 % CI (-2.55, -1.08), P < 0.0001], UPDRS III [MD= - 3.82, 95 % CI (-4.27, -3.37), P < 0.00001]; For the balance function evaluation index BBS [MD= 3.33, 95 % CI (2.76, 3.89), P < 0.00001], the above results were significantly different significance.

CONCLUSION

The currently limited evidence suggests that RAGT provides evidence for the effectiveness of lower extremity motor function and balance dysfunction, and RAGT can significantly improve motor and balance function in PD patients.

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches

Neurodegenerative diseases of the brain pose a major and increasing global health challenge, with only limited progress made in developing effective therapies over the last decade. Interdisciplinary research is improving understanding of these diseases and this article reviews such approaches, with particular emphasis on tools and techniques drawn from physics, chemistry, artificial intelligence and psychology.

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

Italian, European, and international neuroinformatics efforts: An overview

Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.

Functional topologies of spatial cognition predict cognitive and motor progression in Parkinson’s

Background

Spatial cognition deteriorates in Parkinson’s disease (PD), but the neural substrates are not understood, despite the risk for future dementia. It is also unclear whether deteriorating spatial cognition relates to changes in other cognitive domains or contributes to motor dysfunction.

Objective

This study aimed to identify functional connectivity abnormalities in cognitively normal PD (PDCN) in regions that support spatial cognition to determine their relationship to interfacing cognitive functions and motor disability, and to determine if they predict cognitive and motor progression 2 years later in a PDCN subsample.

Methods

Sixty-three PDCN and 43 controls underwent functional MRI while judging whether pictures, rotated at various angles, depicted the left or right hand. The task activates systems that respond to increases in rotation angle, a proxy for visuospatial difficulty. Angle-modulated functional connectivity was analyzed for frontal cortex, posterior cortex, and basal ganglia regions.

Results

Two aberrant connectivity patterns were found in PDCN, which were condensed into principal components that characterized the strength and topology of angle-modulated connectivity. One topology related to a marked failure to amplify frontal, posterior, and basal ganglia connectivity with other brain areas as visuospatial demands increased, unlike the control group (control features). Another topology related to functional reorganization whereby regional connectivity was strengthened with brain areas not recruited by the control group (PDCN features). Functional topologies correlated with diverse cognitive domains at baseline, underscoring their influences on spatial cognition. In PDCN, expression of topologies that were control features predicted greater cognitive progression longitudinally, suggesting inefficient communications within circuitry normally recruited to handle spatial demands. Conversely, stronger expression of topologies that were PDCN features predicted less longitudinal cognitive decline, suggesting functional reorganization was compensatory. Parieto-occipital topologies (control features) had different prognostic implications for longitudinal changes in motor disability. Expression of one topology predicted less motor decline, whereas expression of another predicted increased postural instability and gait disturbance (PIGD) feature severity. Concurrently, greater longitudinal decline in spatial cognition predicted greater motor and PIGD feature progression, suggesting deterioration in shared substrates.

Conclusion

These novel discoveries elucidate functional mechanisms of visuospatial cognition in PDCN, which foreshadow future cognitive and motor disability.

The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review

Neurodegenerative diseases (NDs), including chronic disease such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis, and acute diseases like traumatic brain injury and ischemic stroke are characterized by progressive degeneration, brain tissue damage and loss of neurons, accompanied by behavioral and cognitive dysfunctions. So far, there are no complete cures for NDs; thus, early and timely diagnoses are essential and beneficial to patients' treatment. Magnetic resonance imaging (MRI) has become one of the advanced medical imaging techniques widely used in the clinical examination of NDs due to its non-invasive diagnostic value. In this review, research published in English in current decade from PubMed electronic database on the use of MRI to detect specific biomarkers of NDs was collected, summarized, and discussed, which provides valuable suggestions for the early diagnosis, prevention, and treatment of NDs in the clinic.

QEEG indices are associated with inflammatory and metabolic risk factors in Parkinson's disease dementia: An observational study

BACKGROUND

Quantitative electroencephalography (QEEG) is a reliable and non-invasive diagnostic tool to quantify cortical synaptic injury or loss in the clinical assessment of neurodegenerative diseases, and may be able to differentiate various types of dementia. We investigated if QEEG indices can differentiate Parkinson's Disease (PD) with nondementia (PD-ND) from PD with dementia (PDD), and to determine if QEEG indices correlate with inflammation and lipid metabolism markers in PD.

METHODS

This clinical study collected data between July 1, 2018 and July 1, 2021 in Zhujiang Hospital of Southern Medical University in China and data was analysed. A total of 125 individuals comprising of 31 PDD, 47 patients with PD-ND and 47 healthy controls were included. We calculated the absolute spectral power (ASP) of frequency bands and the slow-to-fast frequency ratios of specific brain regions. Plasma levels of hypersensitive C-reactive protein (Hs-CRP), superoxide dismutase (SOD), and high-density lipoprotein cholesterol (HDL-C) were measured and correlations with QEEG indices were examined.

FINDINGS

A significantly higher ASP of delta frequency especially in the frontal region was observed in patients with PDD compared to PD-ND (P=0.004) and controls (P=0.000). Decreased HDL-C (OR=0.186, P=0.030), and increased Hs-CRP (OR =2.856, P=0.015) were associated with PDD. Frontal-delta ASP was negatively correlated with plasma HDL-C (r=-0.353, P=0.000) and SOD (r=-0.322, P=0.001), and positively correlated with Hs-CRP (r=0.342, P=0.000).

INTERPRETATION

We highlight novel correlations between QEEG indices and inflammation and lipid metabolism markers in PD-ND and PDD. QEEG indices, HDL-C and Hs-CRP are potentially useful for the evaluation of PDD. Our current findings suggest that peripheral inflammation might contribute to the pathogenesis of cognitive impairment and EEG slowing in PDD. The mechanism underlying frontal-delta ASP and its correlation with neuro-inflammatory and metabolic markers in PDD should be further investigated.

FUNDING

The National Natural Science Foundation of China (NO: 81873777, 82071414); the Scientific Research Foundation of Guangzhou (NO: 202206010005); the Science and Technology Program of Guangdong of China (NO: 2020A0505100037); the High-level Hospital Construction Research Project of Maoming People's Hospital (NO: xz2020009); the Science and Technology Program of Maoming City (NO: 2021S0026). Dr EK Tan is supported by the National Medical Research Council, Singapore.

Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data

OBJECTIVE

Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features.

METHODS

We extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance.

RESULTS

In MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI.

SIGNIFICANCE

From cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.

Simulating the progression of brain structural alterations in Parkinson's disease

Considering brain structural alterations as neurodegenerative consequences of Parkinson's disease (PD), we sought to infer the progression of PD via the ordering of brain structural alterations from cross-sectional MRI observations. Having measured cortical thinning in gray matter (GM) regions and disintegrity in white matter (WM) regions as MRI markers of structural alterations for 130 patients with PD (69 ± 10 years, 72 men), stochastic simulation based on the probabilistic relationship between the brain regions was conducted to infer the ordering of structural alterations across all brain regions and the staging of structural alterations according to changes in clinical status. The ordering of structural alterations represented WM disintegrity tending to occur earlier than cortical thinning. The staging of structural alterations indicated structural alterations happening mostly before major disease complications such as postural instability and dementia. Later disease states predicted by the sequence of structural alterations were significantly related to more severe clinical symptoms. The relevance of the ordering of brain structural alterations to the severity of clinical symptoms suggests the clinical feasibility of predicting PD progression states.

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.

Deep phenotyping for precision medicine in Parkinson's disease

A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.

Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models

Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort.

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease

BACKGROUND

Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged.

METHODS

We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort.

RESULTS

We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory.

CONCLUSION

Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.

Therapeutic targeting of mitophagy in Parkinson's disease

Parkinson's disease is a neurodegenerative disorder characterised by cardinal motor symptoms and a diverse range of non-motor disorders in patients. Parkinson's disease is the fastest growing neurodegenerative condition and was described for the first time over 200 years ago, yet there are still no reliable diagnostic markers and there are only treatments that temporarily alleviate symptoms in patients. Early-onset Parkinson's disease is often linked to defects in specific genes, including PINK1 and Parkin, that encode proteins involved in mitophagy, the process of selective autophagic elimination of damaged mitochondria. Impaired mitophagy has been associated with sporadic Parkinson's and agents that damage mitochondria are known to induce Parkinson's-like motor symptoms in humans and animal models. Thus, modulating mitophagy pathways may be an avenue to treat a subset of early-onset Parkinson's disease that may additionally provide therapeutic opportunities in sporadic disease. The PINK1/Parkin mitophagy pathway, as well as alternative mitophagy pathways controlled by BNIP3L/Nix and FUNDC1, are emerging targets to enhance mitophagy to treat Parkinson's disease. In this review, we report the current state of the art of mitophagy-targeted therapeutics and discuss the approaches being used to overcome existing limitations to develop innovative new therapies for Parkinson's disease. Key approaches include the use of engineered mouse models that harbour pathogenic mutations, which will aid in the preclinical development of agents that can modulate mitophagy. Furthermore, the recent development of chimeric molecules (AUTACs) that can bypass mitophagy pathways to eliminate damaged mitochondria thorough selective autophagy offer new opportunities.

Cognition in prodromal Parkinson's disease

One characteristic of Parkinson's disease (PD) is a prodromal phase, lasting many years during which both pre-clinical motor and non-motor symptoms occur. Around one-fifth of patients with PD manifest mild cognitive impairment at time of clinical diagnosis. Thus, important challenges are to define the time of onset of cognitive dysfunction in the prodromal phase of PD, and to define its co-occurrence with other specific characteristics. Evidence for cognitive change in prodromal PD comes from various study designs, including both longitudinal and cross-sectional approaches with different target groups. These studies support the concept that changes in global cognitive function and alterations in executive functions occur, and that these changes may be present up to 6 years before clinical PD diagnosis. Notably, this evidence led to including global cognitive impairment as an independent prodromal marker in the recently updated research criteria of the Movement Disorder Society for prodromal PD. Knowledge in this field, however, is still at its beginning, and evidence is sparse about many aspects of this topic. Further longitudinal studies including standardized assessments of global and domain-specific cognitive functions are needed to gain further knowledge about the first appearance, the course, and the interaction of cognitive deficits with other non-motor symptoms in prodromal stage PD. Treatment approaches, including non-pharmacological interventions, in individuals with prodromal PD might help to prevent or delay cognitive dysfunction in early PD.

Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease

Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.

A data-driven model of brain volume changes in progressive supranuclear palsy

The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model's staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.

Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease

Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.

Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β_1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.