Rick C. Helmich

Background: The brain‐first versus body‐first framework in Parkinson's disease (PD) proposes distinct origins of α‐synuclein pathology, resulting in discrete progression patterns. Although this pathophysiological distinction implies different clinical trajectories, feasible criteria to classify subjects in large cohorts are currently lacking. Objectives: The goal was to develop a pragmatic approach for classifying brain‐first and body‐first PD clinical subtypes in a large early‐PD cohort. Methods: We, therefore, explored a pragmatic approach to separating these subtypes based on motor asymmetry, rapid eye movement sleep behavior disorder, and orthostatic hypotension, applied in a large cohort of persons with early PD (disease duration <5 years). Results: Individuals classified as probable brain‐first were more often tremor‐dominant and showed a trend toward less constipation, after adjusting for disease duration, age and sex. Conclusions: This framework enables subtyping in many existing cohorts, potentially advancing our understanding of PD pathophysiology and facilitating targeted therapies. Validation against gold‐standard measures is recommended to refine these criteria further.

Background: There is ongoing debate on the nature and the phenomenology of the golfers’ yips. Objectives: To clarify the phenomenology of the golfers’ yips, based on assessment of videos by expert neurologists. Methods: A panel of 13 movement disorder neurologists reviewed videos of 15 golfers (10 with and 5 without the yips). Videos showed wrist movements during putting, in three conditions: both hands, right and left hand only. The presence or absence of a movement disorder and its phenomenology was collected via a short questionnaire. Results: The video panel classified the yips as a movement disorder with high overall diagnostic accuracy (0.84[95% CI: 0.71–0.92, p < 0.001]) and significant inter‐rater reliability (Gwet‐AC1 = 0.56, 95% CI: 0.46–0.67, p < 0.001). The phenomenology was predominantly classified as dystonia with significant inter‐rater reliability (Gwet‐AC1 = 0.57, 95% CI: 0.44–0.69, p < 0.001). Conclusions: A panel of expert neurologists considered the golfers’ yips a neurological movement disorder, most consistent with dystonia.

Background: Reliable clinical phenotyping is crucial for diagnosis, treatment, and research in hyperkinetic movement disorders, yet its interrater reliability remains uncertain. Objectives: To quantify interrater agreement in clinical phenotyping of hyperkinetic movement disorders among experts in a large, phenotypically diverse cohort. Methods: Within the NEMO project, 161 patients with hyperkinetic movement disorders underwent standardized video assessments. Each patient was independently classified by three international movement disorders experts in a structured, stepwise procedure. Experts first assessed videos alone, then received clinical history and examination, and finally diagnostic test results. At each step, they assigned primary and secondary phenotypes and rated their diagnostic confidence. Interrater agreement was assessed using Fleiss’ and Cohen's kappa. Results: Across all phenotypes, interrater agreement on the primary phenotype increased with added information ( κ = 0.457 to κ = 0.607). Agreement with the NEMO reference classification similarly improved ( κ = 0.478 to κ = 0.656). Among patients with a single dominant phenotype, agreement reached κ = 0.78. In 93% of cases, at least two out of three experts reached consensus. Agreement was substantially lower in patients with mixed or complex presentations, such as myoclonus‐dystonia and functional movement disorders. Diagnostic certainty rose throughout, though secondary phenotype agreement remained poor. Experts rarely revised initial impressions, suggesting robust pattern recognition but also potential confirmation bias. Conclusions: Movement disorders specialists can reliably identify hyperkinetic phenotypes, even without in‐person assessment. While complex, mixed and functional presentations remain diagnostically challenging, our findings reinforce the value of expert phenotyping in clinical care, training, and research, and provide a benchmark for future studies and computational approaches.

While nigrostriatal dopaminergic cell loss is the pathological hallmark of Parkinson’s disease, it is well-known that dopamine deficiency cannot fully explain the clinical severity or disease progression. Emerging data suggest that resilience, i.e. the brain’s capacity to maintain function despite pathology, plays a key role in shaping clinical variability of both motor and non-motor symptoms. However, different contributing terms have been proposed, including brain compensation and reserve, and it remains unclear how these terms can be delineated both conceptually and mechanistically. Therefore, here we provide a synthesis of recent neuroimaging evidence on resilience mechanisms in Parkinson’s disease, with the following aims: (i) clarify the terminology; (ii) define the mechanisms; and (iii) relate resilience mechanisms to treatments, including dopamine replacement therapy and deep brain stimulation. We distinguish between compensation and reserve. We define compensation as adaptive neural activity that stabilizes task performance in the presence of pathology, particularly under increased demands, and is inversely related to clinical deficits. By contrast, reserve (cognitive or motor) refers to the capacity of neural resources that is built up across the lifetime, shaped by factors of an individual’s exposome. Compensation has been localized to the parieto-premotor network alongside the relatively preserved anterior striatum. Higher motor reserve has been linked to somatomotor network tolerance, preserved structural integrity and higher striatal functional network connectivity. Cognitive reserve relates to network robustness and structural integrity within the fronto-parietal network. Whether resilience mechanisms drive interindividual variability in long-term treatment response remains unresolved. Lower motor reserve predicts accelerated levodopa dose escalation and increased risk of dyskinesias, while cognitive reserve modulates both cognitive and motor outcomes after deep brain stimulation, highlighting cross-domain interactions. Brain compensation and reserve are two key components of resilience that together shape clinical heterogeneity in Parkinson’s disease across motor and cognitive domains. Although they operate at different timescales and through distinct mechanisms, they remain intrinsically related. Our framework suggests that biomarkers of resilience could complement biological definitions of Parkinson’s disease and shift therapeutic strategies from merely slowing degeneration towards strengthening adaptive capacity. Key priorities for future work are to define the longitudinal relationship between resilience failure and subcortical neurodegeneration using concurrent measures of nigrostriatal decline, multimodal datasets or controlled interventions, and to determine how symptom domain-specific resilience mechanisms shape treatment outcomes to enable targeted modulation and better capture disease heterogeneity.

It is currently unclear whether anxiety, cognitive load, or their combination modulates freezing of gait differently and whether people with Parkinson's disease and freezing of gait (freezers) suffer from stronger threat responsiveness than nonfreezing groups. Therefore, we investigated 28 freezers, 19 nonfreezers, and 19 healthy age‐matched controls, who completed a freezing‐provoking protocol under different conditions. State anxiety was generated via a threat of shock or a threat of freezing paradigm. Cognitive load was imposed via a dual task. The primary outcome was the freezing ratio, derived from wearable sensors. Stress was monitored by self‐reported and physiological markers. Freezers had worse cognition and more severe motor symptoms than nonfreezers yet a similar trait anxiety and threat response compared with both nonfreezing groups. Anxiety and cognitive load tended to increase the freezing ratio across groups. When combined, anxiety and cognitive load showed no surplus effects. Compared with the threat of shock, the threat of freezing had less impact on the freezing ratio, and although it induced less perceived stress, it generated higher physiological stress responses. In conclusion, freezers showed overall comparable levels of trait anxiety and a similar threat response as nonfreezers and healthy controls. Anxiety and cognitive load each tended to increase the freezing ratio, but their combined effect seemed to plateau. The threat of freezing had a smaller effect than the threat of shock as the former may not have diverted the attention away from walking to the same degree as the latter, although this explanation remains speculative.

Resting tremor in Parkinson’s disease is characterized by considerable variability: tremor affects some patients but not others, and its amplitude typically fluctuates. Previous research focused primarily on identifying mechanisms that produce tremor, showing an important role for the basal ganglia and cerebello-thalamo-cortical circuit. What has been relatively ignored are mechanisms involved in suppressing tremor. For instance, patients can reduce their tremor by making voluntary movements or by exerting conscious effort. Here, we aimed to distinguish between brain mechanisms that contribute positively or negatively to spontaneous fluctuations in Parkinson’s tremor. We leveraged a large sample of 119 tremulous Parkinson patients with concurrent accelerometry-fMRI. Frame-by-frame fluctuations in accelerometry-derived tremor power, measured per patient, were used to localize tremor-related brain activations and deactivations at a group level. Amplitude-related coupling between tremor-positive and tremor-negative brain networks was examined further using physio-physiological interaction analyses. Lastly, whole-brain multiple regression analyses were used to test the association between tremor-related activity and clinically assessed tremor severity. Tremor-related activations were found in a lateralized network consisting of contralateral (to tremor) sensorimotor cortex, ventrolateral thalamus, posterior putamen, and cerebellum (ipsilateral lobule V, vermis, and bilateral Crus I/II); activity in this network correlated positively with tremor severity. Tremor-related deactivations encompassed a bilateral “anti-tremor” network, including dorsal premotor cortex, pre-supplementary motor area, somatosensory cortex, posterior parietal cortex, and insula; activity in this network correlated negatively with tremor severity. During low versus high tremor episodes, functional connectivity between the anti-tremor and tremor-positive networks increased. This suggests that the anti-tremor network makes a relevant contribution to modulating tremor amplitude by connecting to the cerebello-thalamo-cortical circuit in a tremor-dependent manner. We conclude that spontaneous fluctuations in Parkinson’s tremor amplitude are associated with interactions between a tremor-producing cerebello-thalamo-cortical circuit and a novel antagonistic anti-tremor network. Future intervention studies may investigate whether strengthening of the anti-tremor network leads to a reduction in tremor, as a potential basis for new therapies.

Objective: Sensitive outcome measures are critical for evaluating the efficacy of novel treatments for Parkinson's disease (PD). In this study, we assess the sensitivity to change of sensor‐derived daily‐life tremor measures over 2 years in unmedicated and medicated persons with early PD. Methods: We used 2‐year continuous wrist sensor data (median wear time: 22 hours/day) from the Personalized Parkinson Project (n = 462 medicated; n = 78 unmedicated at baseline), in combination with annual clinical evaluations of tremor severity. From the gyroscope data, we derived previously validated weekly measures for tremor time and power, which were smoothed over time using piecewise linear trend estimation. One‐ and 2‐year standardized response means (SRMs) were computed to compare the sensitivity to change between the sensor‐derived tremor measures and clinical tremor scores. Results: In unmedicated participants with tremor, sensor‐derived tremor measures demonstrated a high sensitivity to progression (2‐year SRMs ranged from 0.67 to 1.09), which was significantly larger than clinical tremor scores (2‐year SRMs ranged from 0.21 to 0.41). In medicated participants, sensor‐derived tremor time decreased (2‐year SRM of −0.18), which was associated with both an increase in dopaminergic medication dose and higher disease duration. In contrast, the sensor‐derived tremor power measures and clinical rest tremor scores (measured in the OFF state) increased slightly (2‐year SRMs ranging from 0.11 to 0.27). Interpretation: Before initiation of symptomatic treatment, sensor‐derived daily‐life tremor measures are substantially more sensitive to progression than clinical tremor scores, making them a promising tool to evaluate the efficacy of disease‐modifying treatments in early PD. ANN NEUROL 2026

Objective: Tremor is a common motor symptom of Parkinson’s disease (PD) that reduces quality of life. Cortico-cortical paired associative stimulation (ccPAS) is a transcranial magnetic stimulation protocol that induces connective associative plasticity between cortical regions. This study investigated the effects of ccPAS over supplementary motor area (SMA) and primary motor cortex (M1) on SMA-M1 facilitation and tremor severity in individuals with tremor-dominant PD. Methods: Fourteen individuals with tremor-dominant PD (mean age 66.5 ± 6.3 years; 5 females) received real and sham ccPAS in separate sessions. SMA-M1 activity and tremor severity were measured before and after ccPAS. Participants were tested OFF dopaminergic medication. Results: SMA-M1 facilitation increased significantly after real but not sham ccPAS. There was no significant change in tremor severity after either real or sham ccPAS. Tremor severity was not associated with the change in SMA-M1 activity following ccPAS or with baseline SMA-M1 activity. Conclusions: ccPAS can increase SMA-M1 facilitation in individuals with tremor-dominant PD. A single session of SMA-M1 ccPAS was insufficient in modulating tremor severity. Significance: This study provides preliminary evidence of significant increases in SMA-M1 facilitation from ccPAS in tremor-dominant PD OFF dopamine medication. Future research should explore ccPAS effects ON dopamine medication, and the effects of multi-session ccPAS.

Background: Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder shaped by, amongst others, high-impact variants and common polygenic factors. The Personalized Parkinson Project (PPP) offers deep phenotyping and longitudinal follow-up of Dutch people with PD. Here, we characterize the genetic landscape and its interaction with lifestyle factors within PPP. Methods: We utilized three complementary approaches in N=507 persons with PD: 1) short-read PD gene panel sequencing of eight PD genes, 2) genome-wide genotyping array, and 3) targeted long-read sequencing of the GBA1 gene. Additionally, we calculated the mitochondrial-function polygenic score (MGS). Associations between genetic factors, smoking status, and age at onset (AAO) were assessed using non-parametric tests, correlation analyses, and multiple regression models. Results: Genetic screening of the participants revealed N=79 GBA1 (∼15%), N=3 LRRK2 , N=1 CHCHD2 , N=1 SNCA variant carrier, and N=9 heterozygous PRKN/PINK1 variants. We also observed an interaction between MGS and smoking: MGS was associated with earlier AAO in non-smokers in persons with iPD (N=414, β=-1.87, p=0.038). Conclusion: Our findings corroborate previously reported frequencies of variants in PD-associated genes in European populations, and suggest a potential association between smoking and a mitochondrial dysfunction signature in PD. Thus, even in persons without rare variants (iPD subgroup), complex genetic contributions remained relevant. Our study supports future downstream stratification and personalized medicine approaches with high-impact variants and polygenic risk scores.