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Background: Cognitive impairment is a frequent feature of COPD. However, the proportion of patients with COPD with mild cognitive impairment (MCI) is still unknown, and no screening test has been validated to date for detecting MCI in this population. The goal of this study was to determine the frequency and subtypes of MCI in patients with COPD and to assess the validity of two cognitive screening tests, the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA), in detecting MCI in patients with COPD.Methods: Forty-five patients with moderate to severe COPD and 50 healthy control subjects underwent a comprehensive neuropsychologic assessment using standard MCI criteria. Receiver operating characteristic curves were obtained to assess the validity of the MMSE and the MoCA to detect MCI in patients with COPD.Results: MCI was found in 36% of patients with COPD compared with 12% of healthy subjects. Patients with COPD with MCI had mainly the nonamnestic MCI single domain subtype with predominant attention and executive dysfunctions. The optimal MoCA screening cutoff was 26 (≤ 25 indicates impairment, with 81% sensitivity, 72% specificity, and 76% correctly diagnosed). No MMSE cutoff had acceptable validity.Conclusions: In this preliminary study, a substantial proportion of patients with COPD were found to have MCI, a known risk factor for dementia. Longitudinal follow-up on these patients is needed to determine the risk of developing more severe cognitive and functional impairments. Moreover, the MoCA is superior to the MMSE in detecting MCI in patients with COPD. [ABSTRACT FROM AUTHOR]

Patients with idiopathic Parkinson's disease exhibit impairments in executive processes, including planning and set-shifting, even at the early stages of the disease. We have recently developed a new card-sorting task to study the specific role of the caudate nucleus in such executive processes and have shown, using functional magnetic resonance imaging (fMRI) in young healthy adults, that the caudate nucleus is specifically required when a set-shift must be planned. Here the same fMRI protocol was used to compare the patterns of activation in a group of early-stage Parkinson's disease patients (seven right-handed patients at Hoehn and Yahr stages 1 and 2; mean age 62 years, range 56-70) and matched control subjects. Increased cortical activation was observed in the patients compared with the control group in the condition not specifically requiring the caudate nucleus. On the other hand, decreased cortical activation was observed in the patient group in the condition significantly involving the caudate nucleus. This event-related fMRI study showed a pattern of cortical activation in Parkinson's disease characterized by either reduced or increased activation depending on whether the caudate nucleus was involved or not in the task. This activation pattern included not only the prefrontal regions but also posterior cortical areas in the parietal and prestriate cortex. These findings are not in agreement with the traditional model, which proposes that the nigrostriatal dopamine depletion results in decreased cortical activity. These observations provide further evidence in favour of the hypothesis that not only the nigrostriatal and but also the mesocortical dopaminergic substrate may play a significant role in the cognitive deficits observed in Parkinson's disease. [ABSTRACT FROM AUTHOR]

Background and Objectives: Intensive speech therapy may improve recovery from poststroke aphasia. Further evidence suggests that pairing repetitive transcranial magnetic stimulation (rTMS) with intensive speech therapy might augment outcomes. This sham-controlled randomized clinical trial evaluated the efficacy of 1-Hz rTMS over the right pars triangularis combined with multimodality aphasia therapy (M-MAT) in chronic poststroke aphasia., Methods: A parallel-group, double-blind, sham-controlled randomized clinical trial was conducted between April 2021 and May 2023 at an outpatient neurorehabilitation clinic. Individuals with chronic nonfluent aphasia after left middle cerebral artery stroke (>6 months from stroke) were enrolled and randomly assigned to receive either rTMS or sham stimulation combined with 35 hours of M-MAT over 10 days. The primary outcome was the Western Aphasia Battery aphasia quotient (WAB-AQ) measured at 3 weeks and 15 weeks. Intention-to-treat analysis examined treatment effects over time using linear mixed models., Results: A total of 44 participants were randomized. Forty-three (mean [SD] age, 63.4 [12.3] years; 14 women [32.6%]) completed the intervention. Overall, WAB-AQ scores improved from baseline to 15 weeks regardless of rTMS allocation (mean difference 5.33, 95% CI 2.9-7.8, p < 0.001). We observed a significant group-by-time interaction (β = 0.31, p = 0.024), suggesting that those who received rTMS combined with M-MAT improved more over time than those who received sham. At 15 weeks, the rTMS group demonstrated significantly less word-finding difficulties and more complete and longer sentences with fewer pauses compared with sham as indicated by higher WAB-AQ scores (mean difference 4.1 points, 95% CI 0.6-7.6, p = 0.022). The change from baseline at 15 weeks was greater in the rTMS group (7.6 points, 95% CI 4.1-11.1) compared with sham (3.0 points, 95% CI -0.3 to 5.2; mean difference 4.6 points, 95% CI 0.6-8.6, p = 0.024)., Discussion: Intensive administration of M-MAT alone improves speech production in patients with chronic poststroke aphasia. Combining 1-Hz rTMS with M-MAT is associated with supplemental improvements in aphasia severity at follow-up. rTMS is a promising candidate as an adjuvant therapy to M-MAT., Trial Registration Information: ClinicalTrials.gov Identifier: NCT04102228., Classification of Evidence: This study provides Class III evidence that in patients with aphasia 6 or more months after a stroke, 1-Hz rTMS combined with intensive M-MAT improves WAB-AQ more than sham stimulation plus M-MAT.

The presence of non-motor symptoms (NMS) such as olfactive deficit or neuropsychiatric symptoms has been associated with the diagnosis of Parkinson's Disease (PD). NMS are also associated with different brain structural features underlying distinctive processes in PD. NMS has been poorly studied in patients with a PD-like clinical profile, showing Scans Without Evidence of Dopaminergic Deficit (SWEDD). This study proposes to compare classification models differentiating PD, SWEDD and Healthy Controls (HC) based on NMS and neurostructural factors. 683 participants (382 PD diagnosed in the last 2 years, 48 with SWEDD, 170 HC) from the PPMI dataset were compared based on available assessments. Each participant underwent an olfactive, neuropsychiatric and sleep assessment, and a 3T MRI. Brain volumes were extracted and standardized from each MRI. Classifications were based on logistic regressions using 5-fold cross-validation models combining different NMS and MRI data and determining their involvement in differentiation between patient subgroups (PD vs. SWEDD) or between patients and HC. NMS were significant factors in PD vs. SWEDD, PD vs. HC and SWEDD vs. HC classifiers, when considered alone or in combination with MRI data. No classification models were significantly different from chance based-on MRI, nor more accurate combining NMS and MRI when compared with models based on NMS only. These results highlight the importance of NMS in differentiating between PD and SWEDD, PD and HC, SWEDD and HC. However, classical imaging data such as cortical and subcortical volumetry seems insufficient to improve these classifications. Other imaging features such as connectivity could also be studied., Competing Interests: Declarations. Ethical approval and Informed consent statement: PPMI is a large multicenter study and each site independently received ethics approval of the protocol. All subjects gave written informed consent in accordance with the Declaration of Helsinki. See Marek et al. (2011) and the online PPMI protocol for details of the PPMI study and image acquisition, respectively. Ethics committee approval and individual patient consents were received by the PPMI (https://www.ppmi-info.org/study-design/research-documents-and-sops). The present study was approved by the Comité d’éthique de la recherche vieillissement-neuroimagerie CER VN 19-20-06. Competing interests: The author declares that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported., (© 2025. Fondazione Società Italiana di Neurologia.)

Distributed learning enables collaborative machine learning model training without requiring cross-institutional data sharing, thereby addressing privacy concerns. However, local quality control variability can negatively impact model performance while systematic human visual inspection is time-consuming and may violate the goal of keeping data inaccessible outside acquisition centers. This work proposes a novel self-supervised method to identify and eliminate harmful data during distributed learning model training fully-automatically. Harmful data is defined as samples that, when included in training, increase misdiagnosis rates. The method was tested using neuroimaging data from 83 centers for Parkinson's disease classification with simulated inclusion of a few harmful data samples. The proposed method reliably identified harmful images, with centers providing only harmful datasets being easier to identify than single harmful images within otherwise good datasets. While only evaluated using neuroimaging data, the presented method is application-agnostic and presents a step towards automated quality control in distributed learning., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Objective. Transcranial ultrasound stimulation (TUS) presents challenges in ultrasound wave transmission through the skull, affecting study outcomes due to aberration and attenuation. While planning strategies incorporating 3D computed tomography (CT) scans help mitigate these issues, they expose participants to radiation, which can raise ethical concerns. A solution involves generating skull masks from participants' anatomical magnetic resonance imaging (MRI). This study aims to compare ultrasound field predictions between CT-derived and MRI-derived skull masks in TUS planning. Approach. Five participants with a range of skull density ratios (SDRs: 0.31, 0.42, 0.55, 0.67, and 0.79) were selected, each having both CT and T1/T2-weighted MRI scans. Ultrasound simulations were performed using BabelBrain software with a single-element transducer (diameter = 50 mm, F # = 1) at 250, 500, and 750 kHz frequencies. CT scans were used to generate maps of the skull's acoustic properties. The MRI scans were processed using the Charm segmentation tool from the SimNIBS tool suite using default and custom settings adapted for better skull segmentation. Ultrasound was adjusted to target 30 mm below the skull's surface at 54 electroencephalogram (EEG) locations. Main Results. The custom setting in Charm significantly improved the Dice coefficient between MRI- and CT-derived masks when compared to the default setting ( p < 0.001). The maximum pressure error significantly decreased in the custom setting compared to the default setting ( p < 0.001). Additionally, the focus location error median across different SDRs averaged 2.32, 1.45, and 1.57 mm in default and 2.08, 1.38, and 1.44 mm in custom conditions for 250 kHz, 500 kHz, and 750 kHz respectively. Significance. MRI-derived skull masks offer satisfactory accuracy at many EEG sites, and using custom settings can further enhance this accuracy. However, significant errors at specific locations highlight the importance of carefully considering stimulation location when choosing between CT- and MRI-derived skull modeling., (Creative Commons Attribution license.)

Background: Variants in the CTSB gene encoding the lysosomal hydrolase cathepsin B (catB) are associated with increased risk of Parkinson's disease (PD). However, neither the specific CTSB variants driving these associations nor the functional pathways that link catB to PD pathogenesis have been characterized. CatB activity contributes to lysosomal protein degradation and regulates signaling processes involved in autophagy and lysosome biogenesis. Previous in vitro studies have found that catB can cleave monomeric and fibrillar alpha-synuclein, a key protein involved in the pathogenesis of PD that accumulates in the brains of PD patients. However, truncated synuclein isoforms generated by catB cleavage have an increased propensity to aggregate. Thus, catB activity could potentially contribute to lysosomal degradation and clearance of pathogenic alpha synuclein from the cell, but also has the potential of enhancing synuclein pathology by generating aggregation-prone truncations. Therefore, the mechanisms linking catB to PD pathophysiology remain to be clarified., Methods: Here, we conducted genetic analyses of the association between common and rare CTSB variants and risk of PD. We then used genetic and pharmacological approaches to manipulate catB expression and function in cell lines, induced pluripotent stem cell-derived dopaminergic neurons and midbrain organoids and assessed lysosomal activity and the handling of aggregated synuclein fibrils., Results: We find that catB inhibition impairs autophagy, reduces glucocerebrosidase (encoded by GBA1) activity, and leads to an accumulation of lysosomal content. In cell lines, reduction of CTSB gene expression impairs the degradation of pre-formed alpha-synuclein fibrils, whereas CTSB gene activation enhances fibril clearance. In midbrain organoids and dopaminergic neurons treated with alpha-synuclein fibrils, catB inhibition potentiates the formation of inclusions which stain positively for phosphorylated alpha-synuclein., Conclusions: These results indicate that the reduction of catB function negatively impacts lysosomal pathways associated with PD pathogenesis, while conversely catB activation could promote the clearance of pathogenic alpha-synuclein., (© 2024. The Author(s).)

Previous studies have established that rare biallelic SYNJ1 mutations cause autosomal recessive parkinsonism and Parkinson's disease (PD). We analyzed 8165 PD cases, 818 early-onset-PD (EOPD, < 50 years) and 70,363 controls. Burden meta-analysis revealed an association between rare nonsynonymous variants and variants with high Combined Annotation-Dependent Depletion score (> 20) in the Sac1 SYNJ1 domain and PD (Pfdr = 0.040). A meta-analysis of EOPD patients demonstrated an association between all rare heterozygous SYNJ1 variants and PD (Pfdr = 0.029)., (© 2024. The Author(s).)

Purpose: Distributed learning is widely used to comply with data-sharing regulations and access diverse datasets for training machine learning (ML) models. The traveling model (TM) is a distributed learning approach that sequentially trains with data from one center at a time, which is especially advantageous when dealing with limited local datasets. However, a critical concern emerges when centers utilize different scanners for data acquisition, which could potentially lead models to exploit these differences as shortcuts. Although data harmonization can mitigate this issue, current methods typically rely on large or paired datasets, which can be impractical to obtain in distributed setups., Approach: We introduced HarmonyTM, a data harmonization method tailored for the TM. HarmonyTM effectively mitigates bias in the model's feature representation while retaining crucial disease-related information, all without requiring extensive datasets. Specifically, we employed adversarial training to "unlearn" bias from the features used in the model for classifying Parkinson's disease (PD). We evaluated HarmonyTM using multi-center three-dimensional (3D) neuroimaging datasets from 83 centers using 23 different scanners., Results: Our results show that HarmonyTM improved PD classification accuracy from 72% to 76% and reduced (unwanted) scanner classification accuracy from 53% to 30% in the TM setup., Conclusion: HarmonyTM is a method tailored for harmonizing 3D neuroimaging data within the TM approach, aiming to minimize shortcut learning in distributed setups. This prevents the disease classifier from leveraging scanner-specific details to classify patients with or without PD-a key aspect for deploying ML models for clinical applications., (© 2024 The Authors.)

Background: Patients with Parkinson's disease (PD) experience changes in behavior, personality, and cognition that can manifest even in the initial stages of the disease. Previous studies have suggested that mild behavioral impairment (MBI) should be considered an early marker of cognitive decline. However, the precise neurostructural underpinnings of MBI in early- to mid-stage PD remain poorly understood., Objective: The aim was to explore the changes in white matter microstructure linked to MBI and mild cognitive impairment (MCI) in early- to mid-stage PD using diffusion magnetic resonance imaging (dMRI)., Methods: A total of 91 PD patients and 36 healthy participants were recruited and underwent anatomical MRI and dMRI, a comprehensive neuropsychological battery, and the completion of the Mild Behavioral Impairment-Checklist. Metrics of white matter integrity included tissue fractional anisotropy (FAt) and radial diffusivity (RDt), free water (FW), and fixel-based apparent fiber density (AFD)., Results: The connection between the left amygdala and the putamen was disrupted when comparing PD patients with MBI (PD-MBI) to PD-non-MBI, as evidenced by increased RDt (η 2  = 0.09, P = 0.004) and both decreased AFD (η 2  = 0.05, P = 0.048) and FAt (η 2  = 0.12, P = 0.014). Compared to controls, PD patients with both MBI and MCI demonstrated increased FW for the connection between the left orbitofrontal gyrus (OrG) and the hippocampus (η 2  = 0.22, P = 0.008), augmented RDt between the right OrG and the amygdala (η 2  = 0.14, P = 0.008), and increased RDt (η 2  = 0.25, P = 0.028) with decreased AFD (η 2  = 0.10, P = 0.046) between the right OrG and the caudate nucleus., Conclusion: MBI is associated with abnormal microstructure of connections involving the orbitofrontal cortex, putamen, and amygdala. To our knowledge, this is the first assessment of the white matter microstructure in PD-MBI using dMRI. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Sharing multicenter imaging datasets can be advantageous to increase data diversity and size but may lead to spurious correlations between site-related biological and non-biological image features and target labels, which machine learning (ML) models may exploit as shortcuts. To date, studies analyzing how and if deep learning models may use such effects as a shortcut are scarce. Thus, the aim of this work was to investigate if site-related effects are encoded in the feature space of an established deep learning model designed for Parkinson's disease (PD) classification based on T1-weighted MRI datasets. Therefore, all layers of the PD classifier were frozen, except for the last layer of the network, which was replaced by a linear layer that was exclusively re-trained to predict three potential bias types (biological sex, scanner type, and originating site). Our findings based on a large database consisting of 1880 MRI scans collected across 41 centers show that the feature space of the established PD model (74% accuracy) can be used to classify sex (75% accuracy), scanner type (79% accuracy), and site location (71% accuracy) with high accuracies despite this information never being explicitly provided to the PD model during original training. Overall, the results of this study suggest that trained image-based classifiers may use unwanted shortcuts that are not meaningful for the actual clinical task at hand. This finding may explain why many image-based deep learning models do not perform well when applied to data from centers not contributing to the training set.