Measurement invariance across countries of the Test of Memory Strategies (TMS): A contribution to the cross-national validity study.

Previous literature showed a complex interpretation of recall tasks due to the complex relationship between Executive Functions (EF) and Long Term Memory (M). The Test of Memory Strategies (TMS) could be useful for assessing this issue, because it evaluates EF and M simultaneously. This study aims to explore the validity of the TMS structure, comparing the models proposed by Vaccaro et al. (2022) and evaluating the measurement invariance according to three countries (Italy, Spain, and Portugal) through Confirmatory Factor Analysis (CFA). Four hundred thirty-one healthy subjects (Age mean = 54.84, sd = 20.43; Education mean = 8.85, sd =4.05; M = 177, F = 259) were recruited in three countries (Italy, Spain, and Portugal). Measurement invariance across three country groups was evaluated through Structural Equation modeling. Also, convergent and divergent validity were examined through the correlation between TMS and classical neuropsychological tests. CFA outcomes suggested that the best model was the three-dimensional model, in which list 1 and list2 reflect EF, list 3 reflects a mixed factor of EF and M (EFM) and list4 and list5 reflect M. This result is in line with the theory that TMS decreases EF components progressively. TMS was metric invariant to the country, but scalar invariance was not tenable. Finally, the factor scores of TMS showed convergent validity with the classical neuropsychological tests. The overall results support cross-validation of TMS in the three countries considered.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.

Objective: To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. Methods: We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. Results: ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. Conclusion: This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Combined blood Neurofilament light chain and third ventricle width to differentiate Progressive Supranuclear Palsy from Parkinson's Disease: A machine learning study.

INTRODUCTION: Differentiating Progressive Supranuclear Palsy (PSP) from Parkinson's Disease (PD) may be clinically challenging. In this study, we explored the performance of machine learning models based on MR imaging and blood molecular biomarkers in distinguishing between these two neurodegenerative diseases. METHODS: Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC. RESULTS: PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04. CONCLUSION: Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.

Sex Differences in Conversion Risk from Mild Cognitive Impairment to Alzheimer's Disease: An Explainable Machine Learning Study with Random Survival Forests and SHAP.

Alzheimer's disease (AD) exhibits sex-linked variations, with women having a higher prevalence, and little is known about the sexual dimorphism in progressing from Mild Cognitive Impairment (MCI) to AD. The main aim of our study was to shed light on the sex-specific conversion-to-AD risk factors using Random Survival Forests (RSF), a Machine Learning survival approach, and Shapley Additive Explanations (SHAP) on dementia biomarkers in stable (sMCI) and progressive (pMCI) patients. With this purpose, we built two separate models for male (M-RSF) and female (F-RSF) cohorts to assess whether global explanations differ between the sexes. Similarly, SHAP local explanations were obtained to investigate changes across sexes in feature contributions to individual risk predictions. The M-RSF achieved higher performance on the test set (0.87) than the F-RSF (0.79), and global explanations of male and female models had limited similarity (<71.1%). Common influential variables across the sexes included brain glucose metabolism and CSF biomarkers. Conversely, the M-RSF had a notable contribution from hippocampus, which had a lower impact on the F-RSF, while verbal memory and executive function were key contributors only in F-RSF. Our findings confirmed that females had a higher risk of progressing to dementia; moreover, we highlighted distinct sex-driven patterns of variable importance, uncovering different feature contribution risks across sexes that decrease/increase the conversion-to-AD risk.

Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study.

Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.

Neuroimaging correlates of postural instability in Parkinson's disease.

BACKGROUND: Postural instability (PI) is a common disabling symptom in Parkinson's disease (PD), but little is known on its pathophysiological basis. OBJECTIVE: In this study, we aimed to identify the brain structures associated with PI in PD patients, using different MRI approaches. METHODS: We consecutively enrolled 142 PD patients and 45 control subjects. PI was assessed using the MDS-UPDRS-III pull-test item (PT). A whole-brain regression analysis identified brain areas where grey matter (GM) volume correlated with the PT score in PD patients. Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) were also used to compare unsteady (PT ≥ 1) and steady (PT = 0) PD patients. Associations between GM volume in regions of interest (ROI) and several clinical features were then investigated using LASSO regression analysis. RESULTS: PI was present in 44.4% of PD patients. The whole-brain approach identified the bilateral inferior frontal gyrus (IFG) and superior temporal gyrus (STG) as the only regions associated with the presence of postural instability. VBM analysis showed reduced GM volume in fronto-temporal areas (superior, middle, medial and inferior frontal gyrus, and STG) in unsteady compared with steady PD patients, and the GM volume of these regions was selectively associated with the PT score and not with any other motor or non-motor symptom. CONCLUSIONS: This study demonstrates a significant atrophy of fronto-temporal regions in unsteady PD patients, suggesting that these brain areas may play a role in the pathophysiological mechanisms underlying postural instability in PD. This result paves the way for further studies on postural instability in Parkinsonism.

Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer's disease.

Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature.For this reason, we aimed at providing a comprehensive study of RSF explainability with SHapley Additive exPlanations (SHAP) on biomarkers of stable and progressive patients (sMCI and pMCI) from Alzheimer's Disease Neuroimaging Initiative. We evaluated three global explanations-RSF feature importance, permutation importance and SHAP importance-and we quantitatively compared them with Rank-Biased Overlap (RBO). Moreover, we assessed whether multicollinearity among variables may perturb SHAP outcome. Lastly, we stratified pMCI test patients in high, medium and low risk grade, to investigate individual SHAP explanation of one pMCI patient per risk group.We confirmed that RSF had higher accuracy (0.890) than CPH (0.819), and its stability and robustness was demonstrated by high overlap (RBO > 90%) between feature rankings within first eight features. SHAP local explanations with and without correlated variables had no substantial difference, showing that multicollinearity did not alter the model. FDG, ABETA42 and HCI were the first important features in global explanations, with the highest contribution also in local explanation. FAQ, mPACCdigit, mPACCtrailsB and RAVLT immediate had the highest influence among all clinical and neuropsychological assessments in increasing progression risk, as particularly evident in pMCI patients' individual explanation. In conclusion, our findings suggest that RSF represents a useful tool to support clinicians in estimating conversion-to-AD risk and that SHAP explainer boosts its clinical utility with intelligible and interpretable individual outcomes that highlights key features associated with AD prognosis.

Development of a New Wearable Device for the Characterization of Hand Tremor.

Rest tremor (RT) is observed in subjects with Parkinson's disease (PD) and Essential Tremor (ET). Electromyography (EMG) studies have shown that PD subjects exhibit alternating contractions of antagonistic muscles involved in tremors, while the contraction pattern of antagonistic muscles is synchronous in ET subjects. Therefore, the RT pattern can be used as a potential biomarker for differentiating PD from ET subjects. In this study, we developed a new wearable device and method for differentiating alternating from a synchronous RT pattern using inertial data. The novelty of our approach relies on the fact that the evaluation of synchronous or alternating tremor patterns using inertial sensors has never been described so far, and current approaches to evaluate the tremor patterns are based on surface EMG, which may be difficult to carry out for non-specialized operators. This new device, named "RT-Ring", is based on a six-axis inertial measurement unit and a Bluetooth Low-Energy microprocessor, and can be worn on a finger of the tremulous hand. A mobile app guides the operator through the whole acquisition process of inertial data from the hand with RT, and the prediction of tremor patterns is performed on a remote server through machine learning (ML) models. We used two decision tree-based algorithms, XGBoost and Random Forest, which were trained on features extracted from inertial data and achieved a classification accuracy of 92% and 89%, respectively, in differentiating alternating from synchronous tremor segments in the validation set. Finally, the classification response (alternating or synchronous RT pattern) is shown to the operator on the mobile app within a few seconds. This study is the first to demonstrate that different electromyographic tremor patterns have their counterparts in terms of rhythmic movement features, thus making inertial data suitable for predicting the muscular contraction pattern of tremors.

A Reflection on Motor Overflow, Mirror Phenomena, Synkinesia and Entrainment.

In patients with movement disorders, voluntary movements can sometimes be accompanied by unintentional muscle contractions in other body regions. In this review, we discuss clinical and pathophysiological aspects of several motor phenomena including mirror movements, dystonic overflow, synkinesia, entrainment and mirror dystonia, focusing on their similarities and differences. These phenomena share some common clinical and pathophysiological features, which often leads to confusion in their definition. However, they differ in several aspects, such as the body part showing the undesired movement, the type of this movement (identical or not to the intentional movement), the underlying neurological condition, and the role of primary motor areas, descending pathways and inhibitory circuits involved, suggesting that these are distinct phenomena. We summarize the main features of these fascinating clinical signs aiming to improve the clinical recognition and standardize the terminology in research studies. We also suggest that the term "mirror dystonia" may be not appropriate to describe this peculiar phenomenon which may be closer to dystonic overflow rather than to the classical mirror movements.

Daytime sleepiness and sleep quality in Charcot-Marie-Tooth disease.

BACKGROUND: Sleep abnormalities have been reported in Charcot-Marie-Tooth disease (CMT), but data are scanty. We investigated their presence and correlation in a large CMT patients' series. METHODS: Epworth Sleepiness Scale (ESS) and Pittsburgh Sleep Quality Index (PSQI) were administered to CMT patients of the Italian registry and controls. ESS score > 10 indicated abnormal daytime somnolence, PSQI score > 5 bad sleep quality. We analyzed correlation with disease severity and characteristics, Hospital Anxiety and Depression Scale (HADS), Modified Fatigue Impact Scale (MFIS), Body Mass Index, drug use. RESULTS: ESS and PSQI questionnaires were filled by 257 and 253 CMT patients, respectively, and 58 controls. Median PSQI score was higher in CMT patients than controls (6 vs 4, p = 0.006), with no difference for ESS score. Abnormal somnolence and poor sleep quality occurred in 23% and 56% of patients; such patients had more frequently anxiety/depression, abnormal fatigue, and positive sensory symptoms than those with normal ESS/PSQI. Moreover, patients with PSQI score > 5 had more severe disease (median CMT Examination Score, CMTES, 8 vs 6, p = 0.006) and more frequent use of anxiolytic/antidepressant drugs (29% vs 7%, p < 0.001). CONCLUSIONS: Bad sleep quality and daytime sleepiness are frequent in CMT and correlated with anxiety, depression and fatigue, confirming that different components affect sleep. Sleep disorders, such as sleep apnea and restless leg syndrome, not specifically investigated here, are other factors known to impact on sleep quality and somnolence. CMT patients' management must include sleep behavior assessment and evaluation of its correlated factors, including general distress and fatigue.

Differentiating between common PSP phenotypes using structural MRI: a machine learning study.

BACKGROUND: Differentiating Progressive supranuclear palsy-Richardson's syndrome (PSP-RS) from PSP-Parkinsonism (PSP-P) may be extremely challenging. In this study, we aimed to distinguish these two PSP phenotypes using MRI structural data. METHODS: Sixty-two PSP-RS, 40 PSP-P patients and 33 control subjects were enrolled. All patients underwent brain 3 T-MRI; cortical thickness and cortical/subcortical volumes were extracted using Freesurfer on T1-weighted images. We calculated the automated MR Parkinsonism Index (MRPI) and its second version including also the third ventricle width (MRPI 2.0) and tested their classification performance. We also employed a Machine learning (ML) classification approach using two decision tree-based algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) with different combinations of structural MRI data in differentiating between PSP phenotypes. RESULTS: MRPI and MRPI 2.0 had AUC of 0.88 and 0.81, respectively, in differentiating PSP-RS from PSP-P. ML models demonstrated that the combination of MRPI and volumetric/thickness data was more powerful than each feature alone. The two ML algorithms showed comparable results, and the best ML model in differentiating between PSP phenotypes used XGBoost with a combination of MRPI, cortical thickness and subcortical volumes (AUC 0.93 ± 0.04). Similar performance (AUC 0.93 ± 0.06) was also obtained in a sub-cohort of 59 early PSP patients. CONCLUSION: The combined use of MRPI and volumetric/thickness data was more accurate than each MRI feature alone in differentiating between PSP-RS and PSP-P. Our study supports the use of structural MRI to improve the early differential diagnosis between common PSP phenotypes, which may be relevant for prognostic implications and patient inclusion in clinical trials.

Neuroimaging correlates of postural instability in Progressive Supranuclear Palsy.

OBJECTIVE: We aimed to identify the brain structures associated with postural instability (PI) in Progressive Supranuclear Palsy (PSP). METHODS: Forty-seven PSP patients and 45 control subjects were enrolled in this study. PI was assessed using the items 27 and 28 of the PSP rating scale (postural instability score, PIS). PSP patients were compared with controls using voxel-based morphometry (VBM). In PSP patients, LASSO regression model was used to investigate associations between VBM-based Region-Of-Interest grey matter (GM) volumes and different categories of the PSP rating scale. A whole-brain multi-regression analysis was also used to identify brain areas where GM volumes correlated with the PIS in PSP patients. RESULTS: VBM analysis showed widespread GM atrophy (fronto-temporal-parietal-occipital regions, limbic lobes, insula, cerebellum, and basal ganglia) in PSP patients compared with control subjects. In PSP patients, LASSO regression analysis showed associations of the right cerebellar lobules IV-V with ocular motor category score, and the left Rolandic area with bulbar category score, while the right inferior frontal gyrus (IFG) was negatively correlated with the PIS. The whole-brain multi-regression analysis identified the right IFG as the only area significantly associated with the PIS. CONCLUSIONS: In our study, two different approaches demonstrated that the IFG volume was associated with PIS in PSP patients, suggesting that this area may play a role in the pathophysiological mechanisms underlying PI. Our findings may have important implications for developing optimal Transcranial Magnetic Stimulation protocols targeting IFG in parkinsonism with postural disorders.

Assessing of the Italian version of the Memory Strategy Test (TMS) in people with Parkinson disease: a preliminary descriptive psychometric study.

BACKGROUND: Previous literature has shown that executive functions (EF) are related to performance in memory (M) tasks. The Test of Memory strategies (TMS) is a psychometric test that examines EF and M simultaneously and it was recently validated on an Italian healthy cohort. The first aim of the study was to apply TMS, for the first time, on a sample of patients with Parkinson's disease (PD), who are characterized by mild cognitive impairment. The second aim is to investigate whether TMS scores can discriminate PD patients from healthy controls. METHOD: Ninety-eight subjects were enrolled, including 68 patients with PD, and 30 Italian healthy controls (HC), who also underwent a memory evaluation through well-known tests. RESULTS: Confirmatory factor analysis (CFA) demonstrated that TMS of PD patients had a bi-dimensional structure as previously found in healthy cohort. In detail, The TMS-1 and TMS-2 lists require greater involvement of the EF factor, while TMS-3, TMS-4 and TMS-5 the M factor. Receiver operating characteristic (ROC) curves and precision-recall (PR) curves showed that the M subscale can distinguish between HC and PD, while EF had poor discrimination power. CONCLUSION: The hypothesized prediction model of TMS test seems to have adequate ability to discriminate PD from HC especially for the M function.

Cortical involvement in essential tremor with and without rest tremor: a machine learning study.

INTRODUCTION: There is some debate on the relationship between essential tremor with rest tremor (rET) and the classic ET syndrome, and only few MRI studies compared ET and rET patients. This study aimed to explore structural cortical differences between ET and rET, to improve the knowledge of these tremor syndromes. METHODS: Thirty-three ET patients, 30 rET patients and 45 control subjects (HC) were enrolled. Several MR morphometric variables (thickness, surface area, volume, roughness, mean curvature) of brain cortical regions were extracted using Freesurfer on T1-weighted images and compared among groups. The performance of a machine learning approach (XGBoost) using the extracted morphometric features was tested in discriminating between ET and rET patients. RESULTS: rET patients showed increased roughness and mean curvature in some fronto-temporal areas compared with HC and ET, and these metrics significantly correlated with cognitive scores. Cortical volume in the left pars opercularis was also lower in rET than in ET patients. No differences were found between ET and HC. XGBoost discriminated between rET and ET with mean AUC of 0.86 ± 0.11 in cross-validation analysis, using a model based on cortical volume. Cortical volume in the left pars opercularis was the most informative feature for classification between the two ET groups. CONCLUSION: Our study demonstrated higher cortical involvement in fronto-temporal areas in rET than in ET patients, which may be linked to the cognitive status. A machine learning approach based on MR volumetric data demonstrated that these two ET subtypes can be distinguished using structural cortical features.

Clinical spectrum and frequency of Charcot-Marie-Tooth disease in Italy: Data from the National CMT Registry.

BACKGROUND AND PURPOSE: Data are reported from the Italian CMT Registry. METHODS: The Italian CMT Registry is a dual registry where the patient registers and chooses a reference center where the attending clinician collects a minimal dataset of information and administers the Charcot-Marie-Tooth (CMT) Examination/Neuropathy Score. Entered data are encrypted. RESULTS: Overall, 1012 patients had registered (535 females) and 711 had received a genetic diagnosis. Demyelinating CMT (65.3%) was more common than axonal CMT2 (24.6%) and intermediate CMT (9.0%). The PMP22 duplication was the most frequent mutation (45.2%), followed by variants in GJB1 and MPZ (both ~10%) and MFN2 (3.3%) genes. A relatively high mutation rate in some "rare" genes (HSPB1 1.6%, NEFL 1.5%, SH3TC2 1.5%) and the presence of multiple mutation clusters across Italy was observed. CMT4A was the most disabling type, followed by CMT4C and CMT1E. Disease progression rate differed depending on the CMT subtype. Foot deformities and walking difficulties were the main features. Shoe inserts and orthotic aids were used by almost one-half of all patients. Scoliosis was present in 20% of patients, especially in CMT4C. Recessive forms had more frequently walking delay, walking support need and wheelchair use. Hip dysplasia occurred in early-onset CMT. CONCLUSIONS: The Italian CMT Registry has proven to be a powerful data source to collect information about epidemiology and genetic distribution, clinical features and disease progression of CMT in Italy and is a useful tool for recruiting patients in forthcoming clinical trials.

The Spread of SARS-CoV-2 Omicron Variant in CALABRIA: A Spatio-Temporal Report of Viral Genome Evolution.

We investigated the evolution of SARS-CoV-2 spread in Calabria, Southern Italy, in 2022. A total of 272 RNA isolates from nasopharyngeal swabs of individuals infected with SARS-CoV-2 were sequenced by whole genome sequencing (N = 172) and/or Sanger sequencing (N = 100). Analysis of diffusion of Omicron variants in Calabria revealed the prevalence of 10 different sub-lineages (recombinant BA.1/BA.2, BA.1, BA.1.1, BA.2, BA.2.9, BA.2.10, BA.2.12.1, BA.4, BA.5, BE.1). We observed that Omicron spread in Calabria presented a similar trend as in Italy, with some notable exceptions: BA.1 disappeared in April in Calabria but not in the rest of Italy; recombinant BA.1/BA.2 showed higher frequency in Calabria (13%) than in the rest of Italy (0.02%); BA.2.9, BA.4 and BA.5 emerged in Calabria later than in other Italian regions. In addition, Calabria Omicron presented 16 non-canonical mutations in the S protein and 151 non-canonical mutations in non-structural proteins. Most non-canonical mutations in the S protein occurred mainly in BA.5 whereas non-canonical mutations in non-structural or accessory proteins (ORF1ab, ORF3a, ORF8 and N) were identified in BA.2 and BA.5 sub-lineages. In conclusion, the data reported here underscore the importance of monitoring the entire SARS-CoV-2 genome.

ANXA1 mutation analysis in Italian patients with early onset PD.

Recently, a novel pathogenic variant in Annexin A1 protein (c.4G > A, p.Ala2Thr) has been identified in an Iranian consanguineous family with autosomal recessive parkinsonism. The deficiencies of ANXA1 could lead to extracellular SNCA accumulation, defects in intracellular signaling pathways and synaptic plasticity causing parkinsonism. The aim of this study was to identify rare ANXA1 variants in 95 early-onset PD patients from South Italy. Sequencing analysis of ANXA1 gene revealed only 2 synonymous variants in PD patients (rs1050305, rs149033255). Therefore, we conclude that the recently published ANXA1 mutation is not a common cause of EOPD in Southern Italy.