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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Clinical spectrum and frequency of Charcot-Marie-Tooth disease in Italy: data from the national CMT registry

BACKGROUND

We report data from the Italian CMT Registry.

METHODS

The Italian CMT Registry is a dual registry where the patient registers, chooses a reference centre, where the attending clinician collects a minimal dataset of information and administers the 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. We observed 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. 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.

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.

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.

Frequency, entity and determinants of fatigue in Charcot-Marie-Tooth disease

BACKGROUND AND PURPOSE

Fatigue, a disabling symptom in many neuromuscular disorders, has been reported also in Charcot-Marie-Tooth disease (CMT). The presence of fatigue and its correlations in CMT was investigated.

METHODS

The Modified Fatigue Impact Scale (MFIS) was administered to CMT patients from the Italian Registry and a control group. An MFIS score >38 indicated abnormal fatigue. The correlation with disease severity and clinical characteristics, the Hospital Anxiety and Depression Scale and Epworth Sleepiness Scale scores, and drug use was analysed.

RESULTS

Data were collected from 251 CMT patients (136 women) and 57 controls. MFIS total (mean ± standard deviation 32 ± 18.3, median 33), physical (18.9 ± 9.7, 20) and psychosocial (2.9 ± 2.4, 3) scores in CMT patients were significantly higher than controls. Abnormal fatigue occurred in 36% of the patients who, compared to patients with normal scores, had more severe disease (median CMT Examination Score 9 vs. 7), more frequent use of foot orthotics (22% vs. 11%), need of support for walking (21% vs. 8%), hand disability (70% vs. 52%) and positive sensory symptoms (56% vs. 36%). Patients with abnormal fatigue had significantly increased frequency of anxiety/depression/general distress (Hospital Anxiety and Depression Scale), somnolence (Epworth Sleepiness Scale), obesity (body mass index ≥ 30) and use of anxiolytic/antidepressant or anti-inflammatory/analgesic drugs.

CONCLUSIONS

Fatigue is a relevant symptom in CMT as 36% of our series had scores indicating abnormal fatigue. It correlated with disease severity but also with anxiety, depression, sleepiness and obesity, indicating different components in the generation of fatigue. CMT patients' management must include treatment of fatigue and of its different generators, including general distress, sleepiness and obesity.

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.

Challenging functional connectivity data: machine learning application on essential tremor recognition

BACKGROUND AND AIMS

This paper aimed to investigate the usefulness of applying machine learning on resting-state fMRI connectivity data to recognize the pattern of functional changes in essential tremor (ET), a disease characterized by slight brain abnormalities, often difficult to detect using univariate analysis.

METHODS

We trained a support vector machine with a radial kernel on the mean signals extracted by 14 brain networks obtained from resting-state fMRI scans of 18 ET and 19 healthy control (CTRL) subjects. Classification performance between pathological and control subjects was evaluated using a tenfold cross-validation. Recursive feature elimination was performed to rank the importance of the extracted features. Moreover, univariate analysis using Mann-Whitney U test was also performed.

RESULTS

The machine learning algorithm achieved an AUC of 0.75, with four networks (language, primary visual, cerebellum, and attention), which have an essential role in ET pathophysiology, being selected as the most important features for classification. By contrast, the univariate analysis was not able to find significant results among these two conditions.

CONCLUSION

The machine learning approach identifies the changes in functional connectivity of ET patients, representing a promising instrument to discriminate specific pathological conditions and find novel functional biomarkers in resting-state fMRI studies.

Reduced left ventricular function and sustained hypertension in women seven years after severe preeclampsia

Objective. To study left ventricular (LV) function and blood pressure (BP) at a long-term follow-up in women after severe pre-eclampsia. Design. In this single-centre, cross-sectional study, 96 patients were eligible for inclusion. LV function was examined by transthoracic echocardiography including tissue Doppler echocardiography and speckle tracking. BP was measured at rest using repeated non-invasive techniques. Results. We compared 36 patients with early-onset and 33 patients with late-onset pre-eclampsia with 28 healthy controls. Mean age (40 ± 3 years) and median time since delivery (7 ± 2 years) were similar across the study groups. The patients had 18% higher systolic BP (139 ± 15 mmHg) and 24% higher diastolic BP (87 ± 19 mmHg) than controls (p < .01). Hypertension was present in 23 patients (33%), where the estimated LV mass was 16% higher (p = .05) than in controls. The LV ejection fraction was 19% lower in the early-onset group (51 ± 4%; p = .01) and 14% lower in the late-onset group (54 ± 6; p = .04) compared with controls. LV global longitudinal strain was 18% lower in the patient group (-17.7 ± 2.1%) compared with controls (p = .01). Indicative of a more restrictive filling pattern, the diastolic indices showed a lower e' mean (p < .01) and subsequently higher E/e' ratio (p < .01). There were no significant differences in BP, systolic or diastolic function indices between the patient groups. Conclusion. We found sustained hypertension, higher LV mass and reduced LV systolic and diastolic function 7 y after severe pre-eclampsia. Our findings emphasize the importance of early risk stratification and clinical counselling, and follow-up for such cases.

A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder

Background and purpose: Growing evidence suggests that Machine Learning (ML) models can assist the diagnosis of neurological disorders. However, little is known about the potential application of ML in diagnosing idiopathic REM sleep behavior disorder (iRBD), a parasomnia characterized by a high risk of phenoconversion to synucleinopathies. This study aimed to develop a model using ML algorithms to identify iRBD patients and test its accuracy. Methods: Data were acquired from 32 participants (20 iRBD patients and 12 controls). All subjects underwent a video-polysomnography. In all subjects, we measured the components of heart rate variability (HRV) during 24 h recordings and calculated night-to-day ratios (cardiac autonomic indices). Discriminating performances of single HRV features were assessed. ML models based on Logistic Regression (LR), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) were trained on HRV data. The utility of HRV features and ML models for detecting iRBD was evaluated by area under the ROC curve (AUC), sensitivity, specificity and accuracy corresponding to optimal models. Results: Cardiac autonomic indices had low performances (accuracy 63-69%) in distinguishing iRBD from control subjects. By contrast, the RF model performed the best, with excellent accuracy (94%), sensitivity (95%) and specificity (92%), while XGBoost showed accuracy (91%), specificity (83%) and sensitivity (95%). The mean triangular index during wake (TIw) was the best discriminating feature between iRBD and HC, with 81% accuracy, reaching 84% accuracy when combined with VLF power during sleep using an LR model. Conclusions: Our findings demonstrated that ML algorithms can accurately identify iRBD patients. Our model could be used in clinical practice to facilitate the early detection of this form of RBD.

Dynamics of Viral Infection and Evolution of SARS-CoV-2 Variants in the Calabria Area of Southern Italy

In this study, we report on the results of SARS-CoV-2 surveillance performed in an area of Southern Italy for 12 months (from March 2021 to February 2022). To this study, we have sequenced RNA from 609 isolates. We have identified circulating VOCs by Sanger sequencing of the S gene and defined their genotypes by whole-genome NGS sequencing of 157 representative isolates. Our results indicated that B.1 and Alpha were the only circulating lineages in Calabria in March 2021; while Alpha remained the most common variant between April 2021 and May 2021 (90 and 73%, respectively), we observed a concomitant decrease in B.1 cases and appearance of Gamma cases (6 and 21%, respectively); C.36.3 and Delta appeared in June 2021 (6 and 3%, respectively); Delta became dominant in July 2021 while Alpha continued to reduce (46 and 48%, respectively). In August 2021, Delta became the only circulating variant until the end of December 2021. As of January 2022, Omicron emerged and took over Delta (72 and 28%, respectively). No patient carrying Beta, Iota, Mu, or Eta variants was identified in this survey. Among the genomes identified in this study, some were distributed all over Europe (B1_S477N, Alpha_L5F, Delta_T95, Delta_G181V, and Delta_A222V), some were distributed in the majority of Italian regions (B1_S477N, B1_Q675H, Delta_T95I and Delta_A222V), and some were present mainly in Calabria (B1_S477N_T29I, B1_S477N_T29I_E484Q, Alpha_A67S, Alpha_A701S, and Alpha_T724I). Prediction analysis of the effects of mutations on the immune response (i.e., binding to class I MHC and/or recognition of T cells) indicated that T29I in B.1 variant; A701S in Alpha variant; and T19R in Delta variant were predicted to impair binding to class I MHC whereas the mutations A67S identified in Alpha; E484K identified in Gamma; and E156G and ΔF157/R158 identified in Delta were predicted to impair recognition by T cells. In conclusion, we report on the results of SARS-CoV-2 surveillance in Regione Calabria in the period between March 2021 and February 2022, identified variants that were enriched mainly in Calabria, and predicted the effects of identified mutations on host immune response.

Video-oculographic biomarkers for evaluating vertical ocular dysfunction in progressive supranuclear palsy

INTRODUCTION

Progressive supranuclear palsy (PSP) patients show reduced amplitude and velocity of vertical saccades, but saccadic abnormalities have also been reported in Parkinson's disease (PD). We investigated amplitude and velocity of vertical saccades in PSP and PD patients, to establish the best video-oculographic (VOG) parameters for PSP diagnosis.

METHODS

Fifty-one PSP patients, 113 PD patients and 40 controls were enrolled. The diagnosis was performed on a clinico-radiological basis (MR Parkinsonism index [MRPI] and MRPI 2.0). We used VOG to assess the diagnostic performances of saccadic amplitude, peak velocity, and their product (AxV) in upward or downward direction and in vertical gaze (upward and downward averaged) in distinguishing PSP from PD patients. The vestibulo-ocular reflex, necessary to establish the supranuclear nature of ocular dysfunction, was evaluated clinically.

RESULTS

PSP patients showed significantly reduced amplitude and peak velocity of ocular saccades in upward and downward directions compared to PD and healthy subjects. In PD patients, upward gaze amplitude was lower than in controls. In vertical gaze, the peak velocity showed 99.1% specificity and 54.7% sensitivity for PSP classification. The AxV product showed high specificity (94.7%) and sensitivity (84.3%) and yielded higher accuracy (91.5%) than velocity and amplitude used alone in distinguishing PSP from PD.

CONCLUSION

Our study demonstrates that the peak velocity of vertical saccades was a very low sensitive parameter and cannot be used alone for PSP diagnosis. A new index combining amplitude and peak velocity in vertical gaze seems the most suitable video-oculographic biomarker for differentiating PSP from PD and controls.

Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features

Scans without evidence of dopaminergic deficit (SWEDD) refers to patients who mimics motor and non-motor symptoms of Parkinson's disease (PD) but showing integrity of dopaminergic system. For this reason, the differential diagnosis between SWEDD and PD patients is often not possible in absence of dopamine imaging. Machine Learning (ML) showed optimal performance in automatically distinguishing these two diseases from clinical and imaging data. However, the most common applied ML algorithms provide high accuracy at expense of findings intelligibility. In this work, a novel ML glass-box model, the Explainable Boosting Machine (EBM), based on Generalized Additive Models plus interactions (GA²Ms), was employed to obtain interpretability in classifying PD and SWEDD while still providing optimal performance. Dataset (168 healthy controls, HC; 396 PD; 58 SWEDD) was obtained from PPMI database and consisted of 178 among clinical and imaging features. Six binary EBM classifiers were trained on feature space with (SBR) and without (noSBR) dopaminergic striatal specific binding ratio: HC-PD_SBR, HC-SWEDD_SBR, PD-SWEDD_SBR and HC-PD_noSBR, HC-SWEDD_noSBR, PD-SWEDD_noSBR. Excellent AUC-ROC (1) was reached in classifying HC from PD and SWEDD, both with and without SBR, and by PD-SWEDD_SBR (0.986), while PD-SWEDD_noSBR showed lower AUC-ROC (0.882). Apart from optimal accuracies, EBM algorithm was able to provide global and local explanations, revealing that the presence of pairwise interactions between UPSIT Booklet #1 and Epworth Sleepiness Scale item 3 (ESS3), MDS-UPDRS-III pronation-supination movements right hand (NP3PRSPR) and MDS-UPDRS-III rigidity left upper limb (NP3RIGLU) could provide good performance in predicting PD and SWEDD also without imaging features.

Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy‐Parkinsonism From Parkinson's Disease

Background

Differentiating progressive supranuclear palsy‐parkinsonism (PSP‐P) from Parkinson's disease (PD) is clinically challenging.

Objective

This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP‐P from PD and to validate its diagnostic performance in two large independent cohorts.

Methods

We enrolled 676 participants: a training cohort (n = 346; 43 PSP‐P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP‐P, 171 PD, and 97 control subjects) from an international research group. We developed a new in‐house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP‐P from PD and control subjects in both cohorts using receiver operating characteristic curves.

Results

The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP‐P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89–0.98] and AUC = 0.97 [0.93–1.00], respectively) and in the international testing cohort (PSP‐P versus PD, AUC = 0.92 [0.87–0.97]; PSP‐P versus controls, AUC = 0.94 [0.90–0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP‐P and PD in the early stage of the diseases (AUC = 0.91 [0.84–0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values.

Conclusions

Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP‐P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP‐P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Diagnostic Accuracy of Magnetic Resonance Imaging Measures of Brain Atrophy Across the Spectrum of Progressive Supranuclear Palsy and Corticobasal Degeneration

Importance

The accurate diagnosis of progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) is hampered by imperfect clinical-pathological correlations.

Objective

To assess and compare the diagnostic value of the magnetic resonance parkinsonism index (MRPI) and other magnetic resonance imaging-based measures of cerebral atrophy to differentiate between PSP, CBD, and other neurodegenerative diseases.

Design, Setting, and Participants

This prospective diagnostic study included participants with 4-repeat tauopathies (4RT), PSP, CBD, other neurodegenerative diseases and available MRI who appeared in the University of California, San Francisco, Memory and Aging Center database. Data were collected from October 27, 1994, to September 29, 2019. Data were analyzed from March 1 to September 14, 2021.

Main Outcomes and Measures

The main outcome of this study was the neuropathological diagnosis of PSP or CBD. The clinical diagnosis at the time of the MRI acquisition was noted. The imaging measures included the MRPI, cortical thickness, subcortical volumes, including the midbrain, pons, and superior cerebellar peduncle volumes. Multinomial logistic regression models (MLRM) combining different cortical and subcortical regions were defined to discriminate between PSP, CBD, and other pathologies. The areas under the receiver operating characteristic curves (AUROC) and cutoffs were calculated to differentiate between PSP, CBD, and other diseases.

Results

Of the 326 included participants, 176 (54%) were male, and the mean (SD) age at MRI was 64.1 (8.0) years. The MRPI showed good diagnostic accuracy for the differentiation between PSP and all other pathologies (accuracy, 87%; AUROC, 0.90; 95% CI, 0.86-0.95) and between 4RT and other pathologies (accuracy, 80%; AUROC, 0.82; 95% CI, 0.76-0.87), but did not allow the discrimination of participants with CBD. Its diagnostic accuracy was lower in the subgroup of patients without the canonical PSP-Richardson syndrome (PSP-RS) or probable corticobasal syndrome (CBS) at MRI. MLRM combining cortical and subcortical measurements showed the highest accuracy for the differentiation between PSP and other pathologies (accuracy, 95%; AUROC, 0.98; 95% CI, 0.97-0.99), CBD and other pathologies (accuracy, 83%; AUROC, 0.86; 95% CI, 0.81-0.91), 4RT and other pathologies (accuracy, 89%; AUROC, 0.94; 95% CI, 0.92-0.97), and PSP and CBD (accuracy, 91%; AUROC, 0.95; 95% CI, 0.91-0.99), even in participants without PSP-RS or CBS at MRI.

Conclusions and Relevance

In this study, the combination of widely available cortical and subcortical measures of atrophy on MRI discriminated between PSP, CBD, and other pathologies and could be used to support the diagnosis of 4RT in clinical practice.

Whole-genome analysis of SARS-CoV-2 in a 2020 infection cluster in a nursing home of Southern Italy

Background

Nursing homes have represented important hotspots of viral spread during the initial wave of COVID-19 pandemics. The proximity of patients inside nursing homes allows investigate the dynamics of viral transmission, which may help understand SARS-Cov2 biology and spread.

Methods

SARS-CoV-2 viral genomes obtained from 46 patients infected in an outbreak inside a nursing home in Calabria region (South Italy) were analyzed by Next Generation Sequencing. We also investigated the evolution of viral genomes in 8 patients for which multiple swabs were available. Phylogenetic analysis and haplotype reconstruction were carried out with IQ-TREE software and RegressHaplo tool, respectively.

Results

All viral strains isolated from patients infected in the nursing home were classified as B.1 lineage, clade G. Overall, 14 major single nucleotide variations (SNVs) (frequency > 80%) and 12 minor SNVs (frequency comprised between 20% and 80%) were identified with reference to the Wuhan-H-1 sequence (NC_045512.2).

All patients presented the same 6 major SNVs: D614G in the S gene; P4715L, ntC3037T (F924F) and S5398P in Orf1ab gene; ntC26681T (F53F) in the M gene; and ntC241T in the non-coding UTR region. However, haplotype reconstruction identified a founder haplotype (Hap A) in 36 patients carrying only the 6 common SNVs indicated above, and 10 other haplotypes (Hap B—K) derived from Hap A in the remaining 10 patients. Notably, no significant association between a specific viral haplotype and clinical parameters was found.

Conclusion

The predominant viral strain responsible for the infection in a nursing home in Calabria was the B.1 lineage (clade G). Viral genomes were classified into 11 haplotypes (Hap A in 36 patients, Hap B—K in the remaining patients).

Multiple system atrophy and C9orf72 hexanucleotide repeat expansions in a cohort of Italian patients

Exanucleotide expansions in C9orf72 gene have been described as potential risk factor in some patients with Multiple system atrophy (MSA) and other forms of atypical parkinsonism. The goal of our study was to extend the knowledge on the involvement of C9orf72 in MSA studying a cohort of 100 patients from Italy. We identified 2 heterozygous patients in the pathological range (> 30 repeats) and 4 heterozygous patients for expansions in the premutation range (20 -30 repeats). Our findings strengthen the previously hypothesized role for this gene as a risk factor for MSA and raise the possibility of a more complex and still unknown involvement of this gene in the heterogeneity of MSA.

Roles of Non-Coding RNAs as Novel Diagnostic Biomarkers in Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder, affecting 5%of the elderly population. Currently, the diagnosis of PD is mainly based on clinical features and no definitive diagnostic biomarkers have been identified. The discovery of biomarkers at the earliest stages of PD is of extreme interest. This review focuses on the current findings in the field of circulating non-coding RNAs in PD. We briefly describe the more established circulating biomarkers in PD and provide a more thorough review of non-coding RNAs, in particular microRNAs, long non-coding RNAs and circular RNAs, differentially expressed in PD, highlighting their potential for being considered as biomarkers for diagnosis. Together, these studies hold promise for the use of peripheral biomarkers for the diagnosis of PD.

Exosomal miRNA as peripheral biomarkers in Parkinson's disease and progressive supranuclear palsy: A pilot study

INTRODUCTION

Parkinson's disease (PD), a progressive neurodegenerative disease, can be misdiagnosed with atypical conditions such as Progressive Supranuclear Paralysis (PSP) due to overlapping clinical features. MicroRNAs (miRNAs) are small non-coding RNAs with a key role in post-transcriptional gene regulation. The aim was to identify a set of differential exosomal miRNAs biomarkers, which may aid in diagnosis.

METHODS

We analyzed the serum level of 188 miRNAs in a discovery set, by using RTqPCR based TaqMan assay, in a small cohort of healthy controls, PD and PSP patients. Subsequently, the differentially expressed miRNAs, between PSP and PD patients, were further tested in a larger and independent cohort of 33 healthy controls, 40 PD and 20 PSP patients. The most accurate diagnostic exosomal miRNAs classifiers were identified in a logistic regression model.

RESULTS

A statistically significant set of three exosomal miRNAs: miR-21-3p, miR-22-3p and miR-223-5p, discriminated PD from HC (area under the curve of 0.75), and a set of three exosomal miRNAs, miR-425-5p, miR-21-3p, and miR-199a-5p, discriminated PSP from PD with good diagnostic accuracy (area under the curve of 0.86). Finally, the classifier that best discriminated PSP from PD consisted of six exosomal miRNAs (area under the curve = 0.91), with diagnostic sensitivity and specificity of 0.89 and 0.90, respectively.

CONCLUSIONS

Based on our analysis, these data showed that exosomal miRNAs could act as biomarkers to differentiate between PSP and PD.

Corticospinal tract abnormalities and ventricular dilatation: A transdiagnostic comparative tractography study

BACKGROUND

Microstructural alterations of corticospinal tract (CST) have been found in idiopathic normal pressure hydrocephalus (iNPH). No study, however, investigated the effect of ventricular dilatation on CST in Progressive Supranuclear Palsy (PSP).

OBJECTIVE

The aim of this study was to investigate CST diffusion profile in a large cohort of PSP patients with and without ventricular dilatation.

METHODS

Twenty-three iNPH patients, 87 PSP patients and 26 controls were enrolled. Evans index (EI) and ventricular volume (VV) were measured in all patients. CST tractography was performed to calculate FA, MD, AxD and RD in six different anatomical regions: medulla oblungata (MO), pons (P), cerebral peduncle (CP), posterior limb of internal capsule (PLIC), corona radiata (CR), subcortical white matter (SWM). ANCOVA was used for comparing CST diffusion profiles between the groups and association between CST microstructural metrics and measures of ventricular dilatation (EI and VV) was assessed.

RESULTS

Thirty-three PSP patients had ventricular dilatation (EI > 0.30, PSP-vd) while 54 PSP patients had normal ventricular system (EI ≤ 0.30, PSP-wvd). iNPH patients had the most marked FA and AxD increase in PLIC and CR of CST followed by PSP-vd, PSP-wvd and controls; RD was altered only in iNPH. A strong correlation was found between CST diffusion metrics and EI or VV.

CONCLUSIONS

Our findings confirm the microstructural changes of CST in iNPH patients and demonstrate for the first time similar alterations in PSP-vd patients, suggesting a crucial role of ventricular dilatation in the mechanical compression of CST.

Progressive supranuclear palsy with marked ventricular dilatation mimicking normal pressure hydrocephalus

BACKGROUND

Progressive supranuclear palsy (PSP) patients can show ventricular enlargement mimicking normal pressure hydrocephalus (NPH). The aim of this study was to distinguish PSP patients with marked ventricular dilatation (PSP-vd) from those with normal ventricular system and to evaluate the coexistence of NPH in PSP-vd patients.

METHODS

One hundred three probable PSP patients, 18 definite NPH patients, and 41 control subjects were enrolled in the study. Evans index (EI) > 0.32 associated with callosal angle (CA) < 100° was used to identify PSP-vd patients. Automated ventricular volumetry (AVV) and Magnetic Resonance Hydrocephalic Index (MRHI) were performed on T1-weighted MR images to evaluate the presence of NPH in PSP-vd patients.

RESULTS

Twelve (11.6%) out of 103 PSP patients had both abnormal EI and CA values (PSP-vd). In two of these 12 patients, AVV and MRHI values suggested PSP + NPH. In the remaining 10 PSP-vd patients, AVV and MRHI values were higher than PSP patients with normal ventricular system and controls, but lower than PSP + NPH and NPH patients, suggesting a non-hydrocephalic ventricular enlargement.

DISCUSSION

Our study provides evidence that the combination of EI and CA biomarkers allowed to identify PSP patients with marked ventricular dilatation mimicking NPH. Only a few of these patients had PSP + NPH. Recognition of these PSP patients with enlarged ventricles can positively impact the care of this disease, helping clinicians to identify patients with PSP + NPH who could benefit from shunt procedure and avoid surgery in those with enlarged ventricles without NPH.

Hyper-religiosity and visual hallucinations in a patient with frontotemporal dementia carrying a double variant in GRN gene

Introduction: Hyper-religiosity has been reported in patients affected by frontotemporal dementia (FTD) with asymmetrical, predominantly right-sided frontotemporal atrophy. Case report: We report a FTD patient carrying a double genetic variant (p.Cys139Arg and c.*78C > T) in the progranulin (GRN) gene who showed an unusual clinical phenotype characterized by hyper-religiosity behavior and visual hallucinations with exclusively religious content. Noteworthy, this patient exhibited a slow clinical and radiological rate of disease progression and a predominantly left-sided frontotemporal atrophy. Discussion and conclusion: The simultaneous presence of these GRN variants in our FTD patient with predominant atrophy in the left (dominant) hemisphere could determine the unusual phenotype with hyper-religiosity and visual hallucinations with exclusively religious content and influence the slow rate of disease progression.

Semi-automated assessment of the principal diffusion direction in the corpus callosum: differentiation of idiopathic normal pressure hydrocephalus from neurodegenerative diseases

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) shares clinical and radiological features with progressive supranuclear palsy (PSP) and Alzheimer's disease (AD). Corpus callosum (CC) involvement in these disorders is well established on structural MRI and diffusion tensor imaging (DTI), but alterations overlap and lack specificity to underlying tissue changes.

OBJECTIVE

We propose a semi-automated approach to assess CC integrity in iNPH based on the spatial distribution of DTI-derived principal diffusion direction orientation (V1).

METHODS

We processed DTI data from 121 subjects (Site1: iNPH = 23, PSP = 27, controls = 14; ADNI: AD = 35, controls = 22) to obtain V1, fractional anisotropy (FA) and mean diffusivity (MD) maps. To increase the estimation accuracy of DTI metrics, analyses were restricted to the midsagittal CC portion (± 6 slices from midsagittal plane). Group-wise comparison of normalized altered voxel count in midsagittal CC was performed using Kruskal-Wallis tests, followed by post hoc comparisons (Bonferroni-corrected p < 0.05). ROC analysis was used to evaluate the diagnostic power of DTI alterations compared to callosal volume.

RESULTS

We found specific changes of V1 distribution in CC splenium of iNPH compared to AD and PSP, while MD and FA showed patterns of alterations common to all disorders. ROC curves showed that, compared to splenial volume, V1 represented the most accurate marker of iNPH diagnosis versus AD and PSP.

CONCLUSIONS

Our results provide evidence that V1 is a powerful biomarker for distinguishing patients with iNPH from patients with AD or PSP. Indeed, our findings also provide more specific insight into the pathophysiological mechanisms that underlie tissue damage across iNPH and its mimics.

Blink reflex recovery cycle distinguishes patients with idiopathic normal pressure hydrocephalus from elderly subjects

BACKGROUND

The R2 component of blink reflex recovery cycle (R2BRrc) is a simple neurophysiological tool to detect the brainstem hyperexcitability commonly occurring in several neurological diseases such as Parkinson's disease and atypical parkinsonisms. In our study, we investigated for the first time the usefulness of R2BRrc to assess brainstem excitability in patients with idiopathic Normal Pressure Hydrocephalus (iNPH) in comparison with healthy subjects.

METHODS

Eighteen iNPH patients and 25 age-matched control subjects were enrolled. R2BRrc was bilaterally evaluated at interstimulus intervals (ISIs) of 100, 150, 200, 300, 400, 500 and 750 ms in all participants. We investigated the diagnostic performance of R2BRrc in differentiating iNPH patients from control subjects using ROC analysis. Midbrain area and Magnetic Resonance Hydrocephalic Index (MRHI), an MRI biomarker for the diagnosis of iNPH, were measured on T1-weighted MR images, and correlations between R2BRrc values and MRI measurements were investigated.

RESULTS

Fourteen (78%) of 18 iNPH patients showed an enhanced R2BRrc at ISIs 100-150-200 ms, while no control subjects had abnormal R2BRrc. The mean amplitude of bilateral R2BRrc at the shortest ISIs (100-150-200 ms) showed high accuracy in differentiating iNPH patients from controls (AUC = 0.89). R2BRrc values significantly correlated with midbrain area and MRHI values.

CONCLUSIONS

This study represents the first evidence of brainstem hyperexcitability in iNPH patients. Given its low cost and wide availability, R2BRrc could be a useful tool for selecting elderly subjects with mild gait and urinary dysfunction who should undergo an extensive diagnostic workup for the diagnosis of NPH.

Microstructural changes in normal-appearing white matter in male sleep apnea patients are reversible after treatment: A pilot study

Visually appreciable white matter (WM) changes have been described in obstructive sleep apnea (OSA). However, few data exist on the involvement of silent WM abnormalities. This prospective study investigated the microstructural integrity of normal-appearing white matter (NAWM) in male OSA patients before and after continuous positive airway pressure (CPAP) treatment, using a neuroimaging approach. Magnetic resonance imaging (MRI) was acquired from 32 participants (16 severe never-treated OSA and 16 controls). Diffusion tensor imaging (DTI) and Tract-Based Spatial Statistics (TBSS) were used to assess the microstructural NAWM changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). In order to evaluate the efficacy of the therapy, OSA patients underwent MRI evaluations at baseline and after 3 months of treatment (follow-up). CPAP treatment significantly increased the FA in NAWM of the brain stem, corpus callosum and bilateral internal capsule of OSA patients at follow-up compared to baseline (p < 0.05, TFCE-corrected). OSA patients also showed increases in AD in the corpus callosum, superior corona radiata, and internal capsule of the right hemisphere (p < 0.05, TFCE-corrected) after CPAP treatment. A significant negative correlation was found between the FA of the corona radiata, corpus callosum, internal capsule, limbic structures, and neuropsychological scores at follow-up evaluation. No significant differences were found in MD and RD of NAWM in our patients after treatment. Our results demonstrate that FA and AD of NAWM in major tracts such as the corpus callosum and the internal capsule increased significantly after CPAP treatment, as a potential beneficial effect of ventilatory therapy. The recovery of NAWM alterations might also be related to the improvement in the neurocognitive profile, suggesting that nonclearly visible WM alterations may contribute to the physiopathology of OSA-related cognitive impairment.

Wearable Devices for Assessment of Tremor

Tremor is an impairing symptom associated with several neurological diseases. Some of such diseases are neurodegenerative, and tremor characterization may be of help in differential diagnosis. To date, electromyography (EMG) is the gold standard for the analysis and diagnosis of tremors. In the last decade, however, several studies have been conducted for the validation of different techniques and new, non-invasive, portable, or even wearable devices have been recently proposed as complementary tools to EMG for a better characterization of tremors. Such devices have proven to be useful for monitoring the efficacy of therapies or even aiding in differential diagnosis. The aim of this review is to present systematically such new solutions, trying to highlight their potentialities and limitations, with a hint to future developments.