Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor.

Introduction: Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data. Methods: We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients. Results: Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase. Conclusion: Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

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.

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.

Evaluation of arteriovenous fistula for hemodialysis with a new generation digital stethoscope: a pilot study.

BACKGROUND AND AIMS: The management of complications of arteriovenous fistula (AVF) for hemodialysis, principally stenosis, remains a major challenge for clinicians with a substantial impact on health resources. Stenosis not infrequently preludes to thrombotic events with the loss of AVF functionality. A functioning AVF, when listened by a stethoscope, has a continuous systolic-diastolic low-frequency murmur, while with stenosis, the frequency of the murmur increases and the duration of diastolic component decreases, disappearing in severe stenosis. These evidences are strictly subjective and dependent from operator skill and experience. New generation digital stethoscopes are able to record sound and subsequently dedicated software allows to extract quantitative variables that characterize the sound in an absolutely objective and repeatable way. The aim of our study was to analyze with an appropriate software sounds from AVFs taken by a commercial digital stethoscope and to investigate the potentiality to develop an objective way to detect stenosis. METHODS: Between September 2022 and January 2023, 64 chronic hemodialysis (HD) patients were screened by two blinded experienced examiners for recognized criteria for stenosis by Doppler ultrasound (DUS) and, consequently, the sound coming from the AVFs using a 3 M™ Littmann® CORE Digital Stethoscope 8570 in standardized sites was recorded. The sound waves were transformed into quantitative variables (amplitude and frequency) using a sound analysis software. The practical usefulness of the core digital stethoscope for a quick identification of an AVF stenosis was further evaluated through a pragmatic trial. Eight young nephrologist trainees underwent a simple auscultatory training consisting of two sessions of sound auscultation focusing two times on a "normal" AVF sound by placing the digital stethoscope on a convenience site of a functional AVF. RESULTS: In 48 patients eligible, all sound components displayed, alone, a remarkable diagnostic capacity. More in detail, the AUC of the average power was 0.872 [95% CI 0.729-0.951], while that of the mean normalized frequency was 0.822 [95% 0.656-0.930]. From a total of 32 auscultations (eight different block sequences, each one comprising four auscultations), the young clinicians were able to identify the correct sound (stenosis/normal AVF) in 25 cases, corresponding to an overall accuracy of 78.12% (95% CI 60.03-90.72%). CONCLUSIONS: The analysis of sound waves by a digital stethoscope permitted us to distinguish between stenotic and no stenotic AVFs. The standardization of this technique and the introducing of data in a deep learning algorithm could allow an objective and fast method for a frequent monitoring of AVF.

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.

Predicting In-Hospital Acute Heart Failure Worsening in the Oldest Old: Insights from Point-of-Care Ultrasound.

The decompensation trajectory check is a basic step to assess the clinical course and to plan future therapy in hospitalized patients with acute decompensated heart failure (ADHF). Due to the atypical presentation and clinical complexity, trajectory checks can be challenging in older patients with acute HF. Point-of-care ultrasound (POCUS) has proved to be helpful in the clinical decision-making of patients with dyspnea; however, to date, no study has attempted to verify its role in predicting determinants of ADHF in-hospital worsening. In this single-center, cross-sectional study, we consecutively enrolled patients aged 75 or older hospitalized with ADHF in a tertiary care hospital. All of the patients underwent a complete clinical examination, blood tests, and POCUS, including Lung Ultrasound and Focused Cardiac Ultrasound. Out of 184 patients hospitalized with ADHF, 60 experienced ADHF in-hospital worsening. By multivariable logistic analysis, total Pleural Effusion Score (PEFs) [aO.R.: 1.15 (CI95% 1.02-1.33), p = 0.043] and IVC collapsibility [aO.R.: 0.90 (CI95% 0.83-0.95), p = 0.039] emerged as independent predictors of acute HF worsening after extensive adjustment for potential confounders. In conclusion, POCUS holds promise for enhancing risk assessment, tailoring diuretic treatment, and optimizing discharge timing for older patients with ADHF.

Advancing healthcare through thoracic ultrasound research in older patients.

This paper reports the proceedings of a meeting convened by the Research Group on Thoracic Ultrasound in Older People of the Italian Society of Gerontology and Geriatrics, to discuss the current state-of-the-art of clinical research in the field of geriatric thoracic ultrasound and identify unmet research needs and potential areas of development. In the last decade, point-of-care thoracic ultrasound has entered clinical practice for diagnosis and management of several respiratory illnesses, such as bacterial and viral pneumonia, pleural effusion, acute heart failure, and pneumothorax, especially in the emergency-urgency setting. Very few studies, however, have been specifically focused on older patients with frailty and multi-morbidity, who frequently exhibit complex clinical pictures needing multidimensional evaluation. At the present state of knowledge, there is still uncertainty on the best requirements of ultrasound equipment, methodology of examination, and reporting needed to optimize the advantages of thoracic ultrasound implementation in the care of geriatric patients. Other issues regard differential diagnosis between bacterial and aspiration pneumonia, objective grading of interstitial syndrome severity, quantification and monitoring of pleural effusions and solid pleural lesions, significance of ultrasonographic assessment of post-COVID-19 sequelae, and prognostic value of assessment of diaphragmatic thickness and motility. Finally, application of remote ultrasound diagnostics in the community and nursing home setting is still poorly investigated by the current literature. Overall, the presence of several open questions on geriatric applications of thoracic ultrasound represents a strong call to implement clinical research in this field.

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.

Predicting mortality and re-hospitalization for heart failure: a machine-learning and cluster analysis on frailty and comorbidity.

BACKGROUND: Machine-learning techniques have been recently utilized to predict the probability of unfavorable outcomes among elderly patients suffering from heart failure (HF); yet none has integrated an assessment for frailty and comorbidity. This research seeks to determine which machine-learning-based phenogroups that incorporate frailty and comorbidity are most strongly correlated with death or readmission at hospital for HF within six months following discharge from hospital. METHODS: In this single-center, prospective study of a tertiary care center, we included all patients aged 65 and older discharged for acute decompensated heart failure. Random forest analysis and a Cox multivariable regression were performed to determine the predictors of the composite endpoint. By k-means and hierarchical clustering, those predictors were utilized to phenomapping the cohort in four different clusters. RESULTS: A total of 571 patients were included in the study. Cluster analysis identified four different clusters according to frailty, burden of comorbidities and BNP. As compared with Cluster 4, we found an increased 6-month risk of poor outcomes patients in Cluster 1 (very frail and comorbid; HR 3.53 [95% CI 2.30-5.39]), Cluster 2 (pre-frail with low levels of BNP; HR 2.59 [95% CI 1.66-4.07], and in Cluster 3 (pre-frail and comorbid with high levels of BNP; HR 3.75 [95% CI 2.25-6.27])). CONCLUSIONS: In older patients discharged for ADHF, the cluster analysis identified four distinct phenotypes according to frailty degree, comorbidity, and BNP levels. Further studies are warranted to validate these phenogroups and to guide an appropriate selection of personalized, model of care.

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.

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.

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.

Heritability of human "directed" functional connectome.

INTRODUCTION: The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome. METHODS: Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled. RESULTS: The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity. CONCLUSION: We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.

FT-IR Analysis of Structural Changes in Ketoprofen Lysine Salt and KiOil Caused by a Pulsed Magnetic Field.

High-intensity, low-frequency magnetic fields (MFs) have been widely used in the treatment of diseases and in drug delivery, even though they could induce structural changes in pharmacological molecules. Morphological changes in ketoprofen and KiOil were investigated through Fourier-transform infrared spectroscopy (FT-IR). Unsupervised principal component analysis was carried out for data clustering. Clinical validation on 22 patients with lower back pain was managed using diamagnetic therapy plus topical ketoprofen or KiOil. The Numerical Rating Scale (NRS) and Short-Form Health Survey 36 (SF-36) were used to evaluate clinical and functional response. Ketoprofen showed clear clustering among samples exposed to MF (4000−650 cm−1), and in the narrow frequency band (1675−1475 cm−1), results evidenced structural changes which involved other excipients than ketoprofen. KiOil has evidenced structural modifications in the subcomponents of the formulation. Clinical treatment with ketoprofen showed an average NRS of 7.77 ± 2.25 before and an average NRS of 2.45 ± 2.38 after MF treatment. There was a statistically significant reduction in NRS (p = 0.003) and in SF-36 (p < 0.005). Patients treated with KiOil showed an average NRS of 7.59 ± 2.49 before treatment and an average NRS of 1.90 ± 2.26 after treatment (p < 0.005). SF-36 showed statistical significance for all items except limitations due to emotional problems. A high-intensity pulsed magnetic field is an adjunct to topical treatment in patients with localized pain, and the effect of MF does not evidence significant effects on the molecules.

Anti-Reflective Zeolite Coating for Implantable Bioelectronic Devices.

Since sunlight is one of the most easily available and clean energy supplies, solar cell development and the improvement of its conversion efficiency represent a highly interesting topic. Superficial light reflection is one of the limiting factors of the photovoltaic cells (PV) efficiency. To this end, interfacial layer with anti-reflective properties reduces this phenomenon, improving the energy potentially available for transduction. Nanoporous materials, because of the correlation between the refractive index and the porosity, allow low reflection, improving light transmission through the coating. In this work, anti-reflective coatings (ARCs) deposited on commercial PV cells, which were fabricated using two different Linde Type A (LTA) zeolites (type 3A and 4A), have been investigated. The proposed technique allows an easier deposition of a zeolite-based mixture, avoiding the use of chemicals and elevated temperature calcination processes. Results using radiation in the range 470-610 nm evidenced substantial enhancement of the fill factor, with maximum achieved values of over 40%. At 590 and 610 nm, which are the most interesting bands for implantable devices, FF is improved, with a maximum of 22% and 10%, respectively. ARCs differences are mostly related to the morphology of the zeolite powder used, which resulted in thicker and rougher coatings using zeolite 3A. The proposed approach allows a simple and reliable deposition technique, which can be of interest for implantable medical devices.

Cortical atrophy distinguishes idiopathic normal-pressure hydrocephalus from progressive supranuclear palsy: A machine learning approach.

INTRODUCTION: Progressive supranuclear palsy (PSP) and idiopathic normal pressure hydrocephalus (iNPH) share several clinical and radiological features, making the differential diagnosis challenging. In this study, we aimed to differentiate between these two diseases using a machine learning approach based on cortical thickness and volumetric data. METHODS: Twenty-three iNPH patients, 50 PSP patients and 55 control subjects were enrolled. All participants underwent a brain 3T-MRI, and cortical thickness and volumes were extracted using Freesurfer 6 on T1-weighted images and compared among groups. Finally, the performance of a machine learning approach with random forest using the extracted cortical features was investigated to differentiate between iNPH and PSP patients. RESULTS: iNPH patients showed cortical thinning and volume loss in the frontal lobe, temporal lobe and cingulate cortex, and thickening in the superior parietal gyrus in comparison with controls and PSP patients. PSP patients only showed mild thickness and volume reduction in the frontal lobe, compared to control subjects. Random Forest algorithm distinguished iNPH patients from controls with AUC of 0.96 and from PSP patients with AUC of 0.95, while a lower performance (AUC 0.76) was reached in distinguishing PSP from controls. CONCLUSION: This study demonstrated a more severe and widespread cortical involvement in iNPH than in PSP, possibly due to the marked lateral ventricular enlargement which characterizes iNPH. A machine learning model using thickness and volumetric data led to accurate differentiation between iNPH and PSP patients, which may help clinicians in the differential diagnosis and in the selection of patients for shunt procedures.

Structural connectivity alterations in the motor network of patients with scans without evidence of dopaminergic deficit (SWEDD).

BACKGROUND: Approximatively, 10% of patients initially diagnosed with Parkinson's disease (PD) show preserved presynaptic dopaminergic function in the nigrostriatal pathway on DAT-SPECT imaging. This syndrome is not compatible with PD diagnosis, and is known as scans without evidence of dopaminergic deficit (SWEDD). OBJECTIVE: To investigate structural connectivity of cerebello-subcortico-cortical networks, including the nigrostriatal pathway, in an international cohort of subjects with SWEDD compared to normal controls using probabilistic tractography. METHODS: Twenty-eight patients with SWEDD and 21 age- and sex-matched healthy controls (HC) were selected from the Parkinson's Progression Markers Initiative (PPMI) database. All participants underwent whole-brain 3D T1-weighted and diffusion-weighted MRI, as well as DAT-SPECT. Probabilistic tractography was performed in network-mode between regions of the cerebello-thalamo-basal ganglia-cortical circuits, to extract the connectivity strength between pairs of nodes of the circuit, as well as volumetric and diffusion measures of each reconstructed tract. Analysis of covariance with age and sex as covariates of non-interest was performed to assess group differences. Statistical significance was set at p < 0.05 after false-discovery-rate correction for multiple comparisons. RESULTS: Compared to HC, patients with SWEDD showed increased fractional anisotropy in bilateral thalamo-putamen-precentral, left nigro-putaminal and left thalamo-pallidal pathways. Furthermore, we found decreased mean streamline length in bilateral thalamo-nigro-cerebellar pathways and in the left nigro-caudate connection. CONCLUSIONS: Clinical heterogeneity of SWEDD syndrome may account for involvement of different brain circuits, such as the cerebello-thalamo-cortical and the nigrostriatal pathways, characteristic of different tremulous disorders.

Cerebellar voxel-based morphometry in essential tremor.

BACKGROUND: Imaging studies investigating cerebellar gray matter (GM) in essential tremor (ET) showed conflicting results. Moreover, no large study explored the cerebellum in ET patients with resting tremor (rET), a syndrome showing enhanced blink reflex recovery cycle (BRrc). OBJECTIVE: To investigate cerebellar GM in ET and rET patients using voxel-based morphometry (VBM) analysis. METHODS: Seventy ET patients with or without resting tremor and 39 healthy controls were enrolled. All subjects underwent brain 3 T-MRI and BRrc recording. We compared the cerebellar GM volumes between ET (n = 40) and rET (n = 30) patients and controls through a VBM analysis. Moreover, we investigated possible correlations between cerebellar GM volume and R2 component of BRrc. RESULTS: rET and ET patients had similar disease duration. All rET patients and none of ET patients had enhanced BRrc. No differences in the cerebellar volume were found when ET and rET patients were compared to each other or with controls. By considering together the two tremor syndromes in a large patient group, the VBM analysis showed bilateral clusters of reduced GM volumes in Crus II in comparison with controls. The linear regression analysis in rET patients revealed a cluster in the left Crus II where the decrease in GM volume correlated with the R2BRrc increase. CONCLUSION: Our study suggests that ET and rET are different tremor syndromes with similar mild cerebellar gray matter involvement. In rET patients, the left Crus II may play a role in modulating the brainstem excitability, encouraging further studies on the role of cerebellum in these patients.

Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices.

The demand of glucose monitoring devices and even of updated guidelines for the management of diabetic patients is dramatically increasing due to the progressive rise in the prevalence of diabetes mellitus and the need to prevent its complications. Even though the introduction of the first glucose sensor occurred decades ago, important advances both from the technological and clinical point of view have contributed to a substantial improvement in quality healthcare. This review aims to bring together purely technological and clinical aspects of interest in the field of glucose devices by proposing a roadmap in glucose monitoring and management of patients with diabetes. Also, it prospects other biological fluids to be examined as further options in diabetes care, and suggests, throughout the technology innovation process, future directions to improve the follow-up, treatment, and clinical outcomes of patients.