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.

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.

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.

Nocardial adrenal abscess: CT and MR findings

To our knowledge, four bacterial adrenal abscesses in adults have already been reported in the international literature, but an adrenal Nocardia abscess has never been described previously. In this report the CT and MR imaging appearances and the differential diagnosis of the entity are discussed. The mass could resemble a malignancy. The observation of a rapid growth and colliquation of the mass helped in distinguishing it from a malignancy. The associated pulmonary infection provided a further clue to the diagnosis. The diagnosis was confirmed by surgery.

Arrhythmogenic right ventricular dysplasia: MR features

Arrhythmogenic right ventricular dysplasia (ARVD) is a heart disease characterized by a total or partial fat replacement of the myocardium. A total of 30 patients were studied with a suspected diagnosis of ARVD. Clinical criteria used for evaluation of ARVD were: (a) ventricular origin arrhythmias with a left bundle branch block configuration, (b) T-wave inversion in the anterior precordial leads, (c) ventricular kinetic alterations observed using echocardiography and angiography and (d) cardiac failure when there are no pathologies attributable to other heart diseases. All patients had serial EKG and echocardiography tests. One third of patients underwent angiocardiography; 7 of 30 had Holter; 7 of 30 had exercise test just to evaluate the effectiveness of the anti-arrhythmic therapy. All patients underwent MRI examination. The following MRI criteria were used: (a) high-intensity areas indicating the fatty substitution of the myocardium, (b) ectasia of the right ventricular outflow tract, (c) dyskinetic bulges, (d) dilation of the right ventricle and (e) enlargement of the right atrium. The diagnosis of ARVD was classified as highly probable for patients manifesting at least three positive criteria, probable with two positive criteria, dubious with one and negative in the absence of all criteria. Highly probable diagnosis of ARVD was made in 8 patients, probable in 4, dubious in 7 and negative in 11. The MRI technique is very effective in the assessment of ARVD. The MRI criteria may be helpful in the diagnosis of this condition.