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.
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.
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.
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.
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.
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.
•iNPH patients showed reduced thickness and volume in several cortical regions.•iNPH and PSP patients had distinct patterns of cortical involvement.•The volume of the cingulate cortex was globally reduced in iNPH and normal in PSP.•iNPH patients had thickening in the superior parietal gyrus compared to controls.•A machine learning model using cortical data accurately distinguished iNPH from PSP.
In this work, we investigated motor network structure in patients affected by essential tremor (ET) with or without resting tremor, using probabilistic tractography of the cerebello–thalamo–basal ...ganglia–cortical loop. Twenty-five patients with ET, twenty-two patients with ET associated with resting tremor (rET), and twenty-five age- and sex-matched healthy controls were included in the study. All participants underwent whole-brain 3D T1-weighted and diffusion-weighted MRI, and DAT–SPECT. Probabilistic tractography was performed on diffusion data in network mode, reconstructing connections between the different structures of the cerebello–thalamo–basal ganglia–cortical loop. All patients with ET, regardless of the presence of resting tremor, had normal DAT–SPECT, but showed significantly decreased connectivity in the cerebello–thalamo–precentral cortex network bilaterally, compared to healthy controls. In addition, patients with rET showed reduced connectivity in a pathway connecting globus pallidus, caudate, and supplementary motor area, compared to ET and controls. This latter circuit was significantly damaged in the hemisphere contralateral to the side clinically most affected by resting tremor. These findings provide insights upon structural changes underlying the different clinical presentations of ET. Our study demonstrates that ET and rET share common alterations in the cerebello–thalamo–precentral cortex circuit, while rET patients are characterized by specific damage to additional structures of motor network, such as globus pallidus, caudate nucleus, and supplementary motor area. Our findings suggest that ET and rET are different subtypes of the same neurodegenerative disorder.
We aimed to identify the brain structures associated with postural instability (PI) in Progressive Supranuclear Palsy (PSP).
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.
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.
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.
•The inferior frontal gyrus (IFG) volume was associated with Postural Instability (PI).•Cerebellum lobules IV-V volumes were associated with ocular dysfunction in PSP.•The Rolandic area correlated with the “Bulbar” category score of PSP Rating Scale.•Whole-brain regression confirmed the correlation between IFG and PI score.
OBJECTIVE:To identify a biomarker for predicting the appearance of vertical supranuclear gaze palsy (VSGP) in patients affected by progressive supranuclear palsy–parkinsonism (PSP-P).
...METHODS:Twenty-four patients with PSP-P were enrolled in the current study. Patients were clinically followed up every 6 months until the appearance of VSGP or the end of the follow-up (4 years). Participants underwent MRI at baseline and at the end of follow-up. Magnetic resonance parkinsonism index (MRPI), an imaging measure useful for diagnosing PSP, was calculated.
RESULTS:Twenty-one patients with PSP-P completed follow-up, and 3 patients dropped out. Eleven of 21 patients with PSP-P developed VSGP after a mean follow-up period of 28.5 months (range 6–48 months), while the remaining 10 patients with PSP-P did not develop VSGP during the 4-year follow-up period. At baseline, patients with PSP-P who later developed VSGP had MRPI values significantly higher than those of patients not developing VSGP without overlapping values between the 2 groups. MRPI showed a higher accuracy (100%) in predicting VSGP than vertical ocular slowness (accuracy 33.3%) or postural instability with or without vertical ocular slowness (accuracy 71.4% and 42.9%, respectively).
CONCLUSIONS:Our study demonstrates that MRPI accurately predicted, on an individual basis, the appearance of VSGP in patients with PSP-P, thus confirming clinical diagnosis in vivo.
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