A total of 2751 patients were identified between January 2010 and August 2020 from Kuopio University Hospital patient archive with a wide search of congruous ICD-10 codes, including codes for Parkinson’s disease and other neurogenerative diseases (G31, F02-F03, G23, G12.2, G25, G20, F04). The patient records were screened by an experienced physician specialized in neurodegenerative diseases to further specify the relevant diagnosis (bvFTD, PSP or CBD). A total of 222 patients with PSP (n = 50), CBD (n = 23), primary progressive aphasia (PPA) (n = 12), or bvFTD and FTD-ALS (n = 137) were identified. The presence of EP symptoms was defined if at least two of the following symptoms were present: rest tremor, bradykinesia, rigidity, prominent hypomimia, postural instability, and loss of automated movements. Based on these criteria, the 222 patients were finally grouped either as EP + (patients with EP symptoms) or EP- (patients without EP symptoms). Acceptable imaging data (MRI and/or FDG-PET) was available from 139 patients. The rest of the patients were excluded due to inadequate sequences, issues in image analysis, or obvious gross pathologies causing symptoms (e.g., brain tumors or chronic infarctions). The MRI and FDG-PET examinations were performed in the early diagnostic phase and the imaging data were collected retrospectively from the picture archiving and communication system. The study was approved by the Ethics Committee of the Hospital District of Northern Savo.
MRI acquisition and analysis
Due to the retrospective nature of our study, the scans were obtained with different scanners (Phillips Achieva TX, Siemens Avanto, or Siemens Aera) and variable parameters. The field strength varied from 1.5T (n = 76) to 3.0T (n = 52). An appropriate 3D T1-weighted MRI scan acquired in the coronal or sagittal plane was available for 128 patients. Slice thickness varied from 0. to 1 mm.
The scans were preprocessed and analyzed using Freesurfer version 7.1.1 image analysis suite. The precise pipeline is demonstrated at http://surfer.nmr.mgh.harvard.edu. In short, processing included motion correction and averaging of multiple volumetric T1-weighted images, removal of non-brain tissue using a hybrid watershed/surface deformation procedure, automated Talairach transformation, segmentation of the subcortical white matter (WM) and deep gray matter (GM) volumetric structures, intensity normalization, tessellation of the GM/WM boundary, automated topology correction, and surface deformation following intensity gradients to optimally place the gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class. Cortices were parcellated with Desikan atlas, and thicknesses of cerebral lobes were merged as defined in the original publication. Subcortical segmentation was performed using probabilistic atlas. Freesurfer morphometric procedures have been demonstrated to show good test–retest reliability across scanner manufacturers and field strengths [13, 14]; thus, we permitted the use of heterogeneous field strengths.
18Fluorodeoxyglucose PET acquisition and analysis
18Fluorodeoxyglucose PET scans were analyzed from patient archives retrospectively. FDG-PET scanning was performed at the Kuopio University Hospital. Subjects were scanned supine in a quiet room, instructed to remain awake with eyes open or closed. An injection of 200 MBq of [18F]-2-fluoro-2-D-glucose IV was used. The scan was commenced 60 min after tracer injection, and the duration of the scan was 15 min. FDG-PET scans were available for 64 patients.
PET scans were preprocessed and analyzed with SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK; http://www.fil.ion.ucl.ac.uk/spm) software, running on Matlab 2019b (The Mathworks, MA, USA). MRI scans were co-registered to PET scans to minimize normalization errors, and then spatially normalized to the T1 MNI152 (Montreal Neurological Institute) template with a non-linear registration. The PET scans were corrected for partial volume effects using a three tissue compartmental algorithm (Müller–Gärtner method) with PETPVE12 toolbox . Then, the FDG-PET images were normalized to the average count of cerebellar gray matter using an algorithm implemented in PETPVE12 toolbox . Finally, images were smoothed with full width half maximum 8 mm Gaussian kernel to deal with subtle anatomical variation.
Statistical analyses were performed with IBM SPSS Statistic 27. Student’s t-test and Pearson's Chi-squared tests were used to assess differences across EP + and EP- groups regarding age and gender distributions.
General linear model, with age at scan as covariate, was used to compare groups as for the cortical thickness and subcortical volumes. The results were corrected for intracranial volume by a simple division. The results were not corrected for multiple comparisons, however only p-values < 0.01 were considered statistically significant. Considering FDG-PET, after preprocessing steps, scans were analyzed using a linear model with age at scan as a covariate. Regional hypometabolism was tested by a linear contrast (EP + vs. EP-) with a statistical threshold of p < 0.05 with a family-wise error (FWE) correction for multiple comparisons at the voxel-level, with minimal cluster size at k = 80. Since the FWE-corrected results yielded scarce statistically significant results, we also present uncorrected exploratory results that showed p value < 0.001.
The data that support the findings of this study are available from the corresponding author (E.S.) upon reasonable request.