Dutch Ministry of Health, Welfare and Sport; Dutch Innovation Fund of Collaborative Health Insurances; and Netherlands Organisation for Health Research and Development.

Apathy is a frequently reported neuropsychiatric symptom in Parkinson's disease (PD), but its prevalence and clinical correlates are debated. We aimed to address these issues by conducting a systematic review and meta-analysis. Embase, Medline/PubMed, and PsychINFO databases were searched for relevant studies. Data were extracted by two independent observers, using predefined extraction forms tailored specifically to the research question. From 1,702 titles and abstracts, 23 studies were selected. Meta-analysis showed a prevalence of apathy in PD of 39.8% (n = 5,388, 905% CI 34.6-45.0%). Apathy was associated with higher age (3.3 years, 95% CI = 1.7-4.9), lower mean Mini-Mental State Evaluation (MMSE) score (-1.4 points, 95% CI = -2.1 to -0.8), an increased risk of co-morbid depression (relative risk [RR] = 2.3, 95% CI = 1.9-2.8), higher Unified Parkinson's Disease Rating Scale (UPDRS) motor score (6.5 points, 95% CI = 2.6-10.3), and more severe disability (Hedges-G = 0.5, 95% CI = 0.3-0.6). Half of the patients with apathy had concomitant depression (57.2%, 95% CI = 49.4-64.9%), and this estimate was similar after exclusion of patients with cognitive impairment (52.5%, 95% CI = 42.2%-62.8%). In conclusion, we found that apathy affects almost 40% of patients with PD. Several factors influence reported prevalence rates, contributing to the considerable heterogeneity in study results. Half of patients with apathy do not suffer from concomitant depression or cognitive impairment, confirming its status as a separate clinical syndrome in PD. The pervasiveness of apathy in PD warrants research into its treatment, although different underlying pathophysiological mechanisms may require different treatment strategies. Treatment of apathy could improve patient quality of life, reduce caregiver burden, alleviate disability by increasing motivation for self-care, and reduce cognitive impairment by improving executive functioning.

IntroductionThe segmentation and volumetric quantification of white matter (WM) lesions play an important role in monitoring and studying neurological diseases such as multiple sclerosis (MS) or cerebrovascular disease. This is often interactively done using 2D magnetic resonance images. Recent developments in acquisition techniques allow for 3D imaging with much thinner sections, but the large number of images per subject makes manual lesion outlining infeasible. This warrants the need for a reliable automated approach. Here we aimed to improve k nearest neighbor (kNN) classification of WM lesions by optimizing intensity normalization and using spatial tissue type priors (TTPs).MethodsThe kNN-TTP method used kNN classification with 3.0 T 3DFLAIR and 3DT1 intensities as well as MNI-normalized spatial coordinates as features. Additionally, TTPs were computed by nonlinear registration of data from healthy controls. Intensity features were normalized using variance scaling, robust range normalization or histogram matching. The algorithm was then trained and evaluated using a leave-one-out experiment among 20 patients with MS against a reference segmentation that was created completely manually. The performance of each normalization method was evaluated both with and without TTPs in the feature set. Volumetric agreement was evaluated using intra-class coefficient (ICC), and voxelwise spatial agreement was evaluated using Dice similarity index (SI). Finally, the robustness of the method across different scanners and patient populations was evaluated using an independent sample of elderly subjects with hypertension.ResultsThe intensity normalization method had a large influence on the segmentation performance, with average SI values ranging from 0.66 to 0.72 when no TTPs were used. Independent of the normalization method, the inclusion of TTPs as features increased performance particularly by reducing the lesion detection error. Best performance was achieved using variance scaled intensity features and including TTPs in the feature set: this yielded ICC = 0.93 and average SI = 0.75 ± 0.08. Validation of the method in an independent sample of elderly subjects with hypertension, yielded even higher ICC = 0.96 and SI = 0.84 ± 0.14.ConclusionAdding TTPs increases the performance of kNN based MS lesion segmentation methods. Best performance was achieved using variance scaling for intensity normalization and including TTPs in the feature set, showing excellent agreement with the reference segmentations across a wide range of lesion severity, irrespective of the scanner used or the pathological substrate of the lesions.

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