Limb-kinetic apraxia affects activities of daily living in Parkinson's disease: a multi-center study.

BACKGROUND AND PURPOSE: Impaired dexterity (fine hand movements) is often present in Parkinson's disease (PD), even at early to moderate disease stages. It has a detrimental impact on activities of daily living (ADL) such as buttoning, contributing to reduced quality of life. Limb-kinetic apraxia, a loss of the ability to make precise, independent but coordinated finger and hand movements, may contribute to impaired dexterity even more than bradykinesia per se. However, the impact of limb-kinetic apraxia on ADL remains controversial. Our aim was to identify the strongest predictor of buttoning and unbuttoning in PD. It was hypothesized that coin rotation (a surrogate of limb-kinetic apraxia) represents the most important determinant. METHODS: Sixty-four right-handed, early to moderate PD patients were recruited from three movement disorder centers (Hoehn andYahr stages 1-3). Buttoning, unbuttoning and coin rotation (right and left hand) represented the target tasks. Motor impairment was assessed according to the Unified Parkinson's Disease Rating Scale. RESULTS: Multiple linear regression analysis showed that coin rotation with the right hand was the only significant predictor of buttoning (P < 0.001) and unbuttoning (P = 0.002). Notably, measures of bradykinesia or overall motor impairment did not represent significant predictors. CONCLUSIONS: Constituting the novel key finding, limb-kinetic apraxia seems to be particularly relevant for ADL requiring dexterity skills in PD, even at early to moderate disease stages. Our results prompt research into the pathophysiological background and therapeutic options to treat limb-kinetic apraxia. The simple coin rotation test provides valuable information about ADL-related dexterity skills.

Olfactory training induces changes in regional functional connectivity in patients with long-term smell loss.

Recently, olfactory training has been introduced as a promising treatment for patients with olfactory dysfunction. However, less is known about the neuronal basis and the influence on functional networks of this training. Thus, we aimed to investigate the neuroplasticity of chemosensory perception through an olfactory training program in patients with smell loss. The experimental setup included functional MRI (fMRI) experiments with three different types of chemosensory stimuli. Ten anosmic patients (7f, 3m) and 14 healthy controls (7f, 7m) underwent the same testing sessions. After a 12-week olfactory training period, seven patients (4f, 3m) were invited for follow-up testing using the same fMRI protocol. Functional networks were identified using independent component analysis and were further examined in detail using functional connectivity analysis. We found that anosmic patients and healthy controls initially use the same three networks to process chemosensory input: the olfactory; the somatosensory; and the integrative network. Those networks did not differ between the two groups in their spatial extent, but in their functional connectivity. After the olfactory training, the sensitivity to detect odors significantly increased in the anosmic group, which was also manifested in modifications of functional connections in all three investigated networks. The results of this study indicate that an olfactory training program can reorganize functional networks, although, initially, no differences in the spatial distribution of neural activation were observed.

A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning?

OBJECTIVES: Measurements of resting-state networks (RSNs) have been used to investigate a wide range of diseases, such as dementia or epilepsy. This raises the question whether this method could also serve as a pre-surgical planning tool. Generating reliable functional connectivity patterns is of crucial importance, particularly for pre-surgical planning, as these patterns may directly affect the outcome. METHODS: This study investigated the reproducibility of four commonly used resting-state conditions: fixation of a black crosshair on a white screen; fixation of the center of a black screen; eyes-closed and fixation of the words "Entspann dich!" (Engl., "relax"). Ten healthy, right-handed male subjects (mean age, 25 years; SD 2) participated in the experiment. The spatial overlap for different RSNs across the four conditions was calculated. RESULTS: The spatial overlap across all four conditions was calculated for each seed region on a single subject and at the group level. Activation maps at the single-subject and group levels were highly stable, especially for the reading network (RNW). The lowest consistency measures were found for the visual network (VIN). At the single-subject level spatial overlap values ranged from 0.31 (VIN) to 0.45 (RNW). CONCLUSION: These findings suggest that RSN measurements are a reliable tool to assess language-related networks in clinical settings. Generally, resting-state conditions showed comparable activation patterns, therefore no specific conditions appears to be preferable.

The benefits of skull stripping in the normalization of clinical fMRI data.

Establishing a reliable correspondence between lesioned brains and a template is challenging using current normalization techniques. The optimum procedure has not been conclusively established, and a critical dichotomy is whether to use input data sets which contain skull signal, or whether skull signal should be removed. Here we provide a first investigation into whether clinical fMRI benefits from skull stripping, based on data from a presurgical language localization task. Brain activation changes related to deskulled/not-deskulled input data are determined in the context of very recently developed (New Segment, Unified Segmentation) and standard normalization approaches. Analysis of structural and functional data demonstrates that skull stripping improves language localization in MNI space - particularly when used in combination with the New Segment normalization technique.