A data-driven machine deep learning approach is proposed for differentiating subjects with Alzheimer’s Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC), by only analyzing ...noninvasive scalp EEG recordings. The methodology here proposed consists of evaluating the power spectral density (PSD) of the 19-channels EEG traces and representing the related spectral profiles into 2-d gray scale images (PSD-images). A customized Convolutional Neural Network with one processing module of convolution, Rectified Linear Units (ReLu) and pooling layer (CNN1) is designed to extract from PSD-images some suitable features and to perform the corresponding two and three-ways classification tasks. The resulting CNN is shown to provide better classification performance when compared to more conventional learning machines; indeed, it achieves an average accuracy of 89.8% in binary classification and of 83.3% in three-ways classification. These results encourage the use of deep processing systems (here, an engineered first stage, namely the PSD-image extraction, and a second or multiple CNN stage) in challenging clinical frameworks.
•Recovery of walking function is one of the main goals of patients after stroke.•RAGT may be considered a valuable tool in improving gait abnormalities.•The earlier the gait training starts, the ...better the motor recovery.
Studies about electromechanical-assisted devices proved the validity and effectiveness of these tools in gait rehabilitation, especially if used in association with conventional physiotherapy in stroke patients.
The aim of this study was to compare the effects of different robotic devices in improving post-stroke gait abnormalities.
A computerized literature research of articles was conducted in the databases MEDLINE, PEDro, COCHRANE, besides a search for the same items in the Library System of the University of Parma (Italy). We selected 13 randomized controlled trials, and the results were divided into sub-acute stroke patients and chronic stroke patients. We selected studies including at least one of the following test: 10-Meter Walking Test, 6-Minute Walk Test, Timed-Up-and-Go, 5-Meter Walk Test, and Functional Ambulation Categories.
Stroke patients who received physiotherapy treatment in combination with robotic devices, such as Lokomat or Gait Trainer, were more likely to reach better results, compared to patients who receive conventional gait training alone. Moreover, electromechanical-assisted gait training in association with Functional Electrical Stimulations produced more benefits than the only robotic treatment (−0.80 −1.14; −0.46, p > .05).
The evaluation of the results confirm that the use of robotics can positively affect the outcome of a gait rehabilitation in patients with stroke. The effects of different devices seems to be similar on the most commonly outcome evaluated by this review.
The red nucleus (RN) is a large subcortical structure located in the ventral midbrain. Although it originated as a primitive relay between the cerebellum and the spinal cord, during its phylogenesis ...the RN shows a progressive segregation between a magnocellular part, involved in the rubrospinal system, and a parvocellular part, involved in the olivocerebellar system. Despite exhibiting distinct evolutionary trajectories, these two regions are strictly tied together and play a prominent role in motor and non-motor behavior in different animal species. However, little is known about their function in the human brain. This lack of knowledge may have been conditioned both by the notable differences between human and non-human RN and by inherent difficulties in studying this structure directly in the human brain, leading to a general decrease of interest in the last decades. In the present review, we identify the crucial issues in the current knowledge and summarize the results of several decades of research about the RN, ranging from animal models to human diseases. Connecting the dots between morphology, experimental physiology and neuroimaging, we try to draw a comprehensive overview on RN functional anatomy and bridge the gap between basic and translational research.
In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive ...impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (<inline-formula> <tex-math notation="LaTeX">p < 0.05 </tex-math></inline-formula>), i.e., a reduced overall coupling strength, specifically in delta and theta bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, theta, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals.
Many studies have demonstrated the usefulness of repetitive task practice by using robotic-assisted gait training (RAGT) devices, including Lokomat, for the treatment of lower limb paresis. Virtual ...reality (VR) has proved to be a valuable tool to improve neurorehabilitation training. The aim of our pilot randomized clinical trial was to understand the neurophysiological basis of motor function recovery induced by the association between RAGT (by using Lokomat device) and VR (an animated avatar in a 2D VR) by studying electroencephalographic (EEG) oscillations.
Twenty-four patients suffering from a first unilateral ischemic stroke in the chronic phase were randomized into two groups. One group performed 40 sessions of Lokomat with VR (RAGT + VR), whereas the other group underwent Lokomat without VR (RAGT-VR). The outcomes (clinical, kinematic, and EEG) were measured before and after the robotic intervention.
As compared to the RAGT-VR group, all the patients of the RAGT + VR group improved in the Rivermead Mobility Index and Tinetti Performance Oriented Mobility Assessment. Moreover, they showed stronger event-related spectral perturbations in the high-γ and β bands and larger fronto-central cortical activations in the affected hemisphere.
The robotic-based rehabilitation combined with VR in patients with chronic hemiparesis induced an improvement in gait and balance. EEG data suggest that the use of VR may entrain several brain areas (probably encompassing the mirror neuron system) involved in motor planning and learning, thus leading to an enhanced motor performance.
Retrospectively registered in Clinical Trials on 21-11-2016, n. NCT02971371 .
Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by ...analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms.
In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree-based supervised methods.
By applying our procedure, we are able to extract reliable human-interpretable classification models that allow to automatically assign the patients into their belonging class. In particular, by exploiting a Wavelet feature extraction we achieve 83%, 92%, and 79% of accuracy when dealing with HC vs AD, HC vs MCI, and MCI vs AD classification problems, respectively.
Finally, by comparing the classification performances with both feature extraction methods, we find out that Wavelets analysis outperforms Fourier. Hence, we suggest it in combination with supervised methods for automatic patients classification based on their EEG signals for aiding the medical diagnosis of dementia.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The role of bone tissue engineering in the field of regenerative medicine has been a main research topic over the past few years. There has been much interest in the use of three-dimensional (3D) ...engineered scaffolds (PLA) complexed with human gingival mesenchymal stem cells (hGMSCs) as a new therapeutic strategy to improve bone tissue regeneration. These devices can mimic a more favorable endogenous microenvironment for cells in vivo by providing 3D substrates which are able to support cell survival, proliferation and differentiation. The present study evaluated the in vitro and in vivo capability of bone defect regeneration of 3D PLA, hGMSCs, extracellular vesicles (EVs), or polyethyleneimine (PEI)-engineered EVs (PEI-EVs) in the following experimental groups: 3D-PLA, 3D-PLA + hGMSCs, 3D-PLA + EVs, 3D-PLA + EVs + hGMSCs, 3D-PLA + PEI-EVs, 3D-PLA + PEI-EVs + hGMSCs.
The structural parameters of the scaffold were evaluated using both scanning electron microscopy and nondestructive microcomputed tomography. Nanotopographic surface features were investigated by means of atomic force microscopy. Scaffolds showed a statistically significant mass loss along the 112-day evaluation.
Our in vitro results revealed that both 3D-PLA + EVs + hGMSCs and 3D-PLA + PEI-EVs + hGMSCs showed no cytotoxicity. However, 3D-PLA + PEI-EVs + hGMSCs exhibited greater osteogenic inductivity as revealed by morphological evaluation and transcriptomic analysis performed by next-generation sequencing (NGS). In addition, in vivo results showed that 3D-PLA + PEI-EVs + hGMSCs and 3D-PLA + PEI-EVs scaffolds implanted in rats subjected to cortical calvaria bone tissue damage were able to improve bone healing by showing better osteogenic properties. These results were supported also by computed tomography evaluation that revealed the repair of bone calvaria damage.
The re-establishing of the integrity of the bone lesions could be a promising strategy in the treatment of accidental or surgery trauma, especially for cranial bones.
Abstract Gait, coordination, and balance may be severely compromised in patients with multiple sclerosis (MS), with considerable consequences on the patient's daily living activities, psychological ...status and quality of life. For this reason, MS patients may benefit from robotic-rehabilitation and virtual reality training sessions. Aim of the present study was to assess the efficacy of robot-assisted gait training (RAGT) equipped with virtual reality (VR) system in MS patients with walking disabilities (EDSS 4.0 to 5.5) as compared to RAGT without VR. We enrolled 40 patients (randomized into two groups) undergoing forty RAGT ± VR sessions over eight weeks. All the patients were assessed at baseline and at the end of the treatment by using specific scales. Effect sizes were very small and non-significant between the groups for Berg Balance Scale (− 0.019, CI95% − 2.403 to 2.365) and TUG (− 0.064, 95%CI − 0.408 to 0.536) favoring RAGT + VR. Effects were moderate-to-large and significant for positive attitude (− 0.505, 95%CI − 3.615 to 2.604) and problem-solving (− 0.905, 95%CI − 2.113 to 0.302) sub-items of Coping Orientation to Problem Experienced, thus largely favoring RAGT + VR. Our findings show that RAGT combined with VR is an effective therapeutic option in MS patients with walking disability as compared to RAGT without VR. We may hypothesize that VR may strengthen RAGT thanks to the entrainment of different brain areas involved in motor panning and learning.
Some evidence suggests that high-intensity motor training slows down the severity of spinocerebellar ataxia. However, whether all patients might benefit from these activities, and by which activity, ...and the underlying mechanisms remain unclear. We provide an update on the effect and limitations of different training programmes in patients with spinocerebellar ataxias. Overall, data converge of the finding that intensive training is still based either on conventional rehabilitation protocols or whole-body controlled videogames (“exergames”). Notwithstanding the limitations, short-term improvement is observed, which tends to be lost once the training is stopped. Exergames and virtual reality can ameliorate balance, coordination, and walking abilities, whereas the efficacy of adapted physical activity, gym, and postural exercises depends on the disease duration and severity. In conclusion, although a disease-modifying effect has not been demonstrated, constant, individually tailored, high-intensity motor training might be effective in patients with degenerative ataxia, even in those with severe disease. These approaches may enhance the remaining cerebellar circuitries or plastically induce compensatory networks. Further research is required to identify predictors of training success, such as the type and severity of ataxia and the level of residual functioning.
Although cerebral white matter lesions (WMLs) are considered as a risk factor for vascular dementia, data on their impact on cerebral hemodynamics are scarce. We test and compare transcranial Doppler ...(TCD) features in WML patients with or without associated cognitive impairment.
A sample of non-demented elderly patients with WMLs was consecutively recruited. Mean blood flow velocity (MBFV), pulsatility index (PI), peak systolic blood flow velocity (PSV), end-diastolic blood flow velocity (EDV), and resistivity index (RI) were recorded from the middle cerebral artery bilaterally. Global cognitive functioning, frontal lobe abilities, functional status, and WML severity were also assessed.
161 patients satisfying the clinical criteria for vascular cognitive impairment-no dementia (VCI-ND) were age-matched with 97 presenting WMLs without any cognitive deficit. VCI-ND patients exhibited a decrease in MBFV and EDV, as well as an increase in PI, RI, and PSV. Moreover, a significant correlation between all TCD parameters and the severity of executive dysfunction was observed, whereas PI, RI, and EDV were significantly correlated with the WML load.
VCI-ND showed a hemodynamic pattern indicative of cerebral hypoperfusion and enhanced vascular resistance. These changes may be considered as the TCD correlate of VCI-ND due to microcirculation pathology. TCD provides useful indices of the occurrence and severity of small vessel disease and executive dysfunction in elderly patients at risk of future dementia.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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