Whole-body parametric mapping of tumour perfusion in metastatic prostate cancer using long axial field-of-view [15O]H2O PET.

PURPOSE: Tumour perfusion is a nutrient-agnostic biomarker for cancer metabolic rate. Use of tumour perfusion for cancer growth assessment has been limited by complicated image acquisition, image analysis and limited field-of-view scanners. Long axial field-of-view (LAFOV) PET scan using [15O]H2O, allows quantitative assessment of whole-body tumour perfusion. We created a tool for automated creation of quantitative parametric whole-body tumour perfusion images in metastatic cancer. METHODS: Ten metastatic prostate cancer patients underwent dynamic LAFOV [15O]H2O PET (Siemens, Quadra) followed by [18F]PSMA-1007 PET. Perfusion was measured as [15O]H2O K1 (mL/min/mL) with a single-tissue compartment model and an automatically captured cardiac image-derived input function. Parametric perfusion images were automatically calculated using the basis-function method with initial voxel-wise delay estimation and a leading-edge approach. Subsequently, perfusion of volumes-of-interest (VOI) can be directly extracted from the parametric images. We used a [18F]PSMA-1007 SUV 4 fixed threshold for tumour delineation and transferred these VOIs to the perfusion map. RESULTS: For 8 primary tumours, 64 lymph node metastases, and 85 bone metastases, median tumour perfusion were 0.19 (0.15-0.27) mL/min/mL, 0.16 (0.13-0.27) mL/min/mL, and 0.26 (0.21-0.39), respectively. The correlation between calculated perfusion from time-activity-curves and parametric images was excellent (r = 0.99, p < 0.0001). CONCLUSION: LAFOV PET imaging using [15O]H2O enables truly quantitative parametric images of whole-body tumour perfusion, a potential biomarker for guiding personalized treatment and monitoring treatment response.

Splenic switch-off in [15O]H2O-positron emission tomography myocardial perfusion imaging using parametric blood flow images.

BACKGROUND: Evaluation of sufficient adenosine response constitutes a significant challenge in myocardial perfusion imaging (MPI). Splenic switch-off in MPI studies denotes a visually (qualitatively) reduced splenic radiotracer signal during adenosine stress and is considered indicative of sufficient cardiac vasodilation. In this study, we examined semi-quantitative and quantitative approaches to splenic switch-off assessment using [15O]H2O-PET with either summed activity images or calculated parametric splenic blood flow images. METHODS: Cohort 1: 90 clinical patients undergoing [15O]H2O MPI in whom adenosine response was considered clinically adequate were identified to characterize the corresponding splenic switch-off. Spleen stress/rest-ratio (SSR-ratio) was calculated as spleen stress signal intensity/spleen rest signal intensity on both summed activity and parametric blood flow images. Cohort 2: Twenty-five patients with repeat MPI due to suspected insufficient adenosine response were identified to observe if splenic switch-off on the initial MPI could predict the outcome of the repeat MPI. Cohort 3: Fifty-four patients who were considered adenosine responders on MPI and who had a coronary angiogram (CAG) follow-up within 3 months after MPI served as a separate validation group. RESULTS: Splenic switch-off was present in most patients with a clinically sufficient adenosine response (Cohort 1), illustrated by both visual (74.4%-86.7%), semi-quantitative (summed activity images) (85.6%), and quantitative (parametric blood flow images) (92.2%) evaluation, which corresponds to the distribution in patients with sufficient adenosine response and follow-up CAG (Cohort 3). In patients suspected of insufficient adenosine response on the initial MPI (Cohort 2), the repeat MPI only yielded different myocardial blood flow (MBF) results if the initial SSR-ratio was >0.90 on splenic parametric blood flow images. CONCLUSION: quantitative splenic switch-off assessment on parametric blood flow images was superior to the semi-quantitative splenic switch-off approach. Patients with a suspected insufficient initial adenosine response and SSR-ratio >0.90 can benefit from a repeat MPI. Thus, the integration of quantitative splenic switch-off using parametric blood flow images in the evaluation of adenosine response may support future clinical decision-making.

The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection.

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.

Extracardiac findings with increased perfusion during clinical O-15-H2O PET/CT myocardial perfusion imaging: A case series.

BACKGROUND: Coincidental extracardiac findings with increased perfusion were reported during myocardial perfusion imaging (MPI) with various retention radiotracers. Clinical parametric O-15-H2O PET MPI yielding quantitative measures of myocardial blood flow (MBF) was recently implemented at our facility. We aim to explore whether similar extracardiac findings are observed using O-15-H2O. METHODS AND RESULTS: All patients (2963) were scanned with O-15-H2O PET MPI according to international guidelines and extracardiac findings were collected. In contrast to parametric O-15-H2O MBF images, extracardiac perfusion was assessed using summed images. Biopsy histopathology and other imaging modalities served as reference standards. Various malignant lesions with increased perfusion were detected, including lymphomas, large-celled neuroendocrine tumour, breast, and lung cancer plus metastases from colonic and renal cell carcinomas. Furthermore, inflammatory and hyperplastic benign conditions with increased perfusion were observed: rib fractures, gynecomastia, atelectasis, sarcoidosis, pneumonia, chronic lung inflammation and fibrosis, benign lung nodule, chronic diffuse lung infiltrates, pleural plaques and COVID-19 infiltrates. CONCLUSIONS: Malignant and benign extracardiac coincidental findings with increased perfusion are readily visible and frequently seen on O-15-H2O PET MPI. We recommend evaluating the summed O-15-H2O PET images in addition to the low-dose CT attenuation images.

Recurrence rate after radical prostatectomy following primary staging of high-risk prostate cancer with 68Ga-PSMA PET/CT.

BACKGROUND: Accurate primary staging is one of the most important issues for initial management of prostate cancer (PCa) patients to perform an optimal selection of patients for curative intended treatment. 68Ga-Prostate-Specific-Membrane-Antigen (PSMA) PET/CT was found superior to conventional imaging both for detection of recurrence after curative intended treatment and for primary staging. We studied the recurrence rate after radical prostatectomy in high-risk PCa patients primary staged with 68Ga-PSMA PET/CT compared with conventional imaging. MATERIAL AND METHODS: The study included 247 D'Amico high-risk PCa patients treated with radical prostatectomy (RP) after primary staging with 68Ga-PSMA PET/CT and a reference group of 137 high-risk patients with RP after conventional imaging (99mTc bone scintigraphy and CT). Recurrence rates were assessed by Cox regression and Kaplan-Meier analysis. RESULTS: The 5-year recurrence-free survival rate was 71.1% in the 68Ga-PSMA PET/CT cohort compared with 56.4% in the conventional imaging cohort. Primary staging by 68Ga-PSMA PET/CT reduced biochemical recurrence (BCR) risk by 42% (HR = 0.58 (0.41-0.83), p = .004). CONCLUSION: The present data could indicate a lower recurrence rate after RP following primary staging with 68Ga-PSMA PET/CT compared to conventional imaging, likely due to improved selection of patients for surgery.

Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees.

Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.

Implementing Performance Accommodation Mechanisms in Online BCI for Stroke Rehabilitation: A Study on Perceived Control and Frustration.

Brain-computer interfaces (BCIs) are successfully used for stroke rehabilitation, but the training is repetitive and patients can lose the motivation to train. Moreover, controlling the BCI may be difficult, which causes frustration and leads to even worse control. Patients might not adhere to the regimen due to frustration and lack of motivation/engagement. The aim of this study was to implement three performance accommodation mechanisms (PAMs) in an online motor imagery-based BCI to aid people and evaluate their perceived control and frustration. Nineteen healthy participants controlled a fishing game with a BCI in four conditions: (1) no help, (2) augmented success (augmented successful BCI-attempt), (3) mitigated failure (turn unsuccessful BCI-attempt into neutral output), and (4) override input (turn unsuccessful BCI-attempt into successful output). Each condition was followed-up and assessed with Likert-scale questionnaires and a post-experiment interview. Perceived control and frustration were best predicted by the amount of positive feedback the participant received. PAM-help increased perceived control for poor BCI-users but decreased it for good BCI-users. The input override PAM frustrated the users the most, and they differed in how they wanted to be helped. By using PAMs, developers have more freedom to create engaging stroke rehabilitation games.

Modulating Heart Rate Variability through Deep Breathing Exercises and Transcutaneous Auricular Vagus Nerve Stimulation: A Study in Healthy Participants and in Patients with Rheumatoid Arthritis or Systemic Lupus Erythematosus.

Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are associated with an impaired autonomic nervous system and vagus nerve function. Electrical or physiological (deep breathing-DB) vagus nerve stimulation (VNS) could be a potential treatment approach, but no direct comparison has been made. In this study, the effect of transcutaneous auricular VNS (taVNS) and DB on vagal tone was compared in healthy participants and RA or SLE patients. The vagal tone was estimated using time-domain heart-rate variability (HRV) parameters. Forty-two healthy participants and 52 patients performed 30 min of DB and 30 min of taVNS on separate days. HRV was recorded before and immediately after each intervention. For the healthy participants, all HRV parameters increased after DB (SDNN + RMSSD: 21-46%), while one HRV parameter increased after taVNS (SDNN: 16%). For the patients, all HRV parameters increased after both DB (17-31%) and taVNS (18-25%), with no differences between the two types of VNS. DB was associated with the largest elevation of the HRV parameters in healthy participants, while both types of VNS led to elevated HRV parameters in the patients. The findings support a potential use of VNS as a new treatment approach, but the clinical effects need to be investigated in future studies.

Investigating the Dose-Response Relationship between Deep Breathing and Heart Rate Variability in Healthy Participants and Across-Days Reliability in Patients with Rheumatoid Arthritis and Systemic Lupus Erythematosus.

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are associated with autonomic dysfunction, potentially through reduced vagus nerve tone. Vagus nerve stimulation has been proposed as an anti-inflammatory treatment, and it can be performed through deep breathing (DB) exercises. In this study, the dose-response relationship between DB exercises and heart rate variability (HRV) was investigated in healthy participants and reliability across days in patients with RA and SLE. On three separate days, 41 healthy participants performed DB for: 5, 15, or 30 min. On two separate days, 52 RA or SLE patients performed DB with the dose associated with the highest HRV increase in healthy participants. The HRV was estimated from ECG-recordings recorded prior and post the DB exercises. Increases in dose led to larger HRV-responses. Thirty minutes led to the largest HRV-response. In the RA and SLE patients, this dose increased the HRV-parameters consistently across the two days, indicating reliability. DB increases HRV in healthy participants and RA or SLE patients, which indicates stimulation of the vagus nerve. Of the tested durations, 30 min of DB was the optimal period of stimulation. A potential anti-inflammatory effect of DB exercises should be investigated in future studies.

Decoding kinetic features of hand motor preparation from single-trial EEG using convolutional neural networks.

Building accurate movement decoding models from brain signals is crucial for many biomedical applications. Predicting specific movement features, such as speed and force, before movement execution may provide additional useful information at the expense of increasing the complexity of the decoding problem. Recent attempts to predict movement speed and force from the electroencephalogram (EEG) achieved classification accuracies at or slightly above chance levels, highlighting the need for more accurate prediction strategies. Thus, the aims of this study were to accurately predict hand movement speed and force from single-trial EEG signals and to decode neurophysiological information of motor preparation from the prediction strategies. To these ends, a decoding model based on convolutional neural networks (ConvNets) was implemented and compared against other state-of-the-art prediction strategies, such as support vector machines and decision trees. ConvNets outperformed the other prediction strategies, achieving an overall accuracy of 84% in the classification of two different levels of speed and force (four-class classification) from pre-movement single-trial EEG (100 ms and up to 1,600 ms prior to movement execution). Furthermore, an analysis of the ConvNet architectures suggests that the network performs a complex spatiotemporal integration of EEG data to optimize classification accuracy. These results show that movement speed and force can be accurately predicted from single-trial EEG, and that the prediction strategies may provide useful neurophysiological information about motor preparation.

Tumour blood flow for prediction of human prostate cancer aggressiveness: a study with Rubidium-82 PET, MRI and Na+/K+-ATPase-density.

PURPOSE: Tumour blood flow (TBF) is a crucial determinant of cancer growth. Recently, we validated Rubidium-82 (82Rb) positron emission tomography (PET) for TBF measurement in prostate cancer (PCa) and found TBF and cancer aggressiveness positively correlated. The aims of the present study were to determine the ability of TBF for separating significant from insignificant PCa and to examine the relation to underlying Na+/K+-ATPase density, which is relevant as 82Rb is transported intracellularly via the Na+/K+-ATPase. METHODS: One hundred and two patients were included for pelvic 82Rb PET scan prior to magnetic resonance imaging (MRI)-guided prostate biopsy. Findings constituted 100 PCa lesions (86 patients) and 25 benign lesions (16 patients). Tumours were defined on MRI and transferred to 82Rb PET for TBF measurement. Immunohistochemical Na+/K+-ATPase staining was subsequently performed on biopsies. RESULTS: TBF was the superior predictor (rho = 0.68, p < 0.0001, inflammatory lesions excluded) of MRI-guided biopsy grade group (GG) over lowest apparent diffusion coefficient (ADC) value (rho = -0.23, p = 0.01), independent of ADC value and tumour volume (p < 0.0001). PET could separate GG-2-5 from GG-1 and benign lesions with an area under the curve (AUC), sensitivity, and specificity of 0.79, 96%, and 59%, respectively. For separating GG-3-5 from GG-1-2 and benign lesions the AUC, sensitivity, and specificity were 0.82, 95%, and 63%, respectively. Na+/K+-ATPase density per PCa cell profile was 38% lower compared with that of the benign prostate cell profiles. Neither cell density nor Na+/K+-ATPase density determined tumour 82Rb uptake. CONCLUSION: TBF is an independent predictor of PCa aggressiveness and deserves more attention, as it may be valuable in separating clinically significant from insignificant PCa.

Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network.

Error-related potentials (ErrPs) have been proposed as a means for improving brain-computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test-retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test-retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63-72% with LDA performing the best. There was no association between the individuals' impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.

Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton.

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography.

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients.

Deep Breathing Increases Heart Rate Variability in Patients With Rheumatoid Arthritis and Systemic Lupus Erythematosus.

BACKGROUND/OBJECTIVE: Autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) have been associated with an impaired function of the autonomic nervous system and reduced vagus nerve (VN) tone measured through lower heart rate variability (HRV). Targeting the VN through electrical stimulation has been proposed as a treatment strategy with promising results in patients with RA. Moreover, it has been suggested that the VN can be stimulated physiologically through deep breathing. In this study, the aim was to investigate if the VN can be stimulated through deep breathing in patients with RA and SLE, as measured by HRV. METHODS: Fifty-seven patients with RA and SLE performed deep breathing exercises for 30 minutes in this explorative study. Before the breathing exercise, 2 electrocardiogram recordings were obtained to determine the patient's baseline HRV during rest. After the 30-minute breathing exercise, 5 minutes of electrocardiogram recordings were obtained to determine postintervention HRV and used as a measure of vagal activity. RESULTS: No change was observed in the HRV between the 2 recordings prior the exercise, but the heart rate and HRV significantly decreased and increased, respectively, after the deep breathing exercise. CONCLUSIONS: HRV can be modulated in patients with RA and SLE; this may have implications for future treatment with medications in conjunction with deep breathing. However, the biological and clinical effect of deep breathing must be investigated in future studies.

EEG Headset Evaluation for Detection of Single-Trial Movement Intention for Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) can be used in neurorehabilitation; however, the literature about transferring the technology to rehabilitation clinics is limited. A key component of a BCI is the headset, for which several options are available. The aim of this study was to test four commercially available headsets' ability to record and classify movement intentions (movement-related cortical potentials-MRCPs). Twelve healthy participants performed 100 movements, while continuous EEG was recorded from the headsets on two different days to establish the reliability of the measures: classification accuracies of single-trials, number of rejected epochs, and signal-to-noise ratio. MRCPs could be recorded with the headsets covering the motor cortex, and they obtained the best classification accuracies (73%-77%). The reliability was moderate to good for the best headset (a gel-based headset covering the motor cortex). The results demonstrate that, among the evaluated headsets, reliable recordings of MRCPs require channels located close to the motor cortex and potentially a gel-based headset.

Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography.

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient's home.

A Multiday Evaluation of Real-Time Intramuscular EMG Usability with ANN.

Recent developments in implantable technology, such as high-density recordings, wireless transmission of signals to a prosthetic hand, may pave the way for intramuscular electromyography (iEMG)-based myoelectric control in the future. This study aimed to investigate the real-time control performance of iEMG over time. A novel protocol was developed to quantify the robustness of the real-time performance parameters. Intramuscular wires were used to record EMG signals, which were kept inside the muscles for five consecutive days. Tests were performed on multiple days using Fitts' law. Throughput, completion rate, path efficiency and overshoot were evaluated as performance metrics using three train/test strategies. Each train/test scheme was categorized on the basis of data quantity and the time difference between training and testing data. An artificial neural network (ANN) classifier was trained and tested on (i) data from the same day (WDT), (ii) data collected from the previous day and tested on present-day (BDT) and (iii) trained on all previous days including the present day and tested on present-day (CDT). It was found that the completion rate (91.6 ± 3.6%) of CDT was significantly better (p < 0.01) than BDT (74.02 ± 5.8%) and WDT (88.16 ± 3.6%). For BDT, on average, the first session of each day was significantly better (p < 0.01) than the second and third sessions for completion rate (77.9 ± 14.0%) and path efficiency (88.9 ± 16.9%). Subjects demonstrated the ability to achieve targets successfully with wire electrodes. Results also suggest that time variations in the iEMG signal can be catered by concatenating the data over several days. This scheme can be helpful in attaining stable and robust performance.

Therapeutic effects of aerobic exercise on EEG parameters and higher cognitive functions in mild cognitive impairment patients.

BACKGROUND: Mild cognitive impairment (MCI) is becoming an emerging problem for developing countries where there is an increase in expected age. There is no specific curative therapeutic treatment available for these patients. OBJECTIVE: The objective of this study was to evaluate short and long-term changes in the electroencephalogram (EEG) parameters and cognition of MCI patients with aerobic exercises. METHODS: A randomized controlled trial was conducted on 40 patients which were randomly divided into two groups, 'aerobic exercise treatment group (n = 21)' and 'no-aerobic control group (n = 19)'. Short-term effects of exercise were measured after single session of exercise and long-term effects were measured after an 18 sessions (6 weeks) treatment. The outcomes which were measured were, electroenphelogram paramaters (slowness and complexity of the EEG) and cognitive functions (using mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), and trail making test (TMT) A and B). RESULTS: After one session of aerobic exercise there were significant improvements in slowness (delta waves; 0.678 ± 0.035 vs 0.791 ± 0.033; p = .015) and complexity (0.601 ± 0.051 vs 0.470 ± 0.042; p = .027) of the EEG in aerobic exercise treated group as compared to no-aerobic exercise group. After six weeks there were significant improvements in slowness (delta waves; 0.581 ± 0.036 vs 0.815 ± 0.025; p = .005) and complexity (0.751 ± 0.045 vs 0.533 ± 0.046; p = .001) of the EEG in the aerobic group as compared to no-aerobic group. Moreover, significant improvements were observed in the MMSE (p = .032), MoCA (p = .036), TMT-A (p = .005), and TMT-B (p = .007) in aerobic exercise group as compared to no-aerobic group. CONCLUSION: Aerobic exercise showed improvement in cognition after short and long-term treatment in MCI subjects and can be used as potential therapeutic candidate.

Prospective comparative study of 18F-sodium fluoride PET/CT and planar bone scintigraphy for treatment response assessment of bone metastases in patients with prostate cancer.

AIM: To compare 18F-sodium fluoride positron emission tomography/computed tomography (NaF PET/CT) and 99mTc-labelled diphosphonate bone scan (BS) for the monitoring of bone metastases in patients with prostate cancer undergoing anti-cancer treatment. MATERIAL AND METHODS: Data from 64 patients with prostate cancer were included. The patients received androgen-deprivation therapy (ADT), next-generation hormonal therapy (NGH) or chemotherapy. The patients had a baseline scan and 1-3 subsequent scans during six months of treatment. Images were evaluated by experienced nuclear medicine physicians and classified for progressive disease (PD) or non-PD according to the Prostate Cancer Working Group 2 (PCWG-2) criteria. The patients were also classified as having PD/non-PD according to the clinical and prostate-specific antigen (PSA) responses. RESULTS: There was no difference between NaF PET/CT and BS in the detection of PD and non-PD during treatment (McNemar's test, p = .18). The agreement between BS and NaF PET/CT for PD/non-PD was moderate (Cohen's kappa 0.53, 95% confidence interval 0.26-0.79). Crude agreement between BS and NaF PET/CT for the assessment of PD/non-PD was 86% (89% for ADT, n = 28; 88% for NGH, n = 16, and 80% for chemotherapy, n = 20). In most discordant cases, BS found PD when NaF PET/CT did not, or BS detected PD on an earlier scan than NaF PET/CT. Biochemical progression (27%) occurred more frequently than progression on functional imaging (BS, 22% and NaF PET/CT, 14%). Clinical progression was rare (11%), and almost exclusively seen in patients receiving chemotherapy. CONCLUSION: There was no difference between NaF PET/CT and BS in the detection of PD and non-PD; however, BS seemingly detects PD by the PCWG-2 criteria earlier than NaF-PET, which might be explained by the fact that NaF-PET is more sensitive at the baseline scan.