Piezosurgery is a promising meticulous system for bone cutting, based on ultrasound microvibrations. It is thought that the impact of piezosurgery on the integrity of soft tissue is generally low, but it has not been examined critically. The authors undertook an experimental study to evaluate the brain tissue response to skull bone removal using piezosurgery compared with a conventional drilling method. In Wistar male rats, a circular bone window was drilled to the parietal bone using piezosurgery on one side and a conventional bone drill on the other side. The behavioural performance of animals was evaluated using the motor BBB test and sensory plantar test. The brains of animals were evaluated by magnetic resonance imaging (MRI) and histology. The results of MRI showed significantly increased depth and width of the brain lesion in the region of conventional drilling compared with the region where piezosurgery was used. Cresylviolet and NF 160 staining confirmed these findings. There was no significant difference in any of the behavioural tests between the two groups. In conclusion, piezosurgery is a safe method for the performance of osteotomy in close relation to soft tissue, including an extremely injury-sensitive tissue such as brain.

• MeSH
• Astrocytes pathology   MeSH
• Coloring Agents MeSH
• Benzoxazines MeSH
• Time Factors MeSH
• Behavior, Animal physiology   MeSH
• Rats MeSH
• Locomotion physiology   MeSH
• Magnetic Resonance Imaging MeSH
• Brain pathology   MeSH
• Random Allocation MeSH
• Neurofilament Proteins analysis   MeSH
• Osteotomy instrumentation   methods   MeSH
• Oxazines MeSH
• Intraoperative Complications prevention & control   MeSH
• Piezosurgery instrumentation   methods   MeSH
• Motor Activity physiology   MeSH
• Brain Injuries prevention & control   MeSH
• Rats, Wistar MeSH
• Parietal Bone surgery   MeSH
• Thermosensing physiology   MeSH
• Hindlimb physiology   MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Male MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Comparative Study MeSH
• Names of Substances
• Coloring Agents MeSH
• Benzoxazines MeSH
• cresyl violet MeSH Browser
• neurofilament protein M MeSH Browser
• Neurofilament Proteins MeSH
• Oxazines MeSH

Alien limb phenomenon is a rare syndrome associated with a feeling of non-belonging and disowning toward one's limb. In contrast, anarchic limb phenomenon leads to involuntary but goal-directed movements. Alien/anarchic limb phenomena are frequent in corticobasal syndrome (CBS), an atypical parkinsonian syndrome characterized by rigidity, akinesia, dystonia, cortical sensory deficit, and apraxia. The structure-function relationship of alien/anarchic limb was investigated in multi-centric structural magnetic resonance imaging (MRI) data. Whole-group and single-subject comparisons were made in 25 CBS and eight CBS-alien/anarchic limb patients versus controls. Support vector machine was used to see if CBS with and without alien/anarchic limb could be distinguished by structural MRI patterns. Whole-group comparison of CBS versus controls revealed asymmetric frontotemporal atrophy. CBS with alien/anarchic limb syndrome versus controls showed frontoparietal atrophy including the supplementary motor area contralateral to the side of the affected limb. Exploratory analysis identified frontotemporal regions encompassing the pre-/and postcentral gyrus as compromised in CBS with alien limb syndrome. Classification of CBS patients yielded accuracies of 79%. CBS-alien/anarchic limb syndrome was differentiated from CBS patients with an accuracy of 81%. Predictive differences were found in the cingulate gyrus spreading to frontomedian cortex, postcentral gyrus, and temporoparietoocipital regions. We present the first MRI-based group analysis on CBS-alien/anarchic limb. Results pave the way for individual clinical syndrome prediction and allow understanding the underlying neurocognitive architecture.

• Keywords
• Alien limb syndrome, Anarchic limb syndrome, Corticobasal syndrome, Diagnosis prediction, Support vector machine,
• MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   MeSH
• Parkinsonian Disorders diagnostic imaging   MeSH
• Image Processing, Computer-Assisted MeSH
• Aged MeSH
• Alien Limb Phenomenon diagnostic imaging   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided.

• MeSH
• Algorithms * MeSH
• Adult MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Nerve Net physiology   MeSH
• Signal Processing, Computer-Assisted MeSH
• Reproducibility of Results MeSH
• Sensitivity and Specificity MeSH
• Auditory Perception physiology   MeSH
• Evoked Potentials, Auditory physiology   MeSH
• Auditory Cortex physiology   MeSH
• Image Enhancement methods   MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, N.I.H., Extramural MeSH

Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.

• MeSH
• Algorithms MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Neoplasms * MeSH
• Neural Networks, Computer * MeSH
• Handwriting MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: The paper deals with joint analysis of fMRI and scalp EEG data, simultaneously acquired during event-related oddball experiment. The analysis is based on deriving temporal sequences of EEG powers in individual frequency bands for the selected EEG electrodes and using them as regressors in the general linear model (GLM). NEW METHOD: Given the infrequent use of EEG spectral changes to explore task-related variability, we focused on the aspects of parameter setting during EEG regressor calculation and searched for such parameters that can detect task-related variability in EEG-fMRI data. We proposed a novel method that uses relative EEG power in GLM. RESULTS: Parameter, the type of power value, has a direct impact as to whether task-related variability is detected or not. For relative power, the final results are sensitive to the choice of frequency band of interest. The electrode selection also has certain impact; however, the impact is not crucial. It is insensitive to the choice of EEG power series temporal weighting step. Relative EEG power characterizes the experimental task activity better than the absolute power. Absolute EEG power contains broad spectrum component. Task-related relative power spectral formulas were derived. COMPARISON WITH EXISTING METHODS: For particular set of parameters, our results are consistent with previously published papers. Our work expands current knowledge by new findings in spectral patterns of different brain processes related to the experimental task. CONCLUSIONS: To make analysis to be sensitive to task-related variability, the parameters type of power value and frequency band should be set properly.

• Keywords
• Absolute and relative power, EEG Regressor Builder, General linear model (GLM), Regressor, Simultaneous EEG-fMRI, Task-related variability, Visual oddball paradigm,
• MeSH
• Adult MeSH
• Electroencephalography MeSH
• Oxygen MeSH
• Humans MeSH
• Magnetic Resonance Imaging MeSH
• Brain Mapping * MeSH
• Young Adult MeSH
• Brain blood supply   physiology   MeSH
• Brain Waves physiology   MeSH
• Image Processing, Computer-Assisted MeSH
• Spectrum Analysis * MeSH
• Check Tag
• Adult MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Oxygen MeSH

The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs, outputs and states. Our ultimate goal is to establish an alternative to the DCM analysis procedure which would eliminate its main drawbacks, namely the need to pre-define the models structure. The paper discusses results based on simulated data provided by the DCM simulator in the SPM toolbox. Several scenarios are presented, with varying amount of noise and number of data samples.

• MeSH
• Algorithms MeSH
• Electronic Data Processing MeSH
• Bayes Theorem MeSH
• Oxygen metabolism   MeSH
• Humans MeSH
• Magnetic Resonance Imaging methods   MeSH
• Brain Mapping methods   MeSH
• Brain metabolism   pathology   MeSH
• Computer Simulation MeSH
• Signal Processing, Computer-Assisted MeSH
• Software MeSH
• Models, Statistical MeSH
• Models, Theoretical MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Oxygen MeSH