Machine learning models to predict the relationship between printing parameters and tensile strength of 3D Poly (lactic acid) scaffolds for tissue engineering applications.

3D printing is an effective method to prepare 3D scaffolds for tissue engineering applications. However, optimization of printing conditions to obtain suitable mechanical properties for various tissue engineering applications is costly and time consuming. To address this problem, in this study, scikit-learn Python machine learning library was used to apply four machine learning-based approaches which are ordinary least squares (OLS) linear regression, random forest (RF), light gradient Boost (LGBM), extreme gradient boosting (XGB) and artificial neural network models to understand the relationship between 3D printing parameters and tensile strength of poly(lactic acid) (PLA). 68 combinations of process parameters for nozzle temperature, printing speed, layer height and tensile strength were used from investigated research papers. Then, datasets were divided as training (80%) and test (20%). After building the OLS linear regression, RF, LGBM, XGB and artificial neural network models, the correlation heatmap and feature importance of each printing parameter for tensile strength values were determined, respectively. Then, the tensile strength was predicted for real datasets to evaluate the performance of the models. The results demonstrate that XGB model was the most successful in predicting tensile strength among the studied models with anR2value of 0.98 and 0.94 for train and test values, respectively. A closeR2value for the train and test also indicated that there was no overfitting of the data to the model. Finally, SHAP analysis shows significance of each feature on prediction of tensile strength. This study can be extended for independent variables including nozzle pressure, strut size and molecular weight of PLA and dependent variables such as elongation and elastic modulus of PLA which may be a powerful tool to predict the mechanical properties of scaffolds for tissue engineering applications.

Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence.

Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal "multiple-demand" (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions - functionally defined in individual participants - was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.

Investigation of functional variability and connectivity in temporal lobe epilepsy: A resting state fMRI study.

It is crucial to reveal the variability between patients with epilepsy and healthy subjects to elucidate the underpinnings of the disease pathology. Herein, we assessed the inter-subject variability between patients with temporal lobe epilepsy (TLE) and healthy subjects in terms of estimating the functional connectivity using resting-state functional magnetic resonance (rs-fMRI) scans. According to inter-subject variability results between healthy and TLE population, the latter showed more variability mainly in frontoparietal control, default mode, dorsal/ventral attention, visual and somatomotor networks in line with the broad seizure onset and propagation pathway. As a result of 17-Network parcellation, a significant attenuation is observed in functional connectivity, mostly in bilateral frontoparietal control, somatomotor, default mode and ventral attention networks associated with the functional impairment in attention, long/short term memory, executive functioning. The results are in favor of the argument that the functional disruption in TLE spreads throughout the cortex beyond the temporal lobe with an implication of greater diversity in the TLE population.

Achromatic temporal-frequency responses of human lateral geniculate nucleus and primary visual cortex.

The sensitivity of the sensory systems to temporal changes of the environment constitutes one of the critical issues in perception. In the present study, we investigated the human early visual system's dependency on the temporal frequency of visual input using fMRI. Blood oxygen level-dependent (BOLD) responses of the lateral geniculate nucleus (LGN) and primary visual cortex (V1) were investigated in a wide frequency range (6-46Hz) with fine frequency sampling (13 frequencies). Subject-specific functional-anatomic ROIs were derived from the combination of the anatomic template masks and the functional maps derived from multi-session fMRI analyses across all 13 stimulation conditions. Using functional-anatomic ROIs, average responses of LGN and V1 were calculated for each frequency. The V1 surface area was further parsed into 7 eccentricity sectors to detail central and peripheral responses. LGN's response revealed fluctuations on a background of non-significant decrease of the BOLD response with increasing stimulation frequency, while V1 response displayed similar fluctuations with a global maximum in the range of 8-12Hz, but a rapid and significant decrease with increasing stimulation frequency especially above 14Hz. This behavior of V1 response valid for both central and peripheral vision emphasizes that the profound low-pass effect of the visual system to visual input emerges in V1, presumably generated by the intra-cortical circuitry of V1 or projections from extra-striate areas. Besides, the high correlation between LGN and V1 BOLD responses across all visual stimulation frequencies supports the oscillatory tuning in thalamo-cortical interactions as previously claimed in electrophysiological studies.

Bubble stream reveals functionality of the right-to-left shunt: detection of a potential source for air embolism.

The existence of a right-to-left shunt may increase the likelihood of micro-embolism by allowing a flux of bubbles under hyperbaric conditions. The aim of the study was to measure the relationship between these shunts and bubbles in 10 consecutive subjects using trans-thoracic and trans-esophageal echocardiography. In video frames, all cardiac chambers were segmented and bubbles were analyzed by our proposed method and two other methods. The relationship with bubbles and shunts was divided into three classes: no bubbles, 1-20 bubbles, >20 bubbles and measured over 2160 frames. Our sensitivity was 100% and our specificity was between 90.1% and 96.4%. There were 4.32-23.78 bubbles/frame in the left atrium according to our method. After the automatic analysis, shunts were graded double-blinded by two cardiologists. Consequently, we noted that aperture size does not necessarily reflect how active the right-to-left shunt is. Instead, our proposed decay curves constitute a better tool for determining functionality.

Simultaneous EEG/fMRI analysis of the resonance phenomena in steady-state visual evoked responses.

The stability of the steady-state visual evoked potentials (SSVEPs) across trials and subjects makes them a suitable tool for the investigation of the visual system. The reproducible pattern of the frequency characteristics of SSVEPs shows a global amplitude maximum around 10 Hz and additional local maxima around 20 and 40 Hz, which have been argued to represent resonant behavior of damped neuronal oscillators. Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) measurement allows testing of the resonance hypothesis about the frequency-selective increases in SSVEP amplitudes in human subjects, because the total synaptic activity that is represented in the fMRI-Blood Oxygen Level Dependent (fMRI-BOLD) response would not increase but get synchronized at the resonance frequency. For this purpose, 40 healthy volunteers were visually stimulated with flickering light at systematically varying frequencies between 6 and 46 Hz, and the correlations between SSVEP amplitudes and the BOLD responses were computed. The SSVEP frequency characteristics of all subjects showed 3 frequency ranges with an amplitude maximum in each of them, which roughly correspond to alpha, beta and gamma bands of the EEG. The correlation maps between BOLD responses and SSVEP amplitude changes across the different stimulation frequencies within each frequency band showed no significant correlation in the alpha range, while significant correlations were obtained in the primary visual area for the beta and gamma bands. This non-linear relationship between the surface recorded SSVEP amplitudes and the BOLD responses of the visual cortex at stimulation frequencies around the alpha band supports the view that a resonance at the tuning frequency of the thalamo-cortical alpha oscillator in the visual system is responsible for the global amplitude maximum of the SSVEP around 10 Hz. Information gained from the SSVEP/fMRI analyses in the present study might be extrapolated to the EEG/fMRI analysis of the transient event-related potentials (ERPs) in terms of expecting more reliable and consistent correlations between EEG and fMRI responses, when the analyses are carried out on evoked or induced oscillations (spectral perturbations) in separate frequency bands instead of the time-domain ERP peaks.

Changes in BOLD transients with visual stimuli across 1-44 Hz.

The dependency of positive BOLD (PBOLD) and post-stimulus undershoot (PSU) on the temporal frequency of visual stimulation was investigated using stimulation frequencies between 1 and 44 Hz. The PBOLD peak at 8 Hz in primary visual cortex was in line with previous neuroimaging studies. In addition to the 8 Hz peak, secondary peaks were observed for stimulation frequencies at 16 and 24 Hz. These additional local peaks were contrary to earlier fMRI studies which reported either a decrease or a plateau for frequencies above 8 Hz but in line with electrophysiological results obtained in animal local field potential (LFP) measurements and human steady-state visual evoked potential (SSVEP) recordings. Our results also indicate that the dependency of PSU amplitude on stimulus frequency deviates from that of PBOLD. Although their amplitudes were correlated within the 1-13 Hz range, they changed independently at stimulation frequencies between 13 and 44 Hz. The different dependency profiles of PBOLD and PSU to stimulation frequency points to different underlying neurovascular mechanisms responsible for the generation of these BOLD transients with regard to their relation to inhibitory and excitatory neuronal activity.

Implementation of low resolution electro-magnetic tomography with FMRI statistical maps on realistic head models.

Functional neuroimaging studies can be performed by combining the modalities of fMRI and Electroencephalography because of their complementary properties. The main advantage of EEG imaging among other modalities is the high temporal resolution while fMRI has high spatial resolution. So, usage of these procedures is going to help us to gain more information about the functional organization of the brain. In this study, changes in the relationship between Steady State Visual Evoked Potentials (SSVEP) generators and BOLD responses during visual stimulation have been systematically studied with 5 stimulus presentation rates (2, 4, 6, 8, 10) between 2-10 Hz. fMRI Analysis was carried out using Statistical Parametric Mapping (SPM). The result of fMRI analysis is used as a localization mask for SSVEP localization process. SSVEP generators are localized using Low Resolution Electro Magnetic Tomography (LORETA) which is implemented on a realistic head model. Then, for each stimulus frequency voxel by voxel correlation values of the active regions are computed.

Neuroimaging of event related brain potentials (ERP) using fMRI and dipole source reconstruction.

Neuroimaging is an essential tool for the diagnosis of cognitive brain disorders along with the EEG measurements. EEG and fMRI are the two crucial modalities which reflect the functional activity inside the brain. EEG is easy to apply and provides high temporal resolution but has poor spatial resolution. Contrarily, fMRI has a higher spatial resolution while having a poor temporal resolution. In this study, multi modal data sets obtained from Event Related fMRI and EEG measurements are analyzed using SPM and LORETA based dipole source reconstruction techniques, respectively. It has been demonstrated that the generator of N170 component of ERP which is located at superior temporal region is in agreement with the SPM results of fMRI. The results imply that the joint use of fMRI and ERP data helps identifying the physiological and hemodynamic correlates of face recognition by estimating the underlying functional activity in a fine temporal and spatial resolution.

Bayesian EEG dipole source localization using SA-RJMCMC on realistic head model.

In this study, electroencephalography (EEG) inverse problem is formulated using Bayesian inference. The posterior probability distribution of current sources is sampled by Markov Chain Monte Carlo (MCMC) methods. Sampling algorithm is designed by combining Reversible Jump (RJ) which permits trans-dimensional iterations and Simulated Annealing (SA), a heuristic to escape from local optima. Two different approaches to EEG inverse problem, Equivalent Current Dipole (ECD) and Distributed Linear Imaging (DLI) are combined in terms of probability. EEG inverse problem is solved with this probabilistic approach using simulated data on a realistic head model. Localization errors are computed. Comparing to Multiple Signal Classification algorithm (MUSIC) and Low-Resolution Electromagnetic Tomography (LORETA), using MCMC methods with a Bayesian approach is useful for solving the EEG inverse problem.

Automatic detection of bubbles in the subclavian vein using Doppler ultrasound signals.

INTRODUCTION: It is possible to detect venous gas bubbles by listening to the Doppler audio signals. However, a serious disadvantage of the audio evaluation is the inability of continuous monitoring and the inter-rater agreement. Several researchers have worked on the automated detection of emboli, but no current system has the required sensitivity and specificity for clinical use. METHOD: We developed software that integrated frequency filtering, processing, and detection phases of microemboli into a graphical user interface. The detection algorithm consists of a rule-based criterion with a user-defined threshold sliding in-time axis that estimates the duration of the embolic event. Subclavian Doppler audio recordings obtained from a high altitude diving expedition were analyzed using digital filtering and non-linear operator combinations of the software. The data set includes 43 embolic events in 9 recordings from 4 different subjects. RESULTS: It was determined that embolic signals are best differentiated from the background signal at the 4500-8000-Hz frequency band. By using the non-linear "Teager Energy Operator", embolic signals were amplified against their background and a high level of sensitivity and specificity was obtained (83.7% and 97.3%, respectively). The duration of the detected emboli was estimated as 12.17 +/- 4.36 ms (mean +/- SD). DISCUSSION: The optimal frequency band for the detection of subclavian emboli is significantly higher than previous findings for the transcranial site. The duration output of the software can be used to estimate the size and the composition of emboli. Successful integration of the software into an ambulatory detection system may provide important site-specific bubble size distribution data for decompression modeling.

Source localization of subtopographic brain maps for event related potentials (ERP).

Localization of the cognitive activity in the brain is one of the major problems in neuroscience. Current techniques for neuro-imaging are based on functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and Event Related Potential (ERP) recordings. The highest temporal resolution is achieved by ERP, which is crucial for temporal localization of activities. However, the spatial resolution of scalp topography for ERP is low. There is a severe limitation for the parametric inverse solution algorithms that they can only perform well for the temporally uncorrelated sources. In this study, a spatial decomposition method is proposed to separate the temporally correlated sources using their topographies prior to their localization.

Coregistration of fNIRS Data on to the Realistic Head Model.

In this paper, we propose a method to visualize fNIRS(Functional Near Infrared Spectroscopy) data on a realistic head model. In order to illustrate the success of this study, we used fNIRS data obtained during the Stroop task. For the realistic head model, high resolution T1 weighted MR images are used. Preliminary results show that proposed method will be usefull for visualization of fNIRS data and also these results are correlated with EEG (Electroencephalogram) topograpghic mapping for the same Stroop Task.