Integrative role of attention networks in frequency-dependent modular organization of human brain.

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain.

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).

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).

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.