Disruption of white matter integrity and its relationship with cognitive function in non-severe traumatic brain injury.

Background: Impairment in cognitive function is a recognized outcome of traumatic brain injury (TBI). However, the degree of impairment has variable relationship with TBI severity and time post injury. The underlying pathology is often due to diffuse axonal injury that has been found even in mild TBI. In this study, we examine the state of white matter putative connectivity in patients with non-severe TBI in the subacute phase, i.e., within 10 weeks of injury and determine its relationship with neuropsychological scores. Methods: We conducted a case-control prospective study involving 11 male adult patients with non-severe TBI and an age-matched control group of 11 adult male volunteers. Diffusion MRI scanning and neuropsychological tests were administered within 10 weeks post injury. The difference in fractional anisotropy (FA) values between the patient and control groups was examined using tract-based spatial statistics. The FA values that were significantly different between patients and controls were then correlated with neuropsychological tests in the patient group. Results: Several clusters with peak voxels of significant FA reductions (p < 0.05) in the white matter skeleton were seen in patients compared to the control group. These clusters were located in the superior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus, and cingulum, as well as white matter fibers in the area of genu of corpus callosum, anterior corona radiata, superior corona radiata, anterior thalamic radiation and part of inferior frontal gyrus. Mean global FA magnitude correlated significantly with MAVLT immediate recall scores while matrix reasoning scores correlated positively with FA values in the area of right superior fronto-occipital fasciculus and left anterior corona radiata. Conclusion: The non-severe TBI patients had abnormally reduced FA values in multiple regions compared to controls that correlated with several measures of executive function during the sub-acute phase of TBI.

Alteration in the Functional Organization of the Default Mode Network Following Closed Non-severe Traumatic Brain Injury.

The debilitating effect of traumatic brain injury (TBI) extends years after the initial injury and hampers the recovery process and quality of life. In this study, we explore the functional reorganization of the default mode network (DMN) of those affected with non-severe TBI. Traumatic brain injury (TBI) is a wide-spectrum disease that has heterogeneous effects on its victims and impacts everyday functioning. The functional disruption of the default mode network (DMN) after TBI has been established, but its link to causal effective connectivity remains to be explored. This study investigated the differences in the DMN between healthy participants and mild and moderate TBI, in terms of functional and effective connectivity using resting-state functional magnetic resonance imaging (fMRI). Nineteen non-severe TBI (mean age 30.84 ± 14.56) and twenty-two healthy (HC; mean age 27.23 ± 6.32) participants were recruited for this study. Resting-state fMRI data were obtained at the subacute phase (mean days 40.63 ± 10.14) and analyzed for functional activation and connectivity, independent component analysis, and effective connectivity within and between the DMN. Neuropsychological tests were also performed to assess the cognitive and memory domains. Compared to the HC, the TBI group exhibited lower activation in the thalamus, as well as significant functional hypoconnectivity between DMN and LN. Within the DMN nodes, decreased activations were detected in the left inferior parietal lobule, precuneus, and right superior frontal gyrus. Altered effective connectivities were also observed in the TBI group and were linked to the diminished activation in the left parietal region and precuneus. With regard to intra-DMN connectivity within the TBI group, positive correlations were found in verbal and visual memory with the language network, while a negative correlation was found in the cognitive domain with the visual network. Our results suggested that aberrant activities and functional connectivities within the DMN and with other RSNs were accompanied by the altered effective connectivities in the TBI group. These alterations were associated with impaired cognitive and memory domains in the TBI group, in particular within the language domain. These findings may provide insight for future TBI observational and interventional research.

Microwave 3D Imaging System Featuring the Phase Coherence Factor for Improved Beamforming.

BACKGROUND: This paper presents an improved radar-based imaging system for breast cancer detection that features p-slot ultrawideband antennae in a 32-array set-up. The improved reconstruction algorithm incorporates the phase coherence factor (PCF) into the conventional delay and sum (DAS) beamforming algorithm, thus effectively suppressing noise arising from the side- and gratinglobe interferences. METHODS: The system is tested by using several breast models fabricated from chemical mixtures formulated on the basis of realistic human tissues. Each model is placed in a hemispherical breast radome that was fabricated from polylactide material and surrounded by 32 p-slot antennae mounted in four concentric layers. These antennae are connected to an 8.5 GHz vector network analyser through two 16-channel multiplexers that automatically switch different combinations of transmitter and receiver pairs in a sequential manner. RESULTS: The system can accurately detect 5 mm tumours in a complex and homogeneously dense 3D breast model with an average signal-to-clutter ratio and full-width half-maximum of 7.0 dB and 2.3 mm, respectively. These values are more competitive than the values of other beamforming algorithms, even with contrasts as low as 1:2. CONCLUSION: The proposed PCF-weighted DAS is the best-performing algorithm amongst the tested beamforming techniques. This research paves the way for a clinical trial involving human subjects. Our laboratory is planning such a trial as part of future work.

Neural alterations in working memory of mild-moderate TBI: An fMRI study in Malaysia.

Working memory (WM) encompasses crucial cognitive processes or abilities to retain and manipulate temporary information for immediate execution of complex cognitive tasks in daily functioning such as reasoning and decision-making. The WM of individuals sustaining traumatic brain injury (TBI) was commonly compromised, especially in the domain of WM. The current study investigated the brain responses of WM in a group of participants with mild-moderate TBI compared to their healthy counterparts employing functional magnetic resonance imaging. All consented participants (healthy: n = 26 and TBI: n = 15) performed two variations of the n-back WM task with four load conditions (0-, 1-, 2-, and 3-back). The respective within-group effects showed a right hemisphere-dominance activation and slower reaction in performance for the TBI group. Random-effects analysis revealed activation difference between the two groups in the right occipital lobe in the guided n-back with cues, and in the bilateral occipital lobe, superior parietal region, and cingulate cortices in the n-back without cues. The left middle frontal gyrus was implicated in the load-dependent processing of WM in both groups. Further group analysis identified that the notable activation changes in the frontal gyri and anterior cingulate cortex are according to low and high loads. Though relatively smaller in scale, this study was eminent as it clarified the neural alterations in WM in the mild-moderate TBI group compared to healthy controls. It confirmed the robustness of the phenomenon in TBI with the reproducibility of the results in a heterogeneous non-Western sample.

Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography.

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

Arrangements of Resting State Electroencephalography as the Input to Convolutional Neural Network for Biometric Identification.

Biometric is an important field that enables identification of an individual to access their sensitive information and asset. In recent years, electroencephalography- (EEG-) based biometrics have been popularly explored by researchers because EEG is able to distinct between two individuals. The literature reviews have shown that convolutional neural network (CNN) is one of the classification approaches that can avoid the complex stages of preprocessing, feature extraction, and feature selection. Therefore, CNN is suggested to be one of the efficient classifiers for biometric identification. Conventionally, input to CNN can be in image or matrix form. The objective of this paper is to explore the arrangement of EEG for CNN input to investigate the most suitable input arrangement of EEG towards the performance of EEG-based identification. EEG datasets that are used in this paper are resting state eyes open (REO) and resting state eyes close (REC) EEG. Six types of data arrangement are compared in this paper. They are matrix of amplitude versus time, matrix of energy versus time, matrix of amplitude versus time for rearranged channels, image of amplitude versus time, image of energy versus time, and image of amplitude versus time for rearranged channels. It was found that the matrix of amplitude versus time for each rearranged channels using the combination of REC and REO performed the best for biometric identification, achieving validation accuracy and test accuracy of 83.21% and 79.08%, respectively.

Low electric field DNA separation and in-channel amperometric detection by microchip capillary electrophoresis.

Miniaturisation of microchip capillary electrophoresis (MCE) is becoming an increasingly important research topic, particularly in areas related to micro total analysis systems or lab on a chip. One of the important features associated with the miniaturised MCE system is the portable power supply unit. In this work, a very low electric field MCE utilising an amperometric detection scheme was designed for use in DNA separation. The device was fabricated from a glass/polydimethylsiloxane hybrid engraved microchannel with platinum electrodes sputtered onto a glass substrate. Measurement was based on a three-electrode arrangement, and separation was achieved using a very low electric field of 12 V/cm and sample volume of 1.5 µl. The device was tested using two commercial DNA markers of different base pair sizes. The results are in agreement with conventional electrophoresis, but with improved resolution. The sensitivity consistently higher than 100 nA, and the separation time approximately 45 min, making this microchip an ideal tool for DNA analysis.

A preliminary study of using active vision guided robotic arm for bone drilling in a developing country.

BACKGROUND: The study assesses the capability and accuracy of a robotic arm to perform burr holes. MATERIAL AND METHODS: The robotic systems are instructed to recognize targets on artificial skull models placed in different positions and to make burr holes. RESULTS: The accuracy ranged from 0.1 to 1.0 mm. CONCLUSION: Robotic arms are capable to perform basic surgical tasks. However, further improvement needs to be done to refine its accuracy and capability.

Robotic neurosurgery: a preliminary study using an active vision-guided robotic arm for bone drilling and endoscopic manoeuvres.

BACKGROUND: Surgical robots have been appearing in operating rooms over the past decade, and neurosurgery has been one of the pioneers in this area. In neurosurgery, the clinical use of robots has been limited to stereotactic procedures and endoscopic manoeuvres, although the brain is a unique organ and well-suited for robotic application. The aim of this study was to assess the ability of our vision-guided robotic system to perform basic neurosurgical procedures. METHODS: THE STUDY WAS DIVIDED INTO TWO PARTS: bone drilling and endoscopic manoeuvres. The robotic system was instructed to recognise targets on artificial skull models placed in different positions (supine, lateral, sitting, and prone) and to make burr holes. A total of 10 selected burr holes were used to assess the capability of the robot to insert an endoscope. RESULTS: The accuracy ranged 0.1-1.0 mm with repeatability ranged 0.03-0.92 mm. CONCLUSION: Generally, the present robotic system is able to perform the surgical tasks. However, further study is needed to refine the robotic system, including the safety mechanisms.