Effect of Pterygium Removal Combined with Conjunctival Autograft on Corneal Parameters in Swept-Source Imaging

Background: Both pterygium ingrowth and excision determine alterations in corneal topography. The aim of this study was to evaluate the influence of pterygium removal combined with conjunctival autografts in addition to the use of human fibrin tissue glue on changes in corneal parameters as measured by 3-D swept-source anterior segment optical coherence tomography (AS-OCT) imaging. Methods: Sixteen eyes (16 patients) with pterygium that qualified for surgical treatment were enrolled in this study. Eye examination, slit lamp, and 3-D AS-OCT (CASIA 2) assessment were performed before the surgery and 7 days, 1 month, and 6 months after pterygium excision. Topographic parameters of both anterior and posterior surfaces of the cornea were analysed at each follow-up visit. Results: The gradual decrease in total astigmatism power from preoperative median 2.75 (6.15) D to 1.2 (1.1) D at 6-month follow-up (p = 0.034) was noted from the day 7 visit. Values were strongly influenced by variations of anterior cornea astigmatism. In contrast, a gradual total HOA reduction at the 1-month (from median 0.79 (1.3) D to 0.44 (0.27) D; p = 0.038) and at 6-month visits (0.25 (0.09); p = 0.001) was observed. Similarly, values were strongly influenced by variations of the anterior. Additionally, total average keratometry values increased from preoperative 44.05 (2.25) D to 44.6 (1.9) (p = 0.043) 1 month after the surgery. Conclusions: Significant steepening of the anterior cornea and a reduction in both astigmatism and HOA were observed after pterygium excision. The anterior corneal surface was an essential component of the total postoperative corneal topography values. Three-dimensional swept-source AS-OCT imaging seems to be a valuable tool for monitoring both the progression of the disease and postoperative effects in pterygium eyes.

Impact of skull sutures, spongiform bone distribution, and aging skull conductivities on the EEG forward and inverse problems

Source imaging is a principal objective for electroencephalography (EEG), the solutions of which require forward problem (FP) computations characterising the electric potential distribution on the scalp due to known sources. Additionally, the EEG-FP is dependent upon realistic, anatomically correct volume conductors and accurate tissue conductivities, where the skull is particularly important. Skull conductivity, however, deviates according to bone composition and the presence of adult sutures. The presented study therefore analyses the effect the presence of adult sutures and differing bone composition have on the EEG-FP and inverse problem (IP) solutions. Utilising a well-established head atlas, detailed head models were generated including compact and spongiform bone and adult sutures. The true skull conductivity was considered as inhomogeneous according to spongiform bone proportion and sutures. The EEG-FP and EEG-IP were solved and compared to results employing homogeneous skull models, with varying conductivities and omitting sutures, as well as using a hypothesised aging skull conductivity model. Significant localised FP errors, with relative error up to 85%, were revealed, particularly evident along suture lines and directly related to the proportion of spongiform bone. This remained evident at various ages. Similar EEG-IP inaccuracies were found, with the largest (maximum 4.14 cm) across suture lines. It is concluded that modelling the skull as an inhomogeneous layer that varies according to spongiform bone proportion and includes differing suture conductivity is imperative for accurate EEG-FP and source localisation calculations. Their omission can result in significant errors, relevant for EEG research and clinical diagnosis.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data

Objective. Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g., ocular, myogenic) and non-biological (e.g., movement-related) artifacts, which -depending on their extent- may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts. Approach. In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during auditory stimulation, as well as event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches. Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts. Significance. Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contribute to further development of mobile brain/body imaging applications.

Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.

Beamforming seizures from the temporal lobe

Background: Surgical treatment of drug-resistant temporal lobe epilepsy (TLE) depends on proper identification of the seizure onset zone (SOZ), and differentiation of mesial, temporolimbic seizure onsets from temporal neocortical seizure onsets. Non-invasive source imaging using electroencephalography (EEG) and magnetoencephalography (MEG) can provide accurate information on interictal spike localization; however, EEG and MEG have low sensitivity for epileptiform activity restricted to deep temporolimbic structures. Moreover, in mesial temporal lobe epilepsy (MTLE), interictal spikes frequently arise in neocortical foci distant from the SOZ, rendering interictal spike localization potentially misleading for presurgical planning. Methods: In this study, we used two different beamformer techniques applied to the MEG signal of ictal events acquired during EEG-MEG recordings in six patients with TLE (three neocortical, three MTLE). The ictal source localization results were compared to the patients' ground truth SOZ localizations determined from intracranial EEG and/or clinical, neuroimaging and postsurgical outcome evidence. Results: Beamformer analysis proved to be highly accurate in all cases and able to reliably identify focal seizure onsets localized to mesial, temporolimbic structures. In three patients, interictal spikes were either absent, too complex for inverse dipole modeling, or localized to anterolateral temporal neocortex distant to a mesial temporal SOZ. Conclusions: This report demonstrates the suitability of MEG beamformer analysis of ictal events in TLE, which can supersede or complement the traditional analysis of interictal spikes. The method outlined is applicable to any type of epileptiform event, greatly expanding the information value of MEG and broadening its utility for presurgical recording in epilepsy.

Differences in verbal and spatial working memory in patients with bipolar II and unipolar depression: an MSI study

Background Depressive symptoms could be similarly expressed in bipolar and unipolar disorder. However, changes in cognition and brain networks might be quite distinct. We aimed to find out the difference in the neural mechanism of impaired working memory in patients with bipolar and unipolar disorder. Method According to diagnostic criteria of bipolar II disorder of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and assessments, 13 bipolar II depression (BP II), 8 unipolar depression (UD) patients and 15 healthy controls (HC) were recruited in the study. We used 2-back tasks and magnetic source imaging (MSI) to test working memory functions and get the brain reactions of the participants. Results Compared with HC, only spatial working memory tasks accuracy was significantly worse in both UD and BP II (p = 0.001). Pearson correlation showed that the stronger the FCs’ strength of MFG-IPL and IPL-preSMA, the higher accuracy of SWM task within left FPN in patients with UD (r = 0.860, p = 0.006; r = 0.752, p = 0.031). However, the FC strength of IFG-IPL was negatively correlated with the accuracy of SWM task within left FPN in patients with BP II (r = − 0.591, p = 0.033). Conclusions Our study showed that the spatial working memory of patients with whether UD or BP II was impaired. The patterns of FCs within these two groups of patients were different when performing working memory tasks.

Imaging the Extent and Location of Spatiotemporally Distributed Epileptiform Sources from MEG Measurements

Non-invasive MEG/EEG source imaging provides valuable information about the epileptogenic brain areas which can be used to aid presurgical planning in focal epilepsy patients suffering from drug-resistant seizures. However, the source extent estimation for electrophysiological source imaging remains to be a challenge and is usually largely dependent on subjective choice. Our recently developed algorithm, fast spatiotemporal iteratively reweighted edge sparsity minimization (FAST-IRES) strategy, has been shown to objectively estimate extended sources from EEG recording, while it has not been applied to MEG recordings. In this work, through extensive numerical experiments and real data analysis in a group of focal drug-resistant epilepsy patients interictal spikes, we demonstrated the ability of FAST-IRES algorithm to image the location and extent of underlying epilepsy sources from MEG measurements. Our results indicate the merits of FAST-IRES in imaging the location and extent of epilepsy sources for pre-surgical evaluation from MEG measurements.