Mechanisms driving pre- and post-stressor repetitive negative thinking: Metacognitions, cognitive avoidance, and thought control

Background and objectives: Repetitive negative thinking (RNT) is common to multiple emotional disorders and occurs before, during, and following a stressor. One replicated difference between common forms of RNT such as worry and rumination is temporal orientation towards a stressor, with worry being more future-oriented and rumination more past-oriented. Different mechanisms may drive RNT at these different time points. The aim of Study 1 was to examine whether previously demonstrated relationships between post-stressor RNT and mechanisms theorized to drive engagement in RNT, including metacognitive beliefs, cognitive avoidance strategies, and thought control strategies, would be replicated with anticipatory (pre-stressor) RNT. The aim of Study 2 was to replicate these associations in a new sample that completed measures of both pre- and post-stressor RNT.Method: Participants in Study 1 (N = 175) completed the RNT-L in anticipation of a stressor, along with measures of metacognitive beliefs, cognitive avoidance strategies, and thought control strategies. Participants in Study 2 (N = 91) completed the measures both before and after a stressor. Results: Pre- and post-stressor RNT were significantly correlated with all three mechanism measures. Metacognitive beliefs that RNT is uncontrollable and dangerous, and the thought control strategy of punishment, were most consistently and uniquely associated with RNT at both time-points.Limitations: Replication with clinical samples and with reference to a broader array of stressors is required. The correlational design precluded causal conclusions.Conclusions: Common and possibly some distinct mechanisms drive RNT before and after a stressor

Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG

Background: Simultaneous EEG-fMRI can reveal haemodynamic changes associated with epileptic activity which may contribute to understanding seizure onset and propagation. Methods: Nine of 83 patients with focal epilepsy undergoing pre-surgical evaluation had seizures during EEG-fMRI and analysed using three approaches, two based on the general linear model (GLM) and one using independent component analysis (ICA): 1. EEGs were divided into up to three phases: early ictal EEG change, clinical seizure onset and late ictal EEG change and convolved with a canonical haemodynamic response function (HRF) (canonical GLM analysis). 2. Seizures lasting three scans or longer were additionally modelled using a Fourier basis set across the entire event (Fourier GLM analysis). 3. Independent component analysis (ICA) was applied to the fMRI data to identify ictal BOLD patterns without EEG. The results were compared with intracranial EEG. Results: The canonical GLM analysis revealed significant BOLD signal changes associated with seizures on EEG in 7/9 patients, concordant with the seizure onset zone in 4/7. The Fourier GLM analysis revealed changes in BOLD signal corresponding with the results of the canonical analysis in two patients. ICA revealed components spatially concordant with the seizure onset zone in all patients (8/9 confirmed by intracranial EEG). Conclusion: Ictal EEG-fMRI visualises plausible seizure related haemodynamic changes. The GLM approach to analysing EEG-fMRI data reveals localised BOLD changes concordant with the ictal onset zone when scalp EEG reflects seizure onset. ICA provides additional information when scalp EEG does not accurately reflect seizures and may give insight into ictal haemodynamics

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

Automatic User Preferences Selection of Smart Hearing Aid Using BioAid

Noisy environments, changes and variations in the volume of speech, and non-face-to-face conversations impair the user experience with hearing aids. Generally, a hearing aid amplifies sounds so that a hearing-impaired person can listen, converse, and actively engage in daily activities. Presently, there are some sophisticated hearing aid algorithms available that operate on numerous frequency bands to not only amplify but also provide tuning and noise filtering to minimize background distractions. One of those is the BioAid assistive hearing system, which is an open-source, freely available downloadable app with twenty-four tuning settings. Critically, with this device, a person suffering with hearing loss must manually alter the settings/tuning of their hearing device when their surroundings and scene changes in order to attain a comfortable level of hearing. However, this manual switching among multiple tuning settings is inconvenient and cumbersome since the user is forced to switch to the state that best matches the scene every time the auditory environment changes. The goal of this study is to eliminate this manual switching and automate the BioAid with a scene classification algorithm so that the system automatically identifies the user-selected preferences based on adequate training. The aim of acoustic scene classification is to recognize the audio signature of one of the predefined scene classes that best represent the environment in which it was recorded. BioAid, an open-source biological inspired hearing aid algorithm, is used after conversion to Python. The proposed method consists of two main parts: classification of auditory scenes and selection of hearing aid tuning settings based on user experiences. The DCASE2017 dataset is utilized for scene classification. Among the many classifiers that were trained and tested, random forests have the highest accuracy of 99.7%. In the second part, clean speech audios from the LJ speech dataset are combined with scenes, and the user is asked to listen to the resulting audios and adjust the presets and subsets. A CSV file stores the selection of presets and subsets at which the user can hear clearly against the scenes. Various classifiers are trained on the dataset of user preferences. After training, clean speech audio was convolved with the scene and fed as input to the scene classifier that predicts the scene. The predicted scene was then fed as input to the preset classifier that predicts the user’s choice for preset and subset. The BioAid is automatically tuned to the predicted selection. The accuracy of random forest in the prediction of presets and subsets was 100%. This proposed approach has great potential to eliminate the tedious manual switching of hearing assistive device parameters by allowing hearing-impaired individuals to actively participate in daily life by automatically adjusting hearing aid settings based on the acoustic scen

Doublecortin-expressing cell types in temporal lobe epilepsy

Doublecortin (DCX) is widely regarded as a marker of immature and migrating neurons during development. While DCX expression persists in adults, particularly in the temporal lobe and neurogenic regions, it is unknown how seizures influence its expression. The aim of the present study was to explore the distribution and characteristics of DCX-expressing cells in surgical and postmortem samples from 40 adult and paediatric patients, with epilepsy and with or without hippocampal sclerosis (HS), compared to post mortem controls. The hippocampus (pes and body), parahippocampal gyrus, amygdala, temporal pole and temporal cortex were examined with DCX immunohistochemistry using four commercially-available DCX antibodies, labelled cells were quantified in different regions of interest as well as their co-expression with cell type specific markers (CD68, Iba1, GFAP, GFAP∂, nestin, SOX2, CD34, OLIG2, PDGFRβ, NeuN) and cell cycle marker (MCM2). Histological findings were compared with clinical data, as well as gene expression data obtained from the temporal cortex of 83 temporal lobe epilepsy cases with HS. DCX immunohistochemistry identified immature (Nestin-/NeuN-) neurons in layer II of the temporal neocortex in patients with and without epilepsy. Their number declined significantly with age but was not associated with the presence of hippocampal sclerosis, seizure semiology or memory dysfunction. DCX+ cells were prominent in the paralaminar nuclei and periamygdalar cortex and these declined with age but were not significantly associated with epilepsy history. DCX expressing cells with ramified processes were prominent in all regions, particularly in the hippocampal subgranular zone, where significantly increased numbers were observed in epilepsy samples compared to controls. DCX ramified cells co-expressed Iba1, CD68 and PDGFRβ, and less frequently MCM2, OLIG2 and SOX2, but no co-localization was observed with CD34, nestin or GFAP/GFAP ∂. Gene expression data from neocortical samples in patients with TLE and HS supported ongoing DCX expression in adults. We conclude that DCX identifies a range of morphological cell types in temporal lobe epilepsy, including immature populations, glial and microglial cell types. Their clinical relevance and biological function requires further study but we show some evidence for alteration with age and in epilepsy

Multi-scale spectrally resolved quantitative fluorescence imaging system: Towards neurosurgical guidance in glioma resection

In glioma resection surgery, the detection of tumour is often guided by using intraoperative fluorescence imaging notably with 5-ALA-PpIX, providing fluorescent contrast between normal brain tissue and the gliomas tissue to achieve improved tumour delineation and prolonged patient survival compared with the conventional white-light guided resection. However, the commercially available fluorescence imaging system relies on surgeon’s eyes to visualise and distinguish the fluorescence signals, which unfortunately makes the resection subjective. In this study, we developed a novel multi-scale spectrally-resolved fluorescence imaging system and a computational model for quantification of PpIX concentration. The system consisted of a wide-field spectrally-resolved quantitative imaging device and a fluorescence endomicroscopic imaging system enabling optical biopsy. Ex vivo animal tissue experiments as well as human tumour sample studies demonstrated that the system was capable of specifically detecting the PpIX fluorescent signal and estimate the true concentration of PpIX in brain specimen

Multi-layer photovoltaic fault detection algorithm

This study proposes a fault detection algorithm based on the analysis of the theoretical curves which describe the behaviour of an existing grid-connected photovoltaic (GCPV) system. For a given set of working conditions, a number of attributes such as voltage ratio (VR) and power ratio (PR) are simulated using virtual instrumentation LabVIEW software. Furthermore, a third-order polynomial function is used to generate two detection limits (high and low limits) for the VR and PR ratios. The high and low detection limits are compared with real-time long-term data measurements from a 1.1 kWp GCPV system installed at the University of Huddersfield, United Kingdom. Furthermore, samples that lie out of the detecting limits are processed by a fuzzy logic classification system which consists of two inputs (VR and PR) and one output membership function. The obtained results show that the fault detection algorithm accurately detects different faults occurring in the PV system. The maximum detection accuracy (DA) of the proposed algorithm before considering the fuzzy logic system is equal to 95.27%; however, the fault DA is increased up to a minimum value of 98.8% after considering the fuzzy logic system