Time course of EEG complexity reflects attentional engagement during listening to speech in noise.

Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.

Health Outcomes with Curative and Palliative Therapies in Real World: Role of the Quality of Life Summary Score in Thoracic Oncology Patients.

BACKGROUND: For patients receiving therapy with curative or palliative intent for a thoracic malignancy, prediction of quality of life (QOL), once therapy starts, remains challenging. The role of health assessments by the patient instead of the doctor herein remains ill-defined. AIMS: To assess the evolution of QOL in patients with thoracic malignancies treated with curative and palliative intent, respectively. To identify factors that determine QOL one year after the start of cancer therapy. To identify factors that affect survival. METHODS: We prospectively included consecutive patients with a thoracic malignancy who were starting anti-cancer therapy and measured QOL with QLQ-C30 before the start of therapy, and thereafter at regular intervals for up to 12 months. A multivariate regression analysis of the global health score (GHS) and QOL summary scores (QSS) one year after the start of therapy was conducted. A proportional hazards Cox regression was conducted to investigate the effects of case-mix variables on survival. RESULTS: Of 587 new patients, 375 started different forms of therapy. Most had non-small cell lung cancer (n = 298), 35 had small cell lung cancer, and 42 had other thoracic malignancies or were diagnosed on imaging alone. There were 203 who went for a curative intent and 172 for a palliative intent strategy. The WHO score of 0-1 was more prevalent in the former group (p = 0.02), and comorbidities were equally distributed. At baseline, all QOL indices were better in the curative group (p < 0.05). The curative group was characterized by a significant worsening of GHS and QSS (p < 0.05). The palliative group was characterized by an improvement in GHS and emotional health (p < 0.05), while other dimensions of functioning remained stable. GHS at 12 months was estimated in a multivariate linear regression model (R2 = 0.23-p < 0.001) based on baseline GHS, QSS, and comorbidity burden. QSS at 12 months was estimated (R2 = 0.31-p < 0.001) by baseline QSS and therapeutic intent strategy (curative vs. palliative). The prognostic factors for overall survival were the type of therapy (curative vs. palliative intent, p < 0.001) and occurrence of early toxicity-related hospitalization (grade ≥ 3, p = 0.001). CONCLUSION: Patients with thoracic malignancies treated with curative intent experience a worsening of their QOL in the first year, whereas those receiving palliative anti-cancer therapy do not. QOL one year after the start of therapy depends on the baseline health scores as determined by the patient, comorbidity burden, and therapeutic strategy. Survival depends on therapeutic strategy and early hospitalization due to toxicity.

Parkinson's disease affects the neural alpha oscillations associated with speech-in-noise processing.

Parkinson's disease (PD) has increasingly been associated with auditory dysfunction, including alterations regarding the control of auditory information processing. Although these alterations may interfere with the processing of speech in degraded listening conditions, behavioural studies have generally found preserved speech-in-noise recognition in PD. However, behavioural speech audiometry does not capture the neurophysiological mechanisms supporting speech-in-noise processing. Therefore, the aim of this study was to investigate the neural oscillatory mechanisms associated with speech-in-noise processing in PD. Twelve persons with PD and 12 age- and gender-matched healthy controls (HCs) were included in this study. Persons with PD were studied in the medication off condition. All subjects underwent an audiometric screening and performed a sentence-in-noise recognition task under simultaneous electroencephalography (EEG) recording. Behavioural speech recognition scores and self-reported ratings of effort, performance, and motivation were collected. Time-frequency analysis of EEG data revealed no significant difference between persons with PD and HCs regarding delta-theta (2-8 Hz) inter-trial phase coherence to noise and sentence onset. In contrast, significantly increased alpha (8-12 Hz) power was found in persons with PD compared with HCs during the sentence-in-noise recognition task. Behaviourally, persons with PD demonstrated significantly decreased speech recognition scores, whereas no significant differences were found regarding effort, performance, and motivation ratings. These results suggest that persons with PD allocate more cognitive resources to support speech-in-noise processing. The interpretation of this finding is discussed in the context of a top-down mediated compensation mechanism for inefficient filtering and degradation of auditory input in PD.

Developing a Multi-channel Beamformer by Enhancing Spatially Constrained ICA for Recovery of Correlated EEG Sources.

BACKGROUND: Brain source imaging based on electroencephalogram (EEG) data aims to recover the neuron populations' activity producing the scalp potentials. This procedure is known as the EEG inverse problem. Recently, beamformers have gained a lot of consideration in the EEG inverse problem. OBJECTIVE: Beamformers lack acceptable performance in the case of correlated brain sources. These sources happen when some regions of the brain have simultaneous or correlated activities such as auditory stimulation or moving left and right extremities of the body at the same time. In this paper, we have developed a multichannel beamformer robust to correlated sources. MATERIAL AND METHODS: In this simulation study, we have looked at the problem of brain source imaging and beamforming from a blind source separation point of view. We focused on the spatially constraint independent component analysis (scICA) algorithm, which generally benefits from the pre-known partial information of mixing matrix, and modified the steps of the algorithm in a way that makes it more robust to correlated sources. We called the modified scICA algorithm Multichannel ICA based EEG Beamformer (MIEB). RESULTS: We evaluated the proposed algorithm on simulated EEG data and compared its performance quantitatively with three algorithms scICA, linearly-constrained minimum-variance (LCMV) and Dual-Core beamformers; it is considered that the latter is specially designed to reconstruct correlated sources. CONCLUSION: The MIEB algorithm has much better performance in terms of normalized mean squared error in recovering the correlated/uncorrelated sources both in noise free and noisy synthetic EEG signals. Therefore, it could be used as a robust beamformer in recovering correlated brain sources.

EEG Correlates of Learning From Speech Presented in Environmental Noise.

How the human brain retains relevant vocal information while suppressing irrelevant sounds is one of the ongoing challenges in cognitive neuroscience. Knowledge of the underlying mechanisms of this ability can be used to identify whether a person is distracted during listening to a target speech, especially in a learning context. This paper investigates the neural correlates of learning from the speech presented in a noisy environment using an ecologically valid learning context and electroencephalography (EEG). To this end, the following listening tasks were performed while 64-channel EEG signals were recorded: (1) attentive listening to the lectures in background sound, (2) attentive listening to the background sound presented alone, and (3) inattentive listening to the background sound. For the first task, 13 lectures of 5 min in length embedded in different types of realistic background noise were presented to participants who were asked to focus on the lectures. As background noise, multi-talker babble, continuous highway, and fluctuating traffic sounds were used. After the second task, a written exam was taken to quantify the amount of information that participants have acquired and retained from the lectures. In addition to various power spectrum-based EEG features in different frequency bands, the peak frequency and long-range temporal correlations (LRTC) of alpha-band activity were estimated. To reduce these dimensions, a principal component analysis (PCA) was applied to the different listening conditions resulting in the feature combinations that discriminate most between listening conditions and persons. Linear mixed-effect modeling was used to explain the origin of extracted principal components, showing their dependence on listening condition and type of background sound. Following this unsupervised step, a supervised analysis was performed to explain the link between the exam results and the EEG principal component scores using both linear fixed and mixed-effect modeling. Results suggest that the ability to learn from the speech presented in environmental noise can be predicted by the several components over the specific brain regions better than by knowing the background noise type. These components were linked to deterioration in attention, speech envelope following, decreased focusing during listening, cognitive prediction error, and specific inhibition mechanisms.

Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG.

Methamphetamine (meth) is potently addictive and is closely linked to high crime rates in the world. Since meth withdrawal is very painful and difficult, most abusers relapse to abuse in traditional treatments. Therefore, developing accurate data-driven methods based on brain functional connectivity could be helpful in classifying and characterizing the neural features of meth dependence to optimize the treatments. Accordingly, in this study, computation of functional connectivity using resting-state EEG was used to classify meth dependence. Firstly, brain functional connectivity networks (FCNs) of 36 meth dependent individuals and 24 normal controls were constructed by weighted phase lag index, in six frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-15 Hz), beta (15-30 Hz), gamma (30-45 Hz) and wideband (1-45 Hz).Then, significant differences in graph metrics and connectivity values of the FCNs were used to distinguish the two groups. Support vector machine classifier had the best performance with 93% accuracy, 100% sensitivity, 83% specificity and 0.94 F-score for differentiating between MDIs and NCs. The best performance yielded when selected features were the combination of connectivity values and graph metrics in the beta frequency band.

Analysis of induced error by susceptibility effect in low-density gel dosimeters.

PURPOSE: In low-density (LD) gel dosimeter, diffusive spin-spin relaxation rate (R2)-dispersion caused by susceptibility-induced internal gradient leads to a significant deviation in the measured R2 from the real value. In this study, the effect of induced internal gradient on R2 was visualized and quantified algebraically as an important cause of inaccuracy in LD gel dosimeters. MATERIALS AND METHODS: In this method, two sets of LD and unit-density (UD) gel dosimeters were prepared. The LD gel was made by mixing the UD gel with expanded polystyrene spheres. The R2 was used to determine the spatially resolved decay rates due to diffusion in internal magnetic field. The internal gradient was calculated for a multiple spin-echo sequence. RESULTS: It is shown that in a LD gel, the internal gradient leads to overestimation of mean R2 value (R2mean). Pixel-by-pixel R2 measurements inside a LD gel showed significant deviation from R2 mapping in UD gel. CONCLUSION: It appears that significant differences between R2mean in a selected region of interest and pixel-by-pixel R2 values are the main source of inaccuracy in dose mapping of a LD gel.

A novel quantification method for low-density gel dosimeter.

AIM: Low signal-to-noise ratio (SNR) images of lung-like (low-density [LD]) gel dosimeters, compared to unit-density (UD) gels, necessitate the use of different quantification methods. SETTING AND DESIGN: In this study, a new method is introduced based on noise correction and exponential (NCEXP) fitting. The feasibility of NCEXP method for quantifying dose absorption in LD gels is evaluated. MATERIALS AND METHODS: Sensitivity, dose resolution, detectable dynamic range, and correlation of the calibration curve for both UD and LD gel dosimeters are the parameters, which we analyze to investigate the consequences of new method. Results of NCEXP method are compared to maximum likelihood estimation of rician distribution (MLE-R) and variable echo number (VAREC) quantification methods. RESULTS: Dose response of LD gel dosimeter shows wider detectable dynamic range as compared to UD gel. Using NCEXP method for both LD and UD dosimeter gels, a more sensitive calibration curve with a superior dose resolution is obtained. The advantage of new quantification method is more significant for LD dosimeter gel analysis, where SNR decreases as a result of higher absorbed doses (≥10 Gy). Despite the inverse effect of the VAREC method on detectable dose range of UD gel, no specific changes are observed in dynamic dose range of LD gel dosimeter with different quantification methods. The correlations obtained with different methods were approximately of the same order for UD and LD gels. CONCLUSION: NCEXP method seems to be more effective than the MLE-R and VAREC methods for quantification of LD dosimeter gel, especially where high-dose absorption and steep-dose gradients exist such as those in intensity-modulated radiation therapy and stereotactic radiosurgery.

Prenatal transplantation of epidermal neural crest stem cells in malformation of cortical development mouse model.

Prenatal interventions may offer an immense opportunity in therapeutic protocols of malformations of cortical development (MCD). Epidermal neural crest stem cells (EPI-NCSCs) of the hair follicle bulge exhibit features of both embryonic and adult stem cells; these cells maintain their neurologic differentiation capability because of their neural crest origin. However, it is unknown if prenatal use of EPI-NCSCs could be beneficial in targeting methylazoxymethanol (MAM)-induced MCD, which further addressed in the present work. EPI-NCSCs were prenatally infused to the MAM-exposed mice. Thicknesses of various cerebral cortex areas as well as corpus callosum was measured; there were markedly decrease in MAM group (p < .001 vs. untreated), but a significant increase in EPI-NCSC group (p < .05 vs. MAM), except for corpus callosum. Real-time PCR analysis showed high expressions for absent, small, or homeotic 2-like protein, nestin, doublecortin (DCX), neuronal specific nuclei protein (NeuN), and glial fibrillary acidic protein (GFAP) in MAM group (p < .001 vs. untreated), except for G-protein-coupled C-X-C chemokine receptor type 4 (CXCR4) and CXC motif ligand 12 (CXCL12), whereas there were low expressions in EPI-NCSCs group (p < .01 vs. MAM). Immunohistochemistry of NeuN, GFAP, ionized calcium-binding adapter molecule (Iba1), and oligodendrocyte lineage transcription factor 2 (Olig2) was also revealed the same pattern as real-time PCR (p < .001 MAM vs. untreated, and p < .05 EPI-NCSCs vs. MAM). Our findings suggest prenatal use of EPI-NCSCs as a possible candidate for cell-based therapy of cortical injury through affecting neural markers and their relationship with glial.

Evaluation of adaptive PARAFAC alogorithms for tracking of simulated moving brain sources.

In this paper, we proposed an online 2D localization method for tracking of dynamic moving brain sources. For this purpose, we used an adaptive version of PARAllel FACtor (PARAFAC) analysis for factorization of electroencephalographic (EEG) signals. We utilized Boundary Element Method (BEM) with four layers to solve the forward problem for the simulated EEG signals caused by two moving dipoles within the brain. Then, we created an appropriate tensor built by second order statistics of EEG signals. We adopted an online method to brain source localization called the Recursive Least Squares Tracking (RLST) as an adaptive PARAFAC algorithm with two windowing schemes. Finally, we evaluated the performance of the method applied to EEG signals.

A statistical approach in human brain connectome of Parkinson Disease in elderly people using Network Based Statistics.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder assumed to involve different areas of CNS and PNS. Thus, Diffusion Tensor Imaging (DTI) is used to examine the areas engaged in PD neurodegeneration. In the present study, we computed average tract length and fiber volume as a measure of white matter integrity and adopted Network Based Statistics (NBS) to conduct group analyses between age- and gender-matched PD patients and healthy control connectivity matrices. NBS is a powerful statistical tool that utilizes the presence of every link in connectivity matrices and controls family wise error rates (in weak sense). The major regions with significantly reduced interconnecting fiber volume or average tract length were cingulum, temporal lobe, frontal lobe, parahippocampus, hippocampus, olfactory lobe, and occipital lobe.