Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls.
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ABSTRACT:
SUBMITTER: Rahmani B
PROVIDER: S-EPMC6029761 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path-length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd-ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase-locking values (functional). Both groups showed a significant electroencephalographic task-related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task-related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task-related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task-related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients.
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Project description:BACKGROUND:Facial emotion recognition (FER) is essential to guide social functioning and behaviour for interpersonal communication. FER may be altered in severe mental illness such as in psychosis and in borderline personality disorder patients. However, it is unclear if these FER alterations are specifically related to psychosis. Awareness of FER alterations may be useful in clinical settings to improve treatment strategies. The aim of our study was to examine FER in patients with severe mental disorder and their relation with psychotic symptomatology. MATERIALS AND METHODS:Socio-demographic and clinical variables were collected. Alterations on emotion recognition were assessed in 3 groups: patients with first episode psychosis (FEP) (n = 64), borderline personality patients (BPD) (n = 37) and healthy controls (n = 137), using the Degraded Facial Affect Recognition Task. The Positive and Negative Syndrome Scale, Structured Interview for Schizotypy Revised and Community Assessment of Psychic Experiences scales were used to assess positive psychotic symptoms. WAIS III subtests were used to assess IQ. RESULTS:Kruskal-Wallis analysis showed a significant difference between groups on the FER of neutral faces score between FEP, BPD patients and controls and between FEP patients and controls in angry face recognition. No significant differences were found between groups in the fear or happy conditions. There was a significant difference between groups in the attribution of negative emotion to happy faces. BPD and FEP groups had a much higher tendency to recognize happy faces as negatives. There was no association with the different symptom domains in either group. CONCLUSIONS:FEP and BPD patients have problems in recognizing neutral faces more frequently than controls. Moreover, patients tend to over-report negative emotions in recognition of happy faces. Although no relation between psychotic symptoms and FER alterations was found, these deficits could contribute to a patient's misinterpretations in daily life.
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Project description:Variability of movement reflects important information for the maintenance of the health of the system. For pathological populations, changes in variability during gait signal the presence of abnormal motor control strategies. For persons with multiple sclerosis (PwMS), extensive gait problems have been reported including changes in gait variability. While previous studies have focused on footfall variability, the present study used accelerometers on the trunk to measure variability during walking. Thus, the purpose of this study was to examine the variability of the acceleration pattern of the upper and lower trunk in PwMS compared to healthy controls. We extracted linear and nonlinear measures of gait variability from 30 s of steady state walking for 15 PwMS and 15 age-matched healthy controls. PwMS had altered variability compared to controls with greater Lyapunov exponent in the ML (p < 0.001) and AP (p < 0.001) directions, and greater frequency dispersion in the ML direction (p = 0.034). PwMS also demonstrated greater mean velocity in the ML direction (p = 0.045) and lower root mean square of acceleration in the AP direction (p = 0.040). These findings indicate that PwMS have altered structure of variability of the trunk during gait compared to healthy controls and agree with previous findings related to changes in gait variability in PwMS.
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Project description:Sleep disturbances are common complaints in patients with post-traumatic stress disorder (PTSD). To date, however, objective markers of PTSD during sleep remain elusive. Sleep spindles are distinctive bursts of brain oscillatory activity during non-rapid eye movement (NREM) sleep and have been implicated in sleep protection and sleep-dependent memory processes. In healthy sleep, spindles observed in electroencephalogram (EEG) data are highly synchronized across different regions of the scalp. Here, we aimed to investigate whether the spatiotemporal synchronization patterns between EEG channels during sleep spindles, as quantified by the phase-locking value (PLV) and the mean phase difference (MPD), are altered in PTSD. Using high-density (64-channel) EEG data recorded from 78 combat-exposed Veteran men (31 with PTSD and 47 without PTSD) during two consecutive nights of sleep, we examined group differences in the PLV and MPD for slow (10-13 Hz) and fast (13-16 Hz) spindles separately. To evaluate the reproducibility of our findings, we set apart the first 47 consecutive participants (18 with PTSD) for the initial discovery and reserved the remaining 31 participants (13 with PTSD) for replication analysis. In the discovery analysis, compared to the non-PTSD group, the PTSD group showed smaller MPDs during slow spindles between the frontal and centro-parietal channel pairs on both nights. We obtained reproducible results in the replication analysis in terms of statistical significance and effect size. The PLVs during slow or fast spindles did not significantly differ between groups. The reduced inter-channel phase difference during slow spindles in PTSD may reflect pathological changes in the underlying thalamocortical circuits. This novel finding, if independently validated, may prove useful in developing sleep-focused PTSD diagnostics and interventions.
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Project description:BackgroundWomen with FM have a reduced ability to perform two simultaneous tasks. However, the impact of dual task (DT) on the neurophysiological response of women with FM has not been studied.ObjectiveTo explore both the neurophysiological response and physical performance of women with FM and healthy controls while performing a DT (motor-cognitive).DesignCross-sectional study.MethodsA total of 17 women with FM and 19 age- and sex-matched healthy controls (1:1 ratio) were recruited. The electroencephalographic (EEG) activity was recorded while participants performed two simultaneous tasks: a motor (30 seconds arm-curl test) and a cognitive (remembering three unrelated words). Theta (4-7 Hz), alpha (8-12 Hz), and beta (13-30) frequency bands were analyzed by using EEGLAB.ResultsSignificant differences were obtained in the healthy control group between single task (ST) and DT in the theta, alpha, and beta frequency bands (p-value < 0.05). Neurophysiological differences between ST and DT were not found in women with FM. In addition, between-group differences were found in the alpha and beta frequency bands between healthy and FM groups, with lower values of beta and alpha in the FM group. Therefore, significant group∗condition interactions were detected in the alpha and beta frequency bands. Regarding physical condition performance, between groups, analyses showed that women with FM obtained significantly worse results in the arm curl test than healthy controls, in both ST and DT.ConclusionWomen with FM showed the same electrical brain activity pattern during ST and DT conditions, whereas healthy controls seem to adapt their brain activity to task commitment. This is the first study that investigates the neurophysiological response of women with FM while simultaneously performing a motor and a cognitive task.
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Project description:The Hurst effect plays an important role in many areas such as physics, climate and finance. It describes the anomalous growth of range and constrains the behavior and predictability of these systems. The Hurst effect is frequently taken to be synonymous with Long-Range Dependence (LRD) and is typically assumed to be produced by a stationary stochastic process which has infinite memory. However, infinite memory appears to be at odds with the Markovian nature of most physical laws while the stationarity assumption lacks robustness. Here we use Lorenz's paradigmatic chaotic model to show that regime behavior can also cause the Hurst effect. By giving an alternative, parsimonious, explanation using nonstationary Markovian dynamics, our results question the common belief that the Hurst effect necessarily implies a stationary infinite memory process. We also demonstrate that our results can explain atmospheric variability without the infinite memory previously thought necessary and are consistent with climate model simulations.
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Project description:Sleep disturbances are prevalent in posttraumatic stress disorder (PTSD) and are associated with a number of adverse health consequences. Few studies have used comprehensive assessment methods to characterize sleep in Operation Iraqi Freedom/Operation Enduring Freedom/Operation New Dawn (OEF/OIF/OND) veterans with PTSD. OEF/OIF/OND veterans with PTSD and sleep disturbance (n = 45) were compared to patients with primary insomnia (n = 25) and healthy control subjects (n = 27). Participants were assessed using questionnaire-based measures as well as daily subjective and objective measures of sleep. The 3 groups were compared with regard to (a) group means, (b) intraindividual (i.e., night-to-night) variability of sleep, and (c) interindividual (i.e., within-group) variability of sleep. In terms of group means, only objective sleep efficiency was significantly worse with PTSD than with primary insomnia (d = 0.54). Those with PTSD differed from those with primary insomnia on measures of intraindividual as well as interindividual variability (d = 0.48-0.73). These results suggested sleep symptoms in OEF/OIF/OND veterans with PTSD are more variable across nights and less consistent across patients relative to sleep symptoms in insomnia patients without PTSD. These findings have implications for research, as well as for personalizing treatment for individuals with PTSD.
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Project description:Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection. Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups. Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained. Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region. Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.
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Project description:To investigate gene expression signatures in PTSD, we used microarray and performed blood-based transcriptome profiling. Our analyses identified key genes and molecular pathways that can be centrally involved in the etiology of PTSD.
2024-01-19 | GSE199841 | GEO
Project description:BackgroundIt has been established that the overall performance of associative memory was disproportionately impaired in contrast to item memory in aMCI (Amnestic mild cognitive impairment) patients, but little is known about the specific aspects of the memory process that show differences between aMCI and healthy controls. By comparing an item-item associative learning test with an individual item learning test, the present study investigated whether the rate of learning was slower in associative memory than in item memory in aMCI. Furthermore, we examined whether deficits in intertrial acquisition and consolidation contributed to the potential disproportionate impairments in the learning rate of associative memory for aMCI patients. In addition, we further explored whether the aMCI-discriminative power of the associative memory test increases more than that of the item memory test when the number of learning-test trials increases.MethodsA group of 40 aMCI patients and 40 matched control participants were administered a standardized item memory test (Auditory Verbal Learning Test, AVLT) and a standardized associative memory test (Paired Associative Learning Test, PALT), as well as other neuropsychological tests and clinical assessments.ResultsThe results indicated that the learning rate deficits in aMCI patients were more obvious for associative memory than for item memory and that the deficits resulted from impairments in both intertrial acquisition and consolidation. In addition, the receiver operating characteristic curve and logistical regression analysis revealed that the discriminative power of the associative memory test for aMCI was larger than that of the item memory test, especially with more than one learning-test trials.ConclusionsDue to more deficits in learning rate of associative memory than that of item memory, the discriminative power for aMCI tended to be larger in associative memory than in item memory when the number of learning-test trials increased. It is suggested that associative memory tests with multiple trials may be particularly useful for early detection of aMCI.
| S-EPMC3751153 | biostudies-literature