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Post‐traumatic stress disorder (PTSD) is a debilitating condition that may develop after experiencing a traumatic event. Combat exposure increases an individual's chance of developing PTSD, making veterans especially susceptible to the disorder. PTSD is characterized by dysregulated emotional networks, memory deficits, and a hyperattentive response to perceived threatening stimuli. Recently, there have been a number of imaging studies that show structural and functional abnormalities associated with PTSD; however, there have been few studies utilizing electroencephalography (EEG). The goal of this study was to characterize **EEG brain dynamics in individuals with PTSD, in order to better understand the neurophysiological underpinnings of some of the salient features of PTSD, such as threat‐processing bias. Veterans of Operation Enduring Freedom/Iraqi Freedom completed an implicit visual threat semantic memory recognition task with stimuli that varied on both category (animals, items, nature, and people) and feature (threatening and nonthreatening) membership, including trauma‐related stimuli. Combat veterans with PTSD had slower reaction times for the threatening stimuli relative to the combat veterans without PTSD (VETC). There were trauma‐specific effects in frontal regions, with theta band EEG power reductions for the threatening combat scenes in the PTSD patients compared to the VETC group. Additionally, a moderate negative correlation was observed between trauma‐specific frontal theta power and hyperarousal symptoms as measured by clinically administered PTSD scale. These findings complement and extend current models of cortico‐limbic dysfunction in PTSD. The moderate negative correlation between frontal theta power and hyperarousal endorsements suggests the utility of these measures as therapeutic markers of symptomatology in PTSD patients.

SummaryRecent advances in Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) allows the imaging and analysis of cellular ultrastructure at nanoscale resolution, but the collection of labels and/or noise-free data sets has several challenges, often immutable. Reasons range from time consuming manual annotations, requiring highly trained specialists, to introducing imaging artifacts from the prolonged scanning during acquisition. We propose a fully unsupervised Noise Reconstruction and Removal Network for denoising scanning electron microscopy images.The architecture, inspired by gated recurrent units, reconstructs and removes the noise by synthesizing the sequential data. At the same time the fully unsupervised training guides the network in distinguishing true signal from noise and gives comparable results to supervised architectures. We demonstrate that this new network specialized on 3D electron microscopy data sets, achieves comparable and even better results than supervised networks.

Introduction Prior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. Methods The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A single‐layer neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. Results The final classifier accurately identified individual task conditions at an overall rate of 92%, estimated by fivefold cross‐validation. The detailed accounting of EEG time–frequency patterns localized to brain regions (i.e., thalamus, pre‐SMA, orbitofrontal cortex, and superior parietal cortex) corroborates and also elaborates upon previous findings from fMRI and EEG studies, and expands the information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. Conclusion This time–frequency‐based classifier extends previous spatiotemporal findings and provides information about neural mechanisms underlying selection and inhibition processes engaged in Go and NoGo trials, respectively. This neural network classifier can be used to assess time–frequency patterns from an individual subject and thus may offer insight into therapeutic uses of neuromodulation in neural dysfunction., We used a relatively large dataset of EEG time–frequency measures for the Go/NoGo task to develop a machine learning based classifier of successful selection and inhibition trials. This neural network classifier accurately identified individual task conditions at an overall rate of 92%, estimated by fivefold cross‐validation. The signatures of the classifier not only replicated the previous main findings of the Neural Hybrid model for selection and inhibition processes in location and time–frequency correlates, but extended these findings with additional signatures not previously isolated.

Chronic verbal retrieval deficits have been noted in traumatic brain injury (TBI), but no U.S. Food and Drug Administration-approved interventions are available. The present study investigated whether 10 sessions of 20 min of 1 mA anodal high-definition transcranial direct current stimulation (HD-tDCS) targeting pre-supplementary motor area/dorsal anterior cingulate cortex (preSMA/dACC) compared with sham HD-tDCS would improve verbal retrieval deficits in TBI patients. Improvements in verbal retrieval processes were observed up to 8 weeks post-treatment. Thus, potential dysfunction to verbal retrieval circuitry in TBI appears amenable to remediation through electromodulation with HD tDCS to the preSMA/dACC. Although further studies clarifying mechanisms by which tDCS brought about these improvements will likely inform refinements in the application of this therapeutic technique, the findings suggest the efficacy of using HD-tDCS to target other systems vulnerable to TBI to improve functioning.

Prior Go/NoGo studies have localized specific regions and EEG spectra for which traditional approaches have distinguished between Go and NoGo conditions. A more detailed characterization of the spatial distribution and timing of the synchronization of frequency bands would contribute substantially to the clarification of neural mechanisms that underlie performance of the Go/NoGo task. The present study used a machine learning approach to learn the features that distinguish between ERSPs involved in selection and inhibition in a Go/NoGo task. A neural network classifier was used to predict task conditions for each subject to characterize ERSPs associated with Go versus NoGo trials. The final model accurately identified individual task conditions at an overall rate of 92%, estimated by 5-fold cross-validation. The detailed accounting of EEG time-frequency patterns localized to brain sources (i.e., thalamus, preSMA, orbitofrontal cortex, and superior parietal cortex) provides elaboration on previous findings from fMRI and EEG studies and more information about EEG power changes in multiple frequency bands (i.e., primarily theta power increase, alpha decreases, and beta increases and decreases) within these regions underlying the selection and inhibition processes engaged in the Go and NoGo trials. This extends previous findings, providing more information about neural mechanisms underlying selection and inhibition processes engaged in the Go and NoGo trials, respectively, and may offer insight into therapeutic uses of neuromodulation in neural dysfunction.

To investigate differences in inhibitory control and processing speed over the life span, participants in 7- to 8-, 10- to 11-, 12- to 15-, 18- to 25-, and 54- to 80-year-old age cohorts completed a Go/No-Go task requiring varying levels of semantic categorization. Discriminant function analysis of correct rejection rates (CRRs), hit rates (HRs), and reaction times (RTs) revealed a function on which CRR loaded positively and RT loaded negatively, across categorization levels. Scores increased from youngest to the younger adult cohort and decreased for the older adult cohort. On a second function, CRR and RT loaded positively and HR loaded negatively across categorization levels. Scores were highest for the older adult cohort and higher for the youngest cohort than for the younger adult cohort. The results suggest change along 2 dimensions might underlie cognitive development: (a) combined increased inhibitory control and processing speed and (b) combined increased speed and decreased biased responding for better inhibitory control. In addition, 2 dimensions might underlie senescence: (a) combined decreased inhibitory control and processing speed and (b) combined decreased speed and increased biased responding for better inhibitory control. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Background The objective was to test whether repetitive Transcranial Magnetic Stimulation (rTMS) just prior to Cognitive Processing Therapy (CPT) would significantly improve the clinical outcome compared to sham rTMS prior to CPT in veterans with PTSD. Methods Veterans 18–60 years of age with current combat-related PTSD symptoms were randomized, using a 1:1 ratio in a parallel design, to active (rTMS+CPT) versus sham (sham+CPT) rTMS just prior to weekly CPT for 12–15 sessions. Blinded raters evaluated veterans at baseline, after the 5th and 9th treatments, and at 1, 3, and 6 months post-treatment. Clinician Administered PTSD Scale (CAPS) was the primary outcome measure with the PTSD Checklist (PCL) as a secondary outcome measure. The TMS coil (active or sham) was positioned over the right dorsolateral prefrontal cortex (110% MT, 1 Hz continuously for 30 min, 1800 pulses/treatment). Results Of the 515 individuals screened for the study, 103 participants were randomized to either active (n = 54) or sham rTMS (n = 49). Sixty-two participants (60%) completed treatment and 59 (57%) completed the 6-month assessment. The rTMS+CPT group showed greater symptom reductions from baseline on both CAPS and PCL across CPT sessions and follow-up assessments, t(df ≥ 325) ≤ −2.01, p ≤ 0.023, one-tailed and t(df ≥ 303) ≤ −2.14, p ≤ 0.017, one-tailed, respectively. Limitations Participants were predominantly male and limited to one era of conflicts as well as those who could safely undergo rTMS. Conclusions The addition of rTMS to CPT compared to sham with CPT produced significantly greater PTSD symptom reduction early in treatment and was sustained up to six months post-treatment.

Veterans with posttraumatic stress disorder (PTSD) underwent a systematic evaluation to determine which factors were associated with the degree of functional status. Demographic information, self-report scales, and symptom ratings performed by trained evaluators were investigated in multiple regression models to determine their contribution to functional status. Ninety-six participants were included in the model assessing degree of functional status. Depressive symptoms, a depressive disorder diagnosis, and to a lesser extent, the Clinician-Administered PTSD Scale were selected in the final model that best predicted the degree of functional status. Depressive symptoms significantly affect the function of veterans with PTSD.

To investigate neural mechanisms that support semantic functions in aging, we recorded scalp EEG during an object retrieval task in 22 younger and 22 older adults. The task required determining if a particular object could be retrieved when two visual words representing object features were presented. Both age groups had comparable accuracy although response times were longer in older adults. In both groups a left fronto-temporal negative potential occurred at around 750ms during object retrieval, consistent with previous findings (Brier, Maguire, Tillman, Hart, & Kraut, 2008). In only older adults, a later positive frontal potential was found peaking between 800 and 1000ms during no retrieval. These findings suggest younger and older adults employ comparable neural mechanisms when features clearly facilitate retrieval of an object memory, but when features yield no retrieval, older adults use additional neural resources to engage in a more effortful and exhaustive search prior to making a decision.

Numerous studies have found evidence for corticolimbic theta band electroencephalographic (EEG) oscillations in the neural processing of visual stimuli perceived as threatening. However, varying temporal and topographical patterns have emerged, possibly due to varying arousal levels of the stimuli. In addition, recent studies suggest neural oscillations in delta, theta, alpha, and beta-band frequencies play a functional role in information processing in the brain. This study implemented a data-driven PCA based analysis investigating the spatiotemporal dynamics of electroencephalographic delta, theta, alpha, and beta-band frequencies during an implicit visual threat processing task. While controlling for the arousal dimension (the intensity of emotional activation), we found several spatial and temporal differences for threatening compared to nonthreatening visual images. We detected an early posterior increase in theta power followed by a later frontal increase in theta power, greatest for the threatening condition. There was also a consistent left lateralized beta desynchronization for the threatening condition. Our results provide support for a dynamic corticolimbic network, with theta and beta band activity indexing processes pivotal in visual threat processing.