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Background Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. New Method We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during “resting-state”, visual oddball and semantic decision paradigms. Results The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. Comparison with existing method(s) Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. Conclusions This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

Electroencephalography (EEG) oscillations reflect the superposition of different cortical sources with potentially different frequencies. Various blind source separation (BSS) approaches have been developed and implemented in order to decompose these oscillations, and a subset of approaches have been developed for decomposition of multi-subject data. Group independent component analysis (Group ICA) is one such approach, revealing spatiospectral maps at the group level with distinct frequency and spatial characteristics. The reproducibility of these distinct maps across subjects and paradigms is relatively unexplored domain, and the topic of the present study. To address this, we conducted separate group ICA decompositions of EEG spatiospectral patterns on data collected during three different paradigms or tasks (resting-state, semantic decision task and visual oddball task). K-means clustering analysis of back-reconstructed individual subject maps demonstrates that fourteen different independent spatiospectral maps are present across the different paradigms/tasks, i.e. they are generally stable.

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.

Objectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. Results: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. Conclusions: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome. (JINS, 2016, 22, 105–119)

While previous studies separately demonstrate EEG spectral modulations during speech preparation and ERP responses to the listened speech, it is unclear whether these responses are related on a trial-by-trial basis between a speaker and listener. In order to determine whether these responses are related in real-time, Electroencephalography (EEG) responses were measured simultaneously within a speaker and listener using a 24 electrode Mobile EEG system (18 participants; 9 pairs) during a sentence completion task. Each trial consisted of a sentence prompt with an incomplete ending (e.g. “I took my dog for a ____”). The speaker was instructed to fill in the ending with something expected (e.g. “walk”) (40 trials) or unexpected (e.g. “drink”) (40 trials). The other participant listened to the speaker throughout the block. We found that lower alpha band activity was reduced when individuals prepared unexpected sentence endings compared to expected sentence endings. Greater reductions in the speaker’s lower alpha activity during response preparation were correlated with a more negative N400 response in the listener to the unexpected word. These findings demonstrate that alpha suppression and the N400 ERP effect are present within a hyperscanning context and they are correlated between the speaker and listener during sentence completion.

Within the last decade, various blind source separation algorithms (BSS) isolating distinct EEG oscillations were derived and implemented. Group Independent Component Analysis (group-ICA) is a promising tool for decomposing spatiospectral EEG maps across multiple subjects. However, researchers are faced with many preprocessing options prior to performing group-ICA, which potentially influences the results. To examine the influence of preprocessing steps, within this article we compare results derived from group-ICA using the absolute power of spatiospectral maps and the relative power of spatiospectral maps. Within a previous study, we used K-means clustering to demonstrate group-ICA of absolute power spatiospectral maps generates sources which are stable across different paradigms (i.e. resting-state, semantic decision, visual oddball) Within the current study, we compare these maps with those obtained using relative power of spatiospectral maps as input to group-ICA. We find that relative EEG power contains 10 stable spatiospectral patterns which were similar to those observed using absolute power as inputs. Interestingly, relative power revealed two γ-band (20–40 Hz) patterns which were present across 3 paradigms, but not present using absolute power. This finding suggests that relative power potentially emphasizes low energy signals which are obscured by the high energy low frequency which dominates absolute power measures.

Background The symptoms that contribute to the clinical diagnosis of depression likely emerge from, or are related to, underlying cognitive deficits. To understand this relationship further, we examined the relationship between self-reported somatic and cognitive-affective Beck׳s Depression Inventory-II (BDI-II) symptoms and aspects of cognitive control reflected in error event-related potential (ERP) responses. Methods Task and assessment data were analyzed within 51 individuals. The group contained a broad distribution of depressive symptoms, as assessed by BDI-II scores. ERPs were collected following error responses within a go/no-go task. Individual error ERP amplitudes were estimated by conducting group independent component analysis (ICA) on the electroencephalographic (EEG) time series and analyzing the individual reconstructed source epochs. Source error amplitudes were correlated with the subset of BDI-II scores representing somatic and cognitive-affective symptoms. Results We demonstrate a negative relationship between somatic depression symptoms (i.e. fatigue or loss of energy) (after regressing out cognitive-affective scores, age and IQ) and the central–parietal ERP response that peaks at 359 ms. The peak amplitudes within this ERP response were not significantly related to cognitive-affective symptom severity (after regressing out the somatic symptom scores, age, and IQ). Limitations These findings were obtained within a population of female adults from a maximum-security correctional facility. Thus, additional research is required to verify that they generalize to the broad population. Conclusions These results suggest that individuals with greater somatic depression symptoms demonstrate a reduced awareness of behavioral errors, and help clarify the relationship between clinical measures of self-reported depression symptoms and cognitive control.

Interacting with the environment requires the ability to flexibly direct attention to relevant features. We examined the degree to which individuals attend to visual features within and across Detection, Fine Discrimination, and Coarse Discrimination tasks. Electroencephalographic (EEG) responses were measured to an unattended peripheral flickering (4 or 6 Hz) grating while individuals ( n = 33) attended to orientations that were offset by 0°, 10°, 20°, 30°, 40°, and 90° from the orientation of the unattended flicker. These unattended responses may be sensitive to attentional gain at the attended spatial location, since attention to features enhances early visual responses throughout the visual field. We found no significant differences in tuning curves across the three tasks in part due to individual differences in strategies. We sought to characterize individual attention strategies using hierarchical Bayesian modeling, which grouped individuals into families of curves that reflect attention to the physical target orientation (“on-channel”) or away from the target orientation (“off-channel”) or a uniform distribution of attention. The different curves were related to behavioral performance; individuals with “on-channel” curves had lower thresholds than individuals with uniform curves. Individuals with “off-channel” curves during Fine Discrimination additionally had lower thresholds than those assigned to uniform curves, highlighting the perceptual benefits of attending away from the physical target orientation during fine discriminations. Finally, we showed that a subset of individuals with optimal curves (“on-channel”) during Detection also demonstrated optimal curves (“off-channel”) during Fine Discrimination, indicating that a subset of individuals can modulate tuning optimally for detection and discrimination.

The topological architecture of brain connectivity has been well characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.

Music is ubiquitous throughout recent human culture, and many individual’s have an innate ability to appreciate and understand music. Our appreciation of music likely emerges from the brain’s ability to process a series of repeated complex acoustic patterns. In order to understand these processes further, cortical responses were measured to a series of guitar notes presented with a musical pattern or without a pattern. ERP responses to individual notes were measured using a 24 electrode Bluetooth mobile EEG system (Smarting mBrainTrain) while 13 healthy non-musicians listened to structured (i.e. within musical keys and with repetition) or random sequences of guitar notes for 10 minutes each. We demonstrate an increased amplitude to the ERP that appears ~200 ms to notes presented within the musical sequence. This amplitude difference between random notes and patterned notes likely reflects individual’s cortical sensitivity to guitar note patterns. These amplitudes were compared to ERP responses to a rare note embedded within a stream of frequent notes to determine whether the sensitivity to complex musical structure overlaps with the sensitivity to simple irregularities reflected in traditional auditory oddball experiments. Response amplitudes to the negative peak at ~175ms are statistically correlated with the mismatch negativity (MMN) response measured to a rare note presented among a series of frequent notes (i.e. in a traditional oddball sequence), but responses to the subsequent positive peak at ~200 do not show a statistical relationship with the P300 response. Thus, the sensitivity to musical structure identified to 4 Hz note patterns appears somewhat distinct from the sensitivity to statistical regularities reflected in the traditional “auditory oddball” sequence. Overall, we suggest that this is a promising approach to examine individual's sensitivity to complex acoustic patterns, which may overlap with higher level cognitive processes, including language.

The ability to remain vigilant over long periods of time is critical for many everyday tasks, but controlled studies of visual sustained attention show that performance declines over time when observers are required to respond to rare stimulus events (targets) occurring in a sequence of standard stimulus events (non-targets). When target discrimination is perceptually difficult, this vigilance decrement manifests as a decline in perceptual sensitivity. We examined whether sudden-onset stimuli could act as exogenous attentional cues to improve sensitivity during a traditional sustained attention task. Sudden-onset cues presented immediately before each stimulus attenuated the sensitivity decrement, but only when event timing (the inter-event interval) was constant. When event timing was variable, exogenous cues increased overall sensitivity but did not prevent performance decline. Finally, independent of the effects of sudden onsets, a constant inter-event interval improved vigilance performance. Our results demonstrate that exogenous attention enhances perceptual sensitivity during vigilance performance, but that this effect is dependent on observers being able to predict the timing of stimulus events. Such a result indicates a strong interaction between endogenous and exogenous attention during vigilance. We relate our findings to a resource model of vigilance, as well as to theories of endogenous and exogenous attention over short time periods.

Recent studies describing the seemingly contradictory actions of estrogens in ischemic stroke injury have led us to reevaluate the circumstances under which estrogen therapy (ET) provides benefits against cerebral stroke and decipher its mechanisms of action. One prominent feature that follows stroke injury is massive central and peripheral inflammatory responses. Evidence now suggests that postischemic inflammatory responses strongly contribute to the extent of brain injury, and 17β-estradiol (E 2 ) may protect the ischemic brain by exerting antiinflammatory actions. In an attempt to explain recently reported dichotomous effects of E 2 in stroke injury, we tested the hypothesis that an extended period of hypoestrogenicity both prevents E 2 from protecting the brain against ischemia and simultaneously suppresses its antiinflammatory actions. We report that E 2 exerts profound neuroprotective action when administered immediately upon ovariectomy, but not when administered after 10 weeks of hypoestrogenicity. Consistently, E 2 treatment given immediately at the time of ovariectomy attenuated central and peripheral production of proinflammatory cytokines after ischemic stroke. In contrast, E 2 did not suppress production of proinflammatory molecules when it was administered after 10 weeks postovariectomy. These results demonstrate that a prolonged period of hypoestrogenicity disrupts both neuroprotective and antiinflammatory actions of E 2 . Our findings may help to explain the results of the Women's Health initiative that reported no beneficial effect of ET against stroke because the majority of the subjects initiated ET after an extended period of hypoestrogenicity.

Meditation training has been shown to enhance attention and improve emotion regulation. However, the brain processes associated with such training are poorly understood and a computational modeling framework is lacking. Modeling approaches that can realistically simulate neurophysiological data while conforming to basic anatomical and physiological constraints can provide a unique opportunity to generate concrete and testable hypotheses about the mechanisms supporting complex cognitive tasks such as meditation. Here we applied the mean-field computational modeling approach using the scalp-recorded electroencephalogram (EEG) collected at three assessment points from meditating participants during two separate 3-month-long shamatha meditation retreats. We modeled cortical, corticothalamic, and intrathalamic interactions to generate a simulation of EEG signals recorded across the scalp. We also present two novel extensions to the mean-field approach that allow for: (a) non-parametric analysis of changes in model parameter values across all channels and assessments; and (b) examination of variation in modeled thalamic reticular nucleus (TRN) connectivity over the retreat period. After successfully fitting whole-brain EEG data across three assessment points within each retreat, two model parameters were found to replicably change across both meditation retreats. First, after training, we observed an increased temporal delay between modeled cortical and thalamic cells. This increase provides a putative neural mechanism for a previously observed reduction in individual alpha frequency in these same participants. Second, we found decreased inhibitory connection strength between the TRN and secondary relay nuclei (SRN) of the modeled thalamus after training. This reduction in inhibitory strength was found to be associated with increased dynamical stability of the model. Altogether, this paper presents the first computational approach, taking core aspects of physiology and anatomy into account, to formally model brain processes associated with intensive meditation training. The observed changes in model parameters inform theoretical accounts of attention training through meditation, and may motivate future study on the use of meditation in a variety of clinical populations.

Background Individuals with schizophrenia demonstrate deficits in context processing. These deficits can be characterized by examining the influence of auditory context on ERP responses to rare target tones. Previous studies demonstrate that target ERP deficits in schizophrenia depend on the number of non-targets that precede the target ERP. Our goal was to extend these findings by examining whether patients with schizophrenia demonstrate a reduced sensitivity to subtle differences in the auditory context preceding rare target stimuli, as quantified by Itti and Baldi's Bayesian prediction error model. Methods Cortical responses to auditory oddball tones were measured within 59 individuals with schizophrenia (SZ) and 59 controls (HC). Individual trial amplitudes were estimated by conducting group ICA on the EEG time series and analyzing the reconstructed individual temporal sources. We quantified the auditory context of target tones using the Bayesian prediction error model and determined whether ERP amplitudes to tones were sensitive to this measure of context, or the number of preceding non-targets directly, within HC and SZ. Results Individuals with schizophrenia show a significant reduction in ERP response amplitudes to targets approximately 244–412 ms following target onsets. Individual amplitudes within this window showed significantly greater sensitivity to the modeled prediction error within the controls than in individuals with schizophrenia. These differences approached significance when examining differences in amplitudes as a function of the number of preceding non-targets. Conclusions These findings further clarify differences in HC and SZ with regard to their attentional and perceptual sensitivity to subtle environmental regularities.

Multimodal brain imaging data fusion is a scientifically interesting and clinically important topic; however, there is relatively little work on N-way data fusion. In this paper, we applied multi-set canonical correlation analysis (MCCA) to combine data of resting state fMRI, EEG and sMRI, in order to elucidate the abnormalities that underlie schizophrenia patients and also covary across multiple modalities. We also tested whether the identified group-discriminative components can be used for feature selection in group classification. MCCA is demonstrated to be an effective feature selection technique, especially in multimodal fusion. We also proposed an ensemble feature selection scheme by combining two sample t-test, MCCA and support vector machine with recursive feature elimination (SVM-RFE), resulting in optimal group-discriminating features for each modality. Finally, we compared the classifying power between two groups based on the above selected features via 7 modality-combinations. Results show that the fMRI-sMRI-EEG combination derives the best classification accuracy in training (91%) and predication rate (100%) in testing data, validating the effectiveness and advantages of multimodal fusion in discriminating schizophrenia.

Different imaging modalities are sensitive to different aspects of brain activity, and integrating information from multiple modalities can provide an improved picture of brain dynamics. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are often integrated since they make up for each other’s limitations. FMRI can reveal localized intrinsic networks whose BOLD signals have periods from 100 s to about 10 s. EEG recordings, in contrast, reflect cortical electrical fluctuations with periods up to 20 ms or higher. The following chapter surveys the physiological differences between EEG and fMRI recordings and the implications and results of their integration. EEG-fMRI findings are reviewed in cases where individuals do not participate in an explicit task (e.g. during “rest”). The results are discussed in the context of different methodological approaches to EEG-fMRI integration, including correlation and GLM-based analysis, and ICA decomposition of group EEG-fMRI datasets. The resulting EEG-fMRI networks capture a broader range of brain dynamics compared to EEG or fMRI alone, and can serve as a reference for studies integrating MEG and fMRI.

OBJECTIVE Cognitive perseverations that include worry and rumination over past or future events may prolong cortisol release, which in turn may contribute to predisease pathways and adversely affect physical health. Meditation training may increase self-reported mindfulness, which has been linked to reductions in cognitive perseverations. However, there are no reports that directly link self-reported mindfulness and resting cortisol output. Here, the authors investigate this link. METHODS In an observational study, we measured self-reported mindfulness and p.m. cortisol near the beginning and end of a 3-month meditation retreat (N = 57). RESULTS Mindfulness increased from pre- to post-retreat, F(1, 56) = 36.20, p < .001. Cortisol did not significantly change. However, mindfulness was inversely related to p.m. cortisol at pre-retreat, r(53) = -.31, p < .05, and post-retreat, r(53) = -.30, p < .05, controlling for age and body mass index. Pre to postchange in mindfulness was associated with pre to postchange in p.m. cortisol, β = -.37, t(49) = 2.30, p < .05: Larger increases in mindfulness were associated with decreases in p.m. cortisol, whereas smaller increases (or slight decreases) in mindfulness were associated with an increase in p.m. cortisol. CONCLUSIONS These data suggest a relation between self-reported mindfulness and resting output of the hypothalamic-pituitary-adrenal system. Future work should aim to replicate this finding in a larger cohort and determine stronger inference about causality by using experimental designs that include control-group conditions.

Different imaging modalities capture different aspects of brain activity. Functional magnetic resonance imaging (fMRI) reveals intrinsic networks whose BOLD signals have periods from 100 s (0.01 Hz) to about 10s (0.1 Hz). Electroencephalographic (EEG) recordings, in contrast, commonly reflect cortical electrical fluctuations with periods up to 20 ms (50 Hz) or above. We examined the correspondence between intrinsic fMRI and EEG network activity at rest in order to characterize brain networks both spatially (with fMRI) and spectrally (with EEG). Brain networks were separately identified within the concurrently recorded fMRI and EEG at the aggregate group level with group independent component analysis and the association between spatial fMRI and frequency by spatial EEG sources was examined by deconvolving their component time courses. The two modalities are considered linked if the estimated impulse response function (IRF) is significantly non-zero at biologically plausible delays. We found that negative associations were primarily present within two of five alpha components, which highlights the importance of considering multiple alpha sources in EEG-fMRI. Positive associations were primarily present within the lower (e.g. delta and theta) and higher (e.g. upper beta and lower gamma) spectral regions, sometimes within the same fMRI components. Collectively, the results demonstrate a promising approach to characterize brain networks spatially and spectrally, and reveal that positive and negative associations appear within partially distinct regions of the EEG spectrum.

Attention biases sensory processing toward neurons containing information about behaviorally relevant events. These attentional biases apparently reflect the combined influence of feature enhancement and suppression. We examined the separate influence of enhancement and suppression in visual processing by determining whether responses to an unattended flicker were modulated when the flicker features matched target features at the attended location, competed with those features, or were neutral. We found that suppression primarily modulated parietal networks with a preferred frequency in the lower alpha band (f2 = 8 Hz), and enhancement primarily influenced parietal networks with a preferred frequency in the upper alpha band (f2 = 12 Hz). These responses were coupled with perception, with large responses to the unattended flicker leading to subsequently detected targets when the target features matched the flicker features (i.e., during enhancement). Our results suggest that enhancement and suppression are two distinct processes that work together to shape visual perception.

The capacity to focus one's attention for an extended period of time can be increased through training in contemplative practices. However, the cognitive processes engaged during meditation that support trait changes in cognition are not well characterized. We conducted a longitudinal wait-list controlled study of intensive meditation training. Retreat participants practiced focused attention (FA) meditation techniques for three months during an initial retreat. Wait-list participants later undertook formally identical training during a second retreat. Dense-array scalp-recorded electroencephalogram (EEG) data were collected during 6 min of mindfulness of breathing meditation at three assessment points during each retreat. Second-order blind source separation, along with a novel semi-automatic artifact removal tool (SMART), was used for data preprocessing. We observed replicable reductions in meditative state-related beta-band power bilaterally over anteriocentral and posterior scalp regions. In addition, individual alpha frequency (IAF) decreased across both retreats and in direct relation to the amount of meditative practice. These findings provide evidence for replicable longitudinal changes in brain oscillatory activity during meditation and increase our understanding of the cortical processes engaged during meditation that may support long-term improvements in cognition.

Telomerase activity is a predictor of long-term cellular viability, which decreases with chronic psychological distress (Epel et al., 2004). Buddhist traditions claim that meditation decreases psychological distress and promotes well-being (e.g., Dalai Lama and Cutler, 2009). Therefore, we investigated the effects of a 3-month meditation retreat on telomerase activity and two major contributors to the experience of stress: Perceived Control (associated with decreased stress) and Neuroticism (associated with increased subjective distress). We used mediation models to test whether changes in Perceived Control and Neuroticism explained meditation retreat effects on telomerase activity. In addition, we investigated whether two qualities developed by meditative practice, increased Mindfulness and Purpose in Life, accounted for retreat-related changes in the two stress-related variables and in telomerase activity.Retreat participants (n=30) meditated for ∼6 h daily for 3 months and were compared with a wait-list control group (n=30) matched for age, sex, body mass index, and prior meditation experience. Retreat participants received instruction in concentrative meditation techniques and complementary practices used to cultivate benevolent states of mind (Wallace, 2006). Psychological measures were assessed pre- and post-retreat. Peripheral blood mononuclear cell samples were collected post-retreat for telomerase activity. Because there were clear, a priori hypotheses, 1-tailed significance criteria were used throughout.Telomerase activity was significantly greater in retreat participants than in controls at the end of the retreat (p0.05). Increases in Perceived Control, decreases in Neuroticism, and increases in both Mindfulness and Purpose in Life were greater in the retreat group (p0.01). Mediation analyses indicated that the effect of the retreat on telomerase was mediated by increased Perceived Control and decreased Neuroticism. In turn, changes in Perceived Control and Neuroticism were both partially mediated by increased Mindfulness and Purpose in Life. Additionally, increases in Purpose in Life directly mediated the telomerase group difference, whereas increases in Mindfulness did not.This is the first study to link meditation and positive psychological change with telomerase activity. Although we did not measure baseline telomerase activity, the data suggest that increases in perceived control and decreases in negative affectivity contributed to an increase in telomerase activity, with implications for telomere length and immune cell longevity. Further, Purpose in Life is influenced by meditative practice and directly affects both perceived control and negative emotionality, affecting telomerase activity directly as well as indirectly.

Manish Saggar and Risto Miikkulainen are with the Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA -- Stephen R Aichele, Baljinder K Sahdra, Phillip R Shaver, Emilio Ferrer, and George R Mangun are with the Department of Psychology, University of California-Davis, Davis, CA 95618, USA -- Stephen R Aichele, Tonya L Jacobs, Anthony P Zanesco, David A Bridwell, Katherine A Maclean, Brandon G King, Baljinder K Sahdra, Erika L Rosenberg, George R Mangun, Clifford D Saron are with the Center for Mind and Brain, University of California-Davis, Davis, CA 95618, USA -- David A Bridwell is with the Department of Cognitive Science, Univ. of California-Irvine, Irvine, CA 92697, USA -- B Alan Wallace is with the Santa Barbara Institute for Consciousness Studies, Santa Barbara, CA 93130, USA -- Katherine A Maclean is with the Department of Psychiatry and Behavioral Sciences, JHU School of Medicine, Baltimore, MD 21224, USA

Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

The ability to focus one’s attention underlies success in many everyday tasks, but voluntary attention cannot be sustained for extended periods of time. In the laboratory, sustained-attention failure is manifest as a decline in perceptual sensitivity with increasing time on task, known as the vigilance decrement. We investigated improvements in sustained attention with training (~5 hr/day for 3 months), which consisted of meditation practice that involved sustained selective attention on a chosen stimulus (e.g., the participant’s breath). Participants were randomly assigned either to receive training first ( n = 30) or to serve as waiting-list controls and receive training second ( n = 30). Training produced improvements in visual discrimination that were linked to increases in perceptual sensitivity and improved vigilance during sustained visual attention. Consistent with the resource model of vigilance, these results suggest that perceptual improvements can reduce the resource demand imposed by target discrimination and thus make it easier to sustain voluntary attention.