Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs.

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen's effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.

Neuroimaging of chronotype, sleep quality and daytime sleepiness: Structural T1-weighted magnetic resonance brain imaging data from 136 young adults.

The dataset contains structural T1-weighted magnetic resonance brain imaging data from 136 young individuals (87 females; age range from 18 to 35 years old) along with questionnaire-assessed measurements of trait-like chronotype, sleep quality and daytime sleepiness. The recruitment criteria excluded individuals with self-reported history of psychiatric or neurological conditions and current medication use. All the brain imaging sessions were performed between 5:20 PM and 8:55 PM in order to control the effect of time of day on acquired images. The data is mostly useful to scientists interested in circadian rhythmicity. It can be deployed in large-scale multicenter meta-analyzes investigating the structural brain correlates of chronotypes in humans. Additionally, the data could be of use in investigations into the effects of sleeping habits and latitude on brain anatomy.

Observing changes in human functioning during induced sleep deficiency and recovery periods.

Prolonged periods of sleep restriction seem to be common in the contemporary world. Sleep loss causes perturbations of circadian rhythmicity and degradation of waking alertness as reflected in attention, cognitive efficiency and memory. Understanding whether and how the human brain recovers from chronic sleep loss is important not only from a scientific but also from a public health perspective. In this work we report on behavioral, motor, and neurophysiological correlates of sleep loss in healthy adults in an unprecedented study conducted in natural conditions and comprising 21 consecutive days divided into periods of 4 days of regular life (a baseline), 10 days of chronic partial sleep restriction (30% reduction relative to individual sleep need) and 7 days of recovery. Throughout the whole experiment we continuously measured the spontaneous locomotor activity by means of actigraphy with 1-minute resolution. On a daily basis the subjects were undergoing EEG measurements (64-electrodes with 500 Hz sampling frequency): resting state with eyes open and closed (8 minutes long each) followed by Stroop task lasting 22 minutes. Altogether we analyzed actigraphy (distributions of rest and activity durations), behavioral measures (reaction times and accuracy from Stroop task) and EEG (amplitudes, latencies and scalp maps of event-related potentials from Stroop task and power spectra from resting states). We observed unanimous deterioration in all the measures during sleep restriction. Further results indicate that a week of recovery subsequent to prolonged periods of sleep restriction is insufficient to recover fully. Only one measure (mean reaction time in Stroop task) reverted to baseline values, while the others did not.

The N250 event-related potential as an index of face familiarity: a replication study.

The neural correlates of face individuation-the acquisition of memory representations for novel faces-have been studied only in coarse detail and disregarding individual differences between learners. In their seminal study, Tanaka et al. (Tanaka et al. 2006 J. Cogn. Neurosci. 18, 1488-1497. (doi:10.1162/jocn.2006.18.9.1488)) required the identification of a particular novel face across 70 trials and found that the N250 component in the EEG event-related potentials became more negative from the first to the second half of the experiment, where it reached a similar amplitude as a well-known face. We were unable to directly replicate this finding in our study when we used the original split of trials. However, when we applied a different split of trials we observed very similar changes in N250 amplitude. We conclude that the N250 component is indeed sensitive to the build-up of a robust representation of a face in memory; the time course of this process appears to vary as a function of variables that may be determined in future research.

Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory.

Significant differences exist in human brain functions affected by time of day and by people's diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual's chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.

Implicit induction of emotional control-A comparative fMRI investigation of self-control and reappraisal goal pursuit.

Implicit forms of emotion regulation are of growing interest and have been shown to be efficient in controlling emotional responses despite the fact that they operate without conscious awareness of the ongoing regulatory process and deliberate attempts to influence emotional responding. Although such forms of affective modulation are considered natural and crucial for mental health, their brain mechanisms have hardly been studied until now. Here, we employ a novel procedure and compare directly brain responses to emotional stimuli after implicitly inducing either a self-control goal or a reappraisal goal with a scrambled sentence task. Both induction methods showed robust attenuation of visual, attentional, and emotion-related brain networks. Moreover, after induction of the self-control goal we observed increased activity in the dorsolateral prefrontal cortex (dlPFC) and the right insula, which are involved in top-down modulation of emotional responses. Reappraisal goal induction led to weaker activation in the right dlPFC, which was localized similarly as in the self-control induction task. Our results not only confirm the effectiveness of implicit induction of affective control, but also indicate the important similarities in underlying neural mechanisms that are putatively shared with conscious forms of emotional regulation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Beyond the Low Frequency Fluctuations: Morning and Evening Differences in Human Brain.

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-h-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-h clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. Twenty-nine extreme morning- and 34 evening-type participants underwent two fMRI sessions: about 1 h after wake-up time (morning) and about 10 h after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning (MS) and evening (ES) sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology.

Linking visual gamma to task-related brain networks-a simultaneous EEG-fMRI study.

There is a growing interest in human gamma-band oscillatory activity due to its direct link to neuronal populations, its associations with many cognitive processes, and its positive relationship with fMRI BOLD signal. Visual gamma has been successfully detected using concurrent EEG-fMRI recordings and linked to activity in the visual cortex using voxel-wise regression analysis. As gamma-band oscillations reflect predominantly feedforward projections between brain regions, its inclusion in functional connectivity analysis is highly recommended; however, very few studies have investigated this line of research. In the current study, we aimed to explore this gap by asking which fMRI brain network is related to gamma activity induced by the color discrimination task. Advanced denoising strategies and multitaper spectral decomposition were applied to EEG data to detect gamma oscillations, and group independent component analysis was performed on fMRI data to identify task-related neural networks. Despite using only trials without motor response (50% of the trials), the two neural measures were successfully coupled. One of the six task-related networks, the occipito-parietal network, exhibited significant trial-by-trial covariations with gamma oscillations. In addition to the expected extrastriate visual cortex, the network encompasses extensive brain activations in the precuneus, bilateral intraparietal, and anterior insular cortices. We argue that the visual cortex is the source of gamma, whereas the remaining brain regions exhibit feedforward and feedback connections related to this oscillatory activity. Our findings provide evidence for the electrophysiological basis of the connectivity revealed by BOLD signal and impart novel insights into the neural mechanism of color discrimination.

From fetus to older age: A review of brain metabolic changes across the lifespan.

INTRODUCTION: The knowledge of metabolic changes across the lifespan is poorly understood. Thus we systematically reviewed the available literature to determine the changes in brain biochemical composition from fetus to older age and tried to explain them in the context of neural, cognitive, and behavioural changes. METHODS: The search identified 1262 articles regarding proton magnetic resonance spectroscopy (1H MRS) examinations through December 2017. The following data was extracted: age range of the subjects, number of subjects studied, brain regions studied, MRS sequence used, echo time, MR system, method of statistical analysis, metabolites analyzed, significant differences in metabolites concentrations with age as well as the way of presentation of the results. RESULTS: 82 studies that described brain metabolite changes with age were identified. Reports on metabolic changes related to healthy aging were analyzed and discussed among six basic age groups: fetuses, infants, children, adolescents, adults, and the elderly as well as between groups and during the whole lifetime. DISCUSSION: The results presented in the reviewed papers provide evidence that normal aging is associated with a number of metabolic changes characteristic for every period of life. Therefore, it can be concluded that the age matching is essential for comparative studies of disease states using 1H MRS.

Time is of the Essence: A Review of Electroencephalography (EEG) and Event-Related Brain Potentials (ERPs) in Language Research.

The discovery of electroencephalography (EEG) over a century ago has changed the way we understand brain structure and function, in terms of both clinical and research applications. This paper starts with a short description of EEG and then focuses on the event-related brain potentials (ERPs), and their use in experimental settings. It describes the typical set-up of an ERP experiment. A description of a number of ERP components typically involved in language research is presented. Finally, the advantages and disadvantages of using ERPs in language research are discussed. EEG has an extensive use in today's world, including medical, psychology, or linguistic research. The excellent temporal resolution of EEG information allows one to track a brain response in milliseconds and therefore makes it uniquely suited to research concerning language processing.