Neural Correlates of Familiar and Unfamiliar Action in Infancy

Behavioral evidence shows that experience with an action shapes action perception. Neural mirroring has been suggested as a mechanism underlying this behavioral phenomenon. Suppression of EEG power in the mu frequency band, an index of motor activation, typically reflects neural mirroring. However, contradictory findings exist regarding the association between mu suppression and motor familiarity in infant EEG studies. In this study, we investigated the neural underpinnings reflecting the role of familiarity on action perception. We measured neural processing of familiar (grasp) and novel (tool-use) actions in 9-and-12-month-old infants. Specifically, we measured infants’ distinct motor/visual activity and explored functional connectivity associated with these processes. Mu suppression was stronger for grasping than tool-use, while significant mu and occipital alpha (indexing visual activity) suppression were evident for both actions. Interestingly, selective visual-motor functional connectivity was found during observation of familiar action, a pattern not observed for novel action. Thus, the neural correlates of perception of familiar actions may be best understood in terms of a functional neural network, rather than isolated regional activity.Our findings provide novel insights on analytic approaches for identifying motor-specific neural activity while also considering neural networks involved in observing motorically familiar versus actions.

Serial Recall Order of Category Fluency Words: Exploring Its Neural Underpinnings

Background: Although performance on the category fluency test (CFT) is influenced by many cognitive functions (i.e., including language, executive functioning and speed of processing), item-level scoring methods of CFT performance might be a promising way to capture aspects of semantic memory that are less influenced by intervenient abilities. One such approach is based on the calculation of correlation coefficients that quantify the association between item-level features and the serial order with which words are recalled (SRO).Methods: We explored the neural underpinnings of 10 of these correlational indices in a sample of 40 healthy adults who completed a classic 1-min CFT and an MRI protocol inclusive of T1-weighted (analysed with voxel-based morphometry) and resting-state fMRI sequences for the evaluation of the default-mode network (DMN). Two sets of linear models were defined to test the association between neural maps and each correlational index: a first set in which major demographic and clinical descriptors were controlled for and a second set in which, additionally, all other 9 correlational indices were regressed out.Results: In the analysis of the DMN, ‘SRO-frequency’, ‘SRO-dominance’ and ‘SRO-body-object interaction’ correlational indices were all negatively associated with the anterior portion of the right temporoparietal junction. The ‘SRO-frequency’ correlational index was also negatively associated with the right dorsal anterior cingulate and the ‘SRO-dominance’ correlational index with the right lateral prefrontal cortex. From the second set of models, the ‘SRO-typicality’ correlational index was positively associated with the left entorhinal cortex. No association was found in relation to grey matter maps.Conclusion: The ability to retrieve more difficult words during CFT performance as measured by the correlational indices between SRO and item-level descriptors is associated with DMN expression in regions deputed to attentional reorienting and processing of salience of infrequent stimuli and dominance status. Of all item-level features, typicality appears to be that most closely linked with entorhinal functioning and may thus play a relevant role in assessing its value in testing procedures for early detection of subtle cognitive difficulties in people with suspected Alzheimer’s degeneration. Although exploratory, these findings warrant further investigations in larger cohorts.

Neural Dynamics of Context-Sensitive Adjustments in Cognitive Flexibility

To adaptively interact with the uncertainties of daily life, we must match our level of cognitive flexibility to contextual demands—being more flexible when frequent shifting between different tasks is required and more stable when the current task requires a strong focus of attention. Such cognitive flexibility adjustments in response to changing contextual demands have been observed in cued task-switching paradigms, where the performance cost incurred by switching versus repeating tasks (switch cost) scales inversely with the proportion of switches (PS) within a block of trials. However, the neural underpinnings of these adjustments in cognitive flexibility are not well understood. Here, we recorded 64-channel EEG measures of electrical brain activity as participants switched between letter and digit categorization tasks in varying PS contexts, from which we extracted ERPs elicited by the task cue and alpha power differences during the cue-to-target interval and the resting precue period. The temporal resolution of the EEG allowed us to test whether contextual adjustments in cognitive flexibility are mediated by tonic changes in processing mode or by changes in phasic, task cue-triggered processes. We observed reliable modulation of behavioral switch cost by PS context that was mirrored in both cue-evoked ERP and time–frequency effects but not by blockwide precue EEG changes. These results indicate that different levels of cognitive flexibility are instantiated after the presentation of task cues, rather than by being maintained as a tonic state throughout low- or high-switch contexts.

Blinding in tDCS Studies: Correct End-of-Study Guess Does Not Moderate the Effects on Associative and Working Memory

Transcranial direct current stimulation (tDCS) has become a valuable tool in cognitive neuroscience research as it enables causal inferences about neural underpinnings of cognition. However, studies using tDCS to modulate cognitive functions often yield inconsistent findings. Hence, there is an increasing interest in factors that may moderate the effects, one of which is the participants’ beliefs of the tDCS condition (i.e., real or sham) they received. Namely, whether participants’ correct guessing of sham condition may lead to false-positive tDCS effects. In this study, we aimed to explore if participants’ beliefs about received stimulation type (i.e., the success of blinding) impacted their task performance in tDCS experiments on associative (AM) and working memory (WM). We analyzed data from four within-subject, sham-controlled tDCS memory experiments (N = 83) to check if the correct end-of-study guess of sham condition moderated tDCS effects. We found no evidence that sham guessing moderated post-tDCS memory performance in experiments in which tDCS effects were observed as well as in experiments that showed null effects of tDCS. The results suggest that the correct sham guessing (i.e., placebo-like effect) is unlikely to influence the results in tDCS memory experiments. We discuss the results in light of the growing debate about the relevance and effectiveness of blinding in brain stimulation research.

Testing for implicit emotion regulation in childhood

Although implicit emotion regulation is thought to be critical for psychosocial development and mental wellbeing, few studies have investigated the neural underpinnings of this form of emotion regulation in children. We used a modified emotional Go/NoGo block design fMRI task to explore the neural correlates of implicit emotion regulation and individual differences in a sample of 40 children (50% female, mean age = 8.65 +/- 0.77). Conditions included happy, sad, neutral, and scrambled faces as implicit distractors within the actual Go/NoGo targets. We used a relatively standard preprocessing pipeline via fMRIprep, with T-contrasts for response inhibition and emotional effects, and a nonparametric multiple comparisons procedure, with SnPM, for our group-level analysis. There were multiple significant response inhibition effects, including larger NoGo vs Go activation in the IFG, insula, and MCC/ACC. Valence effects showed significantly greater right putamen activity for the Sad NoGo vs Go contrast and greater bilateral putamen and right pallidum activity for the Happy Go vs Sad Go contrast. These results provide preliminary findings of neural substrates, particularly the putamen, that may be associated with implicit emotion regulation in children.

PHASE: A MATLAB Based Program for the Analysis of Drosophila Phase, Activity, and Sleep under Entrainment

The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis software tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.

Lost in time: rethinking duration estimation outside the brain.

Our perception of the passage of time can suffer from significant distortions as time flies when we are busy and drags when we are bored. A prominent mechanistic model proposes that this perceptual volatility reflects changes in the activity dynamics of distributed neuronal ensembles referred to as population clocks because they encode time. In this framework, time is understood similarly to space (both can be segmented in seconds or centimeters) and duration estimation is primarily internal (the brain tells time). Here, I challenge this framework from the angle of Bergson’s proposal that the inner experience of time is unlike space because it is ever-changing and indivisible (2 successive seconds are not experienced equivalently). Quantifying and communicating this inner experience requires its externalization and immobilization through distance measurements derived from stereotyped movements and spatial metaphors (“short/long” durations; time “flies/drags”), which explains the habit of thinking time like space. In support of Bergson’s proposal, humans and animals heavily rely on movements in a variety of duration estimation tasks and the neural underpinnings of duration estimates overlap those of motor control and spatial navigation. Thus, philosophical and empirical arguments question whether duration estimation is fundamentally internal. Rather than being explained by ad hoc changes in the speed of population clocks, the puzzle of the volatility of time perception might resolve itself by considering that living beings lack the ability to internally measure time, which they compensate through interactions with regularities afforded by the world and symbolic representation drawn from space.

From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we provide recommendations and offer an up-to-date account of how pupil data can be analyzed in hypothesis-testing experiments. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements, visual features (e.g., luminance), and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. We discuss pre-processing steps and contend that the insights gained from pupillometry are constrained by the analysis techniques available. Then, in addition to the traditional approach of analyzing mean pupil size within some epoch of interest, we focus on time series-based analyses, which enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus series, task of interest, behavioral outcome measures, or other participants' pupil traces. Analytic techniques considered include: correlation (auto-, and cross-, reverse-, and inter/intra-subject-), regression (including temporal response functions), classification, dynamic time warping, phase clustering, magnitude squared coherence, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages.

Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study

The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.