Brain connectivity Patterns Dissociate action of specific Acupressure Treatments in Fatigued Breast cancer survivors

Funding This work was supported by grants R01 CA151445 and 2UL1 TR000433-06 from the National Institutes of Health. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. We thank the expert assistance by Dr. Bradley Foerster in acquisition of 1H-MRS and fMRI data.Peer reviewedPublisher PD

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain

Learning, Arts, and the Brain: The Dana Consortium Report on Arts and Cognition

Reports findings from multiple neuroscientific studies on the impact of arts training on the enhancement of other cognitive capacities, such as reading acquisition, sequence learning, geometrical reasoning, and memory

Dynamic and Integrative Properties of the Primary Visual Cortex

The ability to derive meaning from complex, ambiguous sensory input requires the integration of information over both space and time, as well as cognitive mechanisms to dynamically shape that integration. We have studied these processes in the primary visual cortex (V1), where neurons have been proposed to integrate visual inputs along a geometric pattern known as the association field (AF). We first used cortical reorganization as a model to investigate the role that a specific network of V1 connections, the long-range horizontal connections, might play in temporal and spatial integration across the AF. When retinal lesions ablate sensory information from portions of the visual field, V1 undergoes a process of reorganization mediated by compensatory changes in the network of horizontal collaterals. The reorganization accompanies the brain’s amazing ability to perceptually “fill-inâ€, or “seeâ€, the lost visual input. We developed a computational model to simulate cortical reorganization and perceptual fill-in mediated by a plexus of horizontal connections that encode the AF. The model reproduces the major features of the perceptual fill-in reported by human subjects with retinal lesions, and it suggests that V1 neurons, empowered by their horizontal connections, underlie both perceptual fill-in and normal integrative mechanisms that are crucial to our visual perception. These results motivated the second prong of our work, which was to experimentally study the normal integration of information in V1. Since psychophysical and physiological studies suggest that spatial interactions in V1 may be under cognitive control, we investigated the integrative properties of V1 neurons under different cognitive states. We performed extracellular recordings from single V1 neurons in macaques that were trained to perform a delayed-match-to-sample contour detection task. We found that the ability of V1 neurons to summate visual inputs from beyond the classical receptive field (cRF) imbues them with selectivity for complex contour shapes, and that neuronal shape selectivity in V1 changed dynamically according to the shapes monkeys were cued to detect. Over the population, V1 encoded subsets of the AF, predicted by the computational model, that shifted as a function of the monkeys’ expectations. These results support the major conclusions of the theoretical work; even more, they reveal a sophisticated mode of form processing, whereby the selectivity of the whole network in V1 is reshaped by cognitive state

A Short Window Granger Causality Approach to Identify Brain Functional Pattern Associated with Changes of Performance Induced by Sleep Deprivation

The comprehensive effect of sleep deprivation on biological and behavioral functions largely remains unknown. There is evidence to support that human sleep must be of sufficient duration and physiological continuity to ensure neurocognitive performance while we are waking. Insufficient sleep would lead to high risk of human-error related to accidents, injuries or even fatal outcomes. However, in modern society, more and more people suffer from sleep deprivation because of the increasing social, academic or occupational demand. It is important to study the effect of sleep deprivation, not only on task performance, but also on neurocognitive functions. Recent research that has explored brain effective connectivity has demonstrated the directed inference interaction among pairs of brain areas, which may bring important insight to understand how brain works to support neurocognitive function. This research aimed to identify the brain effective connectivity pattern associated with changes of a task performance, response time, following sleep deprivation. Experiments were conducted by colleagues at Neuroergonomics Department at Jagiellonian University, Krakow, Poland. Ten healthy young women, with an average age of 23-year-old, performed visual spatial sustained-attention tasks under two conditions: (1) the rest-wakeful (RW) condition, where participants had their usual sleep and (2) the sleep-deprived (SD) condition, where participants had 3 hours less sleep than their usual sleep, for 7 nights (amounting to 21 h of sleep debt). Measures included eye tracking performance and functional magnetic resonance imaging (fMRI). In each condition, each subject*s eye-position was monitored through 13 sessions, each with 46 trials, while fMRI data was recorded. There were two task performance measures, accuracy and response time. Accuracy measured the proportion of correct responses of all trials in each session. Response time measured the average amount of milliseconds until participants gazed at the target stimuli in each session. An experimental session could be treated as a short window. By splitting long trials of fMRI data into consecutive windows, Granger causality was applied based on short trials of fMRI data. This procedure helped to calculate pairwise causal influences with respect to time-varying property in brain causal interaction. Causal influence results were then averaged across sessions to create one matrix for each participant. This matrix was averaged within each condition to formulate a model of brain effective connectivity, which also served as a basis of comparison. In conclusion, significant effect of sleep deprivation was found on response time and brain effective connectivity. In addition, the change of brain effective connectivity after sleep deprivation was linked to the change of response time. First, an analysis of variance (ANOVA) showed significant difference for response time between the RW condition and the SD condition. No significant changes for accuracy were found. A paired t-test showed that response time was significantly shorter in sleep deprivation for the visual spatial sustained-attention task. Second, Granger causality analysis demonstrated a reduction of bidirectional connectivity and an increase of directed influences from low-level brain areas to high-level brain areas after sleep deprivation. This observation suggested that sleep deprivation provoked the effective connectivity engaged in salient stimuli processing, but inhibited the effective connectivity in biasing selection of attention on task and in maintaining self-awareness in day time. Furthermore, in the SD condition, attention at the visual spatial task seemed to be driven by a bottom-up modulation mechanism. Third, a relationship was found between brain effective connectivity with response time. Decreases of Granger causal influences in two directions, from medial frontal lobe to sub cortical gray nuclei and from medial parietal lobe to sub cortical gray nuclei, were associated with shorter response time in the SD condition. Additionally, an increase of Granger causal influence from medial parietal lobe to cerebellum was associated with longer response time in the SD condition

Learning under uncertainty in the young and older human brain: Common and distinct mechanisms of different attentional and intentional systems

The human brain is able to infer the probability of future events by combining information of past observations with current sensory input. Naturally, we are surrounded by more stimuli than we can pay attention to, so selection of relevant input is crucial. The present thesis aimed at identifying common and distinct neural correlates engaged in predictive processing in spatial attention (selection of attended locations) and motor intention (selection of prepared motor responses). Secondly, age-related influences on probabilistic inference in spatial-attention, feature-based attention (selection of attended color) and motor intention, and the impact of task difficulty were considered. Orienting attention during goal-directed behavior can be supported by visual cues, whereas reorienting to unexpected events following misguiding information is linked to behavioral costs and updating of predictions. These processes can be investigated with a cueing paradigm in which differences in reaction time (RT) between valid and invalidly cued trials increase with higher cue validity (%CV) (Posner, 1980). Bayesian models can describe the experience-dependent learning effects of inferring %CV, following novel events (Vossel et al., 2014c; Vossel, Mathys, Stephan & Friston, 2015). The principle aim of the first experiment was to identify and compare the neural correlates involved in inferring probabilities in the spatial attentional and motor intentional domain. Cues indicated either the possible location or prepared the motor response associated with the target. Instead of a fixed probability context, participants were exposed to a volatile environment, in which the validity of the cue information changed unpredictably over time. Combining functional magnetic resonance imaging (fMRI) data with behavioral estimates derived from a Bayesian learning model (Mathys, Daunizeau, Friston & Stephan, 2011) unveiled domain-specific predictability-dependent responses within the right temporoparietal junction (TPJ) for spatial attention and the left angular gyrus (ANG) and anterior cingulate (ACC) in the motor intention task. The blood oxygen level dependent (BOLD) amplitude particularly increased in accord with violations of cue predictability in high cue validity contexts (i.e. when invalid trials were least expected). Valid trials however, induced no (TPJ and ANG) or decreased modulation (ACC). A further aim was to examine possible commonalities in the neural signatures of predictability-dependent processing. Connectivity analysis uncovered common coupling of all three seed regions involved in predictability-dependent processing with the right anterior hippocampus. Since cognitive functions undergo substantial changes in healthy ageing, a second behavioral study was conducted to test whether age differentially influences probabilistic inference in different attentional subsystems, and how task difficulty impacts on learning performance. Thus, following up on the first experiment, similar tasks and the same computational model was used to assess updating behavior in healthy aging. Older and younger adults performed two separate experiments with different difficulty levels. Each experiment included three versions of a cueing task, entailing predictive spatial- (i.e. location), feature- (i.e. color of target) and motor intention cues (i.e. prepare response). Results of the easier version demonstrated a preserved ability of older adults to generate predictions and profit from all cue types. Interestingly, increased task demand uncovered a reduced ability to use motor intention cues to update predictions in older compared to younger adults. In conclusion, the results provide evidence for a segregated functional anatomy of probabilistic inference in spatial attention and motor intention. Nonetheless a common connectivity profile with the hippocampus also points at commonalities. Finally age seems to differentially impact the efficiency of learning behavior in the motor intention system, supporting the notion of independence of the attentional- and intentional subsystems