http://www.connectlab.wayne.edu/pdfs/binnewijzend-et-al-2011-resting-state-fmri-changes-alzheimers-and-mci.pdf

Resting-state fMRI changes in Alzheimer's disease and mild cognitive impairment.

Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subjects images, parcellation approaches use brain activity (e.g. found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and K-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regards to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.

https://hal.inria.fr/hal-01015172/document

Which fMRI clustering gives good brain parcellations

Studying Brain Organization via Spontaneous fMRI Signal

Using information from research on the neuroplasticity of selective attention and on the central role of successful parenting in child development, we developed and rigorously assessed a family-based training program designed to improve brain systems for selective attention in preschool children. One hundred forty-one lower socioeconomic status preschoolers enrolled in a Head Start program were randomly assigned to the training program, Head Start alone, or an active control group. Electrophysiological measures of children’s brain functions supporting selective attention, standardized measures of cognition, and parent-reported child behaviors all favored children in the treatment program relative to both control groups. Positive changes were also observed in the parents themselves. Effect sizes ranged from one-quarter to half of a standard deviation. These results lend impetus to the further development and broader implementation of evidence-based education programs that target at-risk families.

Family-Based Training Program Improves Brain Function, Cognition, and Behavior in Lower Socioeconomic Status Preschoolers.

Pupillometry has been one of the most widely used response systems in psychophysiology. Changes in pupil size can reflect diverse cognitive and emotional states, ranging from arousal, interest and effort to social decisions, but they are also widely used in clinical practice to assess patients’ brain functioning. As a result, research involving pupil size measurements has been reported in practically all psychology, psychiatry, and psychophysiological research journals, and now it has found its way into the primatology literature as well as into more practical applications, such as using pupil size as a measure of fatigue or a safety index during driving. The different systems used for recording pupil size are almost as variable as its applications, and all yield, as with many measurement techniques, a substantial amount of noise in addition to the real pupillometry data. Before analyzing pupil size, it is therefore of crucial importance first to detect this noise and deal with it appropriately, even prior to (if need be) resampling and baseline-correcting the data. In this article we first provide a short review of the literature on pupil size measurements, then we highlight the most important sources of noise and show how these can be detected. Finally, we provide step-by-step guidelines that will help those interested in pupil size to preprocess their data correctly. These guidelines are accompanied by an open source MATLAB script (available at https://github.com/ElioS-S/pupil-size). Given that pupil diameter is easily measured by standard eyetracking technologies and can provide fundamental insights into cognitive and emotional processes, it is hoped that this article will further motivate scholars from different disciplines to study pupil size.

/pdf/preprocessing-pupil-size-data-guidelines-and-code-2t6e7h7e1p.pdf

Preprocessing pupil size data: Guidelines and code

https://cyberleninka.org/article/n/1450567.pdf

Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?

Although brain plasticity is greatest in the first few years of life, the brain continues to be shaped by experience throughout adulthood. Advances in fMRI have enabled us to examine the plasticity of large-scale networks using blood oxygen level-dependent (BOLD) correlations measured at rest. Resting-state functional connectivity analysis makes it possible to measure task-independent changes in brain function and therefore could provide unique insights into experience-dependent brain plasticity in humans. Here, we evaluate the hypothesis that resting-state functional connectivity reflects the repeated history of co-activation between brain regions. To this end, we review resting-state fMRI studies in the sensory, motor, and cognitive learning literature. This body of research provides evidence that the brain's resting-state functional architecture displays dynamic properties in young adulthood.

/pdf/resting-state-fmri-a-window-into-human-brain-plasticity-287ult72x3.pdf

Resting-State fMRI A Window into Human Brain Plasticity

Attending school is a multifaceted experience. Students are not only exposed to new knowledge but are also immersed in a structured environment in which they need to respond flexibly in accordance with changing task goals, keep relevant information in mind, and constantly tackle novel problems. To quantify the cumulative effect of this experience, we examined retrospectively and prospectively, the relationships between educational attainment and both cognitive performance and learning. We analyzed data from 196,388 subscribers to an online cognitive training program. These subscribers, ages 15–60, had completed eight behavioral assessments of executive functioning and reasoning at least once. Controlling for multiple demographic and engagement variables, we found that higher levels of education predicted better performance across the full age range, and modulated performance in some cognitive domains more than others (e.g., reasoning vs. processing speed). Differences were moderate for Bachelor’s degree vs. High School (d = 0.51), and large between Ph.D. vs. Some High School (d = 0.80). Further, the ages of peak cognitive performance for each educational category closely followed the typical range of ages at graduation. This result is consistent with a cumulative effect of recent educational experiences, as well as a decrement in performance as completion of schooling becomes more distant. To begin to characterize the directionality of the relationship between educational attainment and cognitive performance, we conducted a prospective longitudinal analysis. For a subset of 69,202 subscribers who had completed 100 days of cognitive training, we tested whether the degree of novel learning was associated with their level of education. Higher educational attainment predicted bigger gains, but the differences were small (d = 0.04–0.37). Altogether, these results point to the long-lasting trace of an effect of prior cognitive challenges but suggest that new learning opportunities can reduce performance gaps related to one’s educational history.

/pdf/does-higher-education-hone-cognitive-functioning-and-2mtz4armlh.pdf

Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample

In this chapter, the authors describe research showing that people’s mindsets, or their “implicit theories,” about intelligence significantly impact their academic motivation and performance, sometimes in counter-intuitive ways. In particular, accumulating evidence shows that holding a “growth mindset”—the belief in intelligence as malleable as opposed to fixed—enhances challenge-seeking, interest in learning, effort investment, use of effective strategies, achievement outcomes, and even how the brain functions. They discuss converging evidence for the malleability of intelligence drawn from recent research in cognitive neuroscience that indicates greater brain plasticity and development resulting from learning than previously thought to exist, as shown by the impact of training on executive functions and fluid intelligence. Finally, they discuss how mindsets can be influenced and changed, and the practical implications of this research for educational policy and practice.

Intelligence as a Malleable Construct

Past studies have demonstrated the educational impact of achievement goals, but have not yet captured their effects at a critical learning moment—students’ response to negative feedback and their s...

/pdf/achievement-goal-task-framing-and-fit-with-personal-goals-10nl15b982.pdf

Belén Guerra-Carrillo

Papers

Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?

Resting-State fMRI A Window into Human Brain Plasticity

Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample

Intelligence as a Malleable Construct

Achievement Goal Task Framing and Fit With Personal Goals Modulate the Neurocognitive Response to Corrective Feedback