The organization of behavior: A neuropsychological theory

ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

/pdf/scanning-the-horizon-towards-transparent-and-reproducible-oifq7uu09d.pdf

Scanning the horizon: towards transparent and reproducible neuroimaging research

This article reviews the hypothesis that mind wandering can be integrated into executive models of attention. Evidence suggests that mind wandering shares many similarities with traditional notions of executive control. When mind wandering occurs, the executive components of attention appear to shift away from the primary task, leading to failures in task performance and superficial representations of the external environment. One challenge for incorporating mind wandering into standard executive models is that it often occurs in the absence of explicit intention--a hallmark of controlled processing. However, mind wandering, like other goal-related processes, can be engaged without explicit awareness; thus, mind wandering can be seen as a goal-driven process, albeit one that is not directed toward the primary task.

The restless mind

Preface The Effects of Acute, Chronic & Withdrawal from Chronic Ethanol on Emotional Learning Enhanced Odorant Preference Associative Learning in C. Elegans with Protein Kinase C-Interactive Protein (PKCI)/Hint1 Mutation Brain Derives Neurotrophic Factor (BDNF) & Adult Neurogenesis Neural Mechanisms for Expertise in Mental Imagery Language Processing Across Modalities: Insights from Bimodal Bilingualism Exploiting the Relationship of Natural Language & Computer Science: A Novel Theoretical Approach to Fairness Zero Power & Selflessness: What Meditation & Conscious Perception Have in Common Early Memories of Children & Adults: Implications for Infantile Amnesia APOE ? 4 Allele & Alheimer's Disease: Perspectives on AD Pathogenesis & Therapy The "Egocentric" Americans? Long-Term Memory for Public Events in Five Countries Ecological Validity & the Study of Procedural & Episodic Memory Function in Songbirds Need Dendritic Spines? There's an APP for That Cholesterol & Neuronal Susceptibility to Beta-Amyloid Toxicity Index.

Trends in cognitive sciences

In order to identify the local areas whose activity are most similar with region of interest (ROI), we usually compute the correlation of fMRI data for the brain functional connectivity. The fMRI data is usually noisy, extraction of functional connectivity with the voxel by voxel based method such as Pearson correlation analysis is not robust. Many people smooth the fMRI data before compute the correlation coefficient, which only makes the effect worse, because some useful original information is lost during the smoothing. Here, we analyzed this issue in details and improved the data processing flow to make the result better. Furthermore, a new criterion RV correlation coefficient was introduced in this article to measure the correlation between two local brain regions; This multivariate correlation technique applied the spatiotemporal information within the local regions to measure the similarity of the activity in different brain regions. We compared four different strategies mentioned above to detect the functional connectivity on the simulated and real fMRI data, and the results demonstrated that the RV-coefficient method obtained the best performance.

Direct Measure of Local Region Functional Connectivity by Multivariate Correlation Technique

The widespread statistical parametric mapping standardly performs spatial smoothing of the data with a Gaus-sian kernel (GK) to improve signal to noise ratio and statistical power. However, the best ltering is dependenton the shape of the activation regions, which is irregular in nature and not well matched by a constant GK. As aresult, smoothing the data with a GK will obscure ne-scale patterns of weak eects that contain neuroscienti-cally relevant information. To improve the sensitivity of activation detection, in the presented work, multivariatestatistical technique (PCA) and univariate statistical technique (GLM) were combined together to discover thene-grained activity patterns. The time courses from every local homogenous regions were rst integrated withPCA; then, GLM was used to construct the interests of statistic. The approach has implicitly taken accountof the structures of both BOLD signal and noise existed in local regions. Therefore, it can highlight detailsof die rent regions. Experiments with real fMRI data, demonstrate that proposed technique can dramaticallyincrease the sensitivity of the detectio n of the ne-scale brain activity patterns which contain subtle informationabout the experimental conditions.Keywords: functional Magnetic Resonance Imaging (fMRI),multivariate analysis,univariate analysis,local re-gion analysis,ne-scale activity patterns

Detection of fine-scale activity patterns by integration of information in local regions

ABSTRACT The Fusiform Face Area (FFA) is a widely studied region causally involved in face perception. Even though cognitive neuroscientists have been studying the FFA for over two decades, answers to foundational questions regarding the structure, function, and connectivity of the FFA from a large (N&gt;1000) group of participants are still lacking. To fill this gap, we quantified structural, functional, and connectivity features of fusiform face-selective regions in 1080 participants in the Human Connectome Project (HCP). After manually defining over 4,000 fusiform face-selective regions, we report five main findings. First, 68.94% of hemispheres have two cortically separate regions (pFus-faces/FFA-1 and mFus-faces/FFA-2). Second, in 26.48% of hemispheres, pFus-faces/FFA-1 and mFus-faces/FFA-2 are spatially contiguous, yet functionally and structurally distinct. Third, pFus-faces/FFA-1 is more face-selective than mFus-faces/FFA-2, and the two regions have distinct functional connectivity fingerprints. Fourth, pFus-faces/FFA-1 is cortically thinner and more heavily myelinated than mFus-faces/FFA-2. Fifth, face-selective patterns and functional connectivity fingerprints of each region were more similar in monozygotic than dizygotic twins and more so than structural gradients. As we share our areal definitions with the field, future studies can explore how structural and functional features of these regions will inform theories regarding how visual categories are represented in the brain. 

Functionally and structurally distinct fusiform face area(s) in over 1000 participants

Diffusion Magnetic Resonance Imaging (dMRI) has been extensively applied to depict local intra-voxel microstructure of white matter (WM). These indices (i.e., FA and MD) have been extensively applied as markers for studying WM abnormity related to pathological conditions. The aim of this study is to propose a new metric for measuring inter-voxel coherence of regional diffusivity (i.e., RDD) using mutual information (MI).

Measuring Regional Diffusivity Dependency via Mutual Information

Opposing transcriptomic gradients explain the large-scale organization of cortex. Here, we show that opposing transcriptomic gradients also explain the fine-scale organization of orthogonal maps in human visual areas. We propose a model relating transcriptomics, cell density, and function, which predicts that specific cortical locations within these visual maps are microanatomically distinct and differentially susceptible to genetic mutations. We conclude with histological and translational data that support both predictions.

/pdf/opposing-transcriptomic-gradients-explain-orthogonal-maps-in-2am6vxp7v1.pdf

Opposing transcriptomic gradients explain orthogonal maps in human visual areas

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