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      Differences in Brain Hemodynamics in Response to Achromatic and Chromatic Cards of the Rorschach

      Rorschachiana
      Hogrefe Publishing

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          Spatial registration and normalization of images

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            Episodic and declarative memory: role of the hippocampus.

            The fact that medial temporal lobe structures, including the hippocampus, are critical for declarative memory is firmly established by now. The understanding of the role that these structures play in declarative memory, however, despite great efforts spent in the quest, has eluded investigators so far. Given the existing scenario, novel ideas that hold the promise of clarifying matters should be eagerly sought. One such idea was recently proposed by Vargha-Khadem and her colleagues (Science 1997; 277:376-380) on the basis of their study of three young people suffering from anterograde amnesia caused by early-onset hippocampal pathology. The idea is that the hippocampus is necessary for remembering ongoing life's experiences (episodic memory), but not necessary for the acquisition of factual knowledge (semantic memory). We discuss the reasons why this novel proposal makes good sense and why it and its ramifications should be vigorously pursued. We review and compare declarative and episodic theories of amnesia, and argue that the findings reported by Vargha-Khadem and her colleagues fit well into an episodic theory that retains components already publicized, and adds new ones suggested by the Vargha-Khadem et al. study. Existing components of this theory include the idea that acquisition of factual knowledge can occur independently of episodic memory, and the idea that in anterograde amnesia it is quite possible for episodic memory to be more severely impaired than semantic memory. We suggest a realignment of organization of memory such that declarative memory is defined in terms of features and properties that are common to both episodic and semantic memory. The organization of memory thus modified gives greater precision to the Vargha-Khadem et al. neuroanatomical model in which declarative memory depends on perihippocampal cortical regions but not on the hippocampus, whereas episodic memory, which is separate from declarative memory, depends on the hippocampus.
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              Detecting activations in PET and fMRI: levels of inference and power.

              This paper is about detecting activations in statistical parametric maps and considers the relative sensitivity of a nested hierarchy of tests that we have framed in terms of the level of inference (voxel level, cluster level, and set level). These tests are based on the probability of obtaining c, or more, clusters with k, or more, voxels, above a threshold u. This probability has a reasonably simple form and is derived using distributional approximations from the theory of Gaussian fields. The most important contribution of this work is the notion of set-level inference. Set-level inference refers to the statistical inference that the number of clusters comprising an observed activation profile is highly unlikely to have occurred by chance. This inference pertains to the set of activations reaching criteria and represents a new way of assigning P values to distributed effects. Cluster-level inferences are a special case of set-level inferences, which obtain when the number of clusters c = 1. Similarly voxel-level inferences are special cases of cluster-level inferences that result when the cluster can be very small (i.e., k = 0). Using a theoretical power analysis of distributed activations, we observed that set-level inferences are generally more powerful than cluster-level inferences and that cluster-level inferences are generally more powerful than voxel-level inferences. The price paid for this increased sensitivity is reduced localizing power: Voxel-level tests permit individual voxels to be identified as significant, whereas cluster-and set-level inferences only allow clusters or sets of clusters to be so identified. For all levels of inference the spatial size of the underlying signal f (relative to resolution) determines the most powerful thresholds to adopt. For set-level inferences if f is large (e.g., fMRI) then the optimum extent threshold should be greater than the expected number of voxels for each cluster. If f is small (e.g., PET) the extent threshold should be small. We envisage that set-level inferences will find a role in making statistical inferences about distributed activations, particularly in fMRI.
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                Author and article information

                Journal
                10.1027/1192-5604/a000076
                http://creativecommons.org/licenses/by-nc/3.0/

                Nursing,Psychology,Social & Behavioral Sciences,Health & Social care
                Nursing, Psychology, Social & Behavioral Sciences, Health & Social care

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