Diffusion Imaging in Python (Dipy) is a free and open source software project for the analysis of data from diffusion magnetic resonance imaging (dMRI) experiments. dMRI is an application of MRI that can be used to measure structural features of brain white matter. Many methods have been developed to use dMRI data to model the local configuration of white matter nerve fiber bundles and infer the trajectory of bundles connecting different parts of the brain. Dipy gathers implementations of many different methods in dMRI, including: diffusion signal pre-processing; reconstruction of diffusion distributions in individual voxels; fiber tractography and fiber track post-processing, analysis and visualization. Dipy aims to provide transparent implementations for all the different steps of dMRI analysis with a uniform programming interface. We have implemented classical signal reconstruction techniques, such as the diffusion tensor model and deterministic fiber tractography. In addition, cutting edge novel reconstruction techniques are implemented, such as constrained spherical deconvolution and diffusion spectrum imaging (DSI) with deconvolution, as well as methods for probabilistic tracking and original methods for tractography clustering. Many additional utility functions are provided to calculate various statistics, informative visualizations, as well as file-handling routines to assist in the development and use of novel techniques. In contrast to many other scientific software projects, Dipy is not being developed by a single research group. Rather, it is an open project that encourages contributions from any scientist/developer through GitHub and open discussions on the project mailing list. Consequently, Dipy today has an international team of contributors, spanning seven different academic institutions in five countries and three continents, which is still growing.

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"Attention" is not a unitary brain process. Evidence from adult studies indicates that distinct neuroanatomical networks perform specific attentional operations and that these are vulnerable to selective damage. Accordingly, characterising attentional disorders requires the use of a variety of tasks that differentially challenge these systems. Here we describe a novel battery, the Test of Everyday Attention for Children (TEA-Ch), comprising nine subtests adapted from the adult literature. The performance of 293 healthy children between the ages of 6 and 16 is described together with the relationships to IQ, existing measures of attention, and scholastic attainment. This large normative sample also allows us to test the fit of the adult model of functionally separable attention systems to the observed patterns of variance in children's performance. A Structural Equation Modelling approach supports this view. A three-factor model of sustained and selective attention and higher-level "executive" control formed a good fit to the data, even in the youngest children. A single factor model was rejected. There are behavioural and anatomical grounds to believe that Attention Deficit Disorder (ADD) is particularly associated with poor self-sustained attention and behavioural control. The TEA-Ch performance of 24 boys diagnosed with ADD presented here is consistent with this view. When performance levels on WISC-III subtests were taken into account, specific deficits in sustained attention were apparent while selective attention performance was within the normal range.

Certain patterns of stripes are judged to be unpleasant to look at. They induce illusions of colour, shape and motion that are sometimes perceived predominantly to one side of fixation. People who suffer frequent headaches tend to report more illusions, and if the pain consistently occurs on the same side of the head the illusions tend to be lateralized. The parameters of the patterns that induce illusions (including their shape, spatial frequency, duty cycle, contrast and cortical representation) closely resemble those that elicit epileptiform electroencephalographic abnormalities in patients with photosensitive epilepsy. The viewing conditions under which such abnormalities are likely to appear are also those under which more illusions are seen.

The frequency selectivity of the auditory system was measured by masking a sinusoidal signal (0.5, 2.0, or 4.0 kHz) or a filtered-speech signal with a wideband noise having a notch, or stopband, centered on the signal. As the notch was widened performance improved for both types of signal but the rate of improvement decreased as the age of the 16 listeners increased from 23 to 75 years, indicating a loss in frequency selectivity with age. Auditory filter shapes derived from the tone-in-noise data show (a) that the passband of the filter broadens progressively with age, and (b) that the dynamic range of the filter ages like the audiogram. That is, the range changes little with age before 55, but beyond this point there is an accelerating rate of loss. The speech experiment shows comparable but smaller effects. The filter-width measurements show that the critical ratio is a poor estimator of frequency selectivity because it confounds the tuning of the system with the efficiency of the signal-detection and speech-processing mechanisms that follow the filter. An alternative, one-point measure of frequency selectivity, which is both sensitive and reliable, is developed via the filter-shape model of masking.

A range of tests of everyday attention is described, based on ecologically plausible activities such as searching maps, looking through telephone directories, and listening to lottery number broadcasts. An age-, sex- and IQ-stratified sample of 154 normal participants was given these tests, along with a number of existing tests of attention. The factor structure revealed by this data set matched well contemporary evidence for a set of functionally independent attentional circuits in the brain, and included factors for sustained attention, selective attention, attentional switching and auditory-verbal working memory. The Test of Everyday Attention (TEA), which was developed and standardized on the basis of these subtests, has three parallel forms, high test-retest reliability, and correlates significantly with existing measures of attention. Furthermore, selected subtests successfully discriminate among a number of brain-impaired groups, including closed head injury versus age-matched controls, minimal versus mild Alzheimer’s disease, and progressive supranuclear palsy patients versus age-matched controls. (JINS, 1996, 2, 525–534.)

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The development of a test aimed at estimating premorbid intelligence is described. The test, Spot-the-Word, involves presenting the subject with pairs of items comprising one word and one non-word, and requiring the subject to identify the word. Data show that performance correlates highly with verbal intelligence as estimated by Mill Hill Vocabulary score and by performance on the National Adult Reading Test (NART). Performance does not decline with age, in contrast to an associated test of verbal recognition memory. A second study attempted to test the effect of intellectual deterioration due to age on Spot-the-Word performance. Elderly subjects who had high vocabulary scores scored well on the Spot-the-Word regardless of whether fluid intelligence as measured by the AH4 test was well preserved, or was low, implying intellectual deterioration. A final study collected normative data on a sample of 224 subjects stratified by age and socio-economic status, with each subject performing two parallel forms of the test, A and B, together with the NART. Correlation between the two forms was .884, while correlation with NART was 331 for Form A and ,859 for Form B, suggesting adequate reliability and validity. It is concluded that the test provides a potentially useful additional method of estimating premorbid intelligence.When assessing patients for clinical, medico-legal or research purposes, interpretation of results will often depend on the assumptions made about the premorbid intellectual capacity of the patient. A low level of performance may of course imply either deterioration from a previously high level, or little or no impairment from a low premorbid level. Similarly, performance within the average range may reflect an absence of deficit, or a genuine impairment in the performance of a patient who previously performed at a superior level. A number of methods have been devised in order to cope with this problem by estimating premorbid intelligence.This area is reviewed by Crawford (1989), who concentrates on two approaches, one based on demographic variables such as educational and occupational history, while the other is based on the National Adult Reading Test (NART). This capitalizes on the fact that the capacity to read and correctly pronounce a familiar word is relatively insensitive to general intellectual deterioration. By requiring the subject to read a series of orthographically irregular words ranging in frequency from common to rare, Nelson & McKenna (1975) demonstrated that performance on the NART can be used to estimate premorbid verbal intelligence.In comparing these two methods, Crawford concludes that, while demographic * Requests for reprints.