SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

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.

Diffusion-weighted imaging coupled with tractography is the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces a large collection of white-matter fascicles as output; the connectome. We introduce a method to evaluate the evidence supporting connectomes. Linear Fascicle Evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to measure prediction error. Finally, we introduce two metrics that use the prediction error to evaluate the evidence supporting properties of the connectome. One metric compares the mean prediction error between alternative hypotheses, and the second metric compares full distributions of prediction error. We use these metrics to (1) compare tractography algorithms, and (2) test hypotheses about tracts and connections.

The neural mechanisms underlying cognitive deficits in schizophrenia remain essentially unknown. The GABA hypothesis proposes that reduced neuronal GABA concentration and neurotransmission results in cognitive impairments in schizophrenia. However, few in vivo studies have directly examined this hypothesis. We used magnetic resonance spectroscopy (MRS) at high field to measure visual cortical GABA levels in 13 subjects with schizophrenia and 13 demographically matched healthy control subjects. We found that the schizophrenia group had an ϳ10% reduction in GABA concentration. We further tested the GABA hypothesis by examining the relationship between visual cortical GABA levels and orientation-specific surround suppression (OSSS), a behavioral measure of visual inhibition thought to be dependent on GABAergic synaptic transmission. Previous work has shown that subjects with schizophrenia exhibit reduced OSSS of contrast discrimination (Yoon et al., 2009). For subjects with both MRS and OSSS data (n ϭ 16), we found a highly significant positive correlation (r ϭ 0.76) between these variables. GABA concentration was not correlated with overall contrast discrimination performance for stimuli without a surround (r ϭ Ϫ0.10). These results suggest that a neocortical GABA deficit in subjects with schizophrenia leads to impaired cortical inhibition and that GABAergic synaptic transmission in visual cortex plays a critical role in OSSS.

The vertical occipital fasciculus (VOF) is the only major fiber bundle connecting dorsolateral and ventrolateral visual cortex. Only a handful of studies have examined the anatomy of the VOF or its role in cognition in the living human brain. Here, we trace the contentious history of the VOF, beginning with its original discovery in monkey by Wernicke (1881) and in human by Obersteiner (1888), to its disappearance from the literature, and recent reemergence a century later. We introduce an algorithm to identify the VOF in vivo using diffusion-weighted imaging and tractography, and show that the VOF can be found in every hemisphere (n = 74). Quantitative T1 measurements demonstrate that tissue properties, such as myelination, in the VOF differ from neighboring white-matter tracts. The terminations of the VOF are in consistent positions relative to cortical folding patterns in the dorsal and ventral visual streams. Recent findings demonstrate that these same anatomical locations also mark cytoarchitectonic and functional transitions in dorsal and ventral visual cortex. We conclude that the VOF is likely to serve a unique role in the communication of signals between regions on the ventral surface that are important for the perception of visual categories (e.g., words, faces, bodies, etc.) and regions on the dorsal surface involved in the control of eye movements, attention, and motion perception. T he vertical occipital fasciculus (VOF) is the only major fiber bundle connecting dorsal and ventral regions of occipital, parietal, and temporal cortex. The signals carried by the VOF are likely to play an essential role in an array of visual and cognitive functions. Characterizing the VOF connections and tissue structure in the living human brain is important for the study of human vision and cognitive neuroscience alike.Carl Wernicke discovered the VOF (1). For the next 30 y, the VOF was included in many major neuroanatomy atlases and journal articles (1-14). However, Wernicke's study contradicted a widely accepted principle of white-matter organization proposed by Meynert, Wernicke's mentor. Over the subsequent decades, there emerged a camp of neuroanatomists who acknowledged Wernicke's discovery and another group that, like Meynert, disregarded the discovery. Due to its controversial beginnings, haphazard naming convention, and the difficulty of standardizing postmortem procedures, the VOF largely disappeared from the literature for most of the next century. A century later, Yeatman et al. (15) rediscovered the VOF using diffusion magnetic resonance imaging (dMRI); they were the first to characterize the VOF cortical projections in the living, behaving, human brain.Why would such an important pathway disappear from the literature for so long? The disappearance can be traced to controversies and confusions among some of the most prominent neuroanatomists of the 19th century (1-13, 16-18). Modern, in vivo, MRI measurements and algorithms allow for precise, reproducible, scalable computations that can resolve these cen...

Degenerative retinal diseases such as retinitis pigmentosa and macular degeneration cause irreversible vision loss in more than 10 million people worldwide. Retinal prostheses, now implanted in over 250 patients worldwide, electrically stimulate surviving cells in order to evoke neuronal responses that are interpreted by the brain as visual percepts (‘phosphenes’). However, instead of seeing focal spots of light, current implant users perceive highly distorted phosphenes that vary in shape both across subjects and electrodes. We characterized these distortions by asking users of the Argus retinal prosthesis system (Second Sight Medical Products Inc.) to draw electrically elicited percepts on a touchscreen. Using ophthalmic fundus imaging and computational modeling, we show that elicited percepts can be accurately predicted by the topographic organization of optic nerve fiber bundles in each subject’s retina, successfully replicating visual percepts ranging from ‘blobs’ to oriented ‘streaks’ and ‘wedges’ depending on the retinal location of the stimulating electrode. This provides the first evidence that activation of passing axon fibers accounts for the rich repertoire of phosphene shape commonly reported in psychophysical experiments, which can severely distort the quality of the generated visual experience. Overall our findings argue for more detailed modeling of biological detail across neural engineering applications.

Human visual cortex comprises many visual field maps organized into clusters. A standard organization separates visual maps into 2 distinct clusters within ventral and dorsal cortex. We combined fMRI, diffusion MRI, and fiber tractography to identify a major white matter pathway, the vertical occipital fasciculus (VOF), connecting maps within the dorsal and ventral visual cortex. We use a model-based method to assess the statistical evidence supporting several aspects of the VOF wiring pattern. There is strong evidence supporting the hypothesis that dorsal and ventral visual maps communicate through the VOF. The cortical projection zones of the VOF suggest that human ventral (hV4/VO-1) and dorsal (V3A/B) maps exchange substantial information. The VOF appears to be crucial for transmitting signals between regions that encode object properties including form, identity, and color and regions that map spatial information.