A continuum of acoustically varying speech sounds is not perceived as a continuum, but as distinct phonetic categories. Chang et al. recorded directly from human posterior superior temporal gyrus and found that this area has a similarly discontinuous coding of objectively continuous sound, matching perception and indicating higher-level processing. Speech perception requires the rapid and effortless extraction of meaningful phonetic information from a highly variable acoustic signal. A powerful example of this phenomenon is categorical speech perception, in which a continuum of acoustically varying sounds is transformed into perceptually distinct phoneme categories. We found that the neural representation of speech sounds is categorically organized in the human posterior superior temporal gyrus. Using intracranial high-density cortical surface arrays, we found that listening to synthesized speech stimuli varying in small and acoustically equal steps evoked distinct and invariant cortical population response patterns that were organized by their sensitivities to critical acoustic features. Phonetic category boundaries were similar between neurometric and psychometric functions. Although speech-sound responses were distributed, spatially discrete cortical loci were found to underlie specific phonetic discrimination. Our results provide direct evidence for acoustic-to–higher order phonetic level encoding of speech sounds in human language receptive cortex.

The brain should integrate related but not unrelated information from different senses. Temporal patterning of inputs to different modalities may provide critical information about whether those inputs are related or not. We studied effects of temporal correspondence between auditory and visual streams on human brain activity with functional magnetic resonance imaging (fMRI). Streams of visual flashes with irregularly jittered, arrhythmic timing could appear on right or left, with or without a stream of auditory tones that coincided perfectly when present (highly unlikely by chance), were noncoincident with vision (different erratic, arrhythmic pattern with same temporal statistics), or an auditory stream appeared alone. fMRI revealed blood oxygenation level-dependent (BOLD) increases in multisensory superior temporal sulcus (mSTS), contralateral to a visual stream when coincident with an auditory stream, and BOLD decreases for noncoincidence relative to unisensory baselines. Contralateral primary visual cortex and auditory cortex were also affected by audiovisual temporal correspondence or noncorrespondence, as confirmed in individuals. Connectivity analyses indicated enhanced influence from mSTS on primary sensory areas, rather than vice versa, during audiovisual correspondence. Temporal correspondence between auditory and visual streams affects a network of both multisensory (mSTS) and sensory-specific areas in humans, including even primary visual and auditory cortex, with stronger responses for corresponding and thus related audiovisual inputs.

Human colour vision originates in the cone photoreceptors, whose spatial density peaks in the fovea and declines rapidly into the periphery. For this reason, one expects to find a large representation of the cone–rich fovea in those cortical locations that support colour perception. Human occipital cortex contains several distinct foveal representations including at least two that extend onto the ventral surface: a region thought to be critical for colour vision. To learn more about these ventral signals, we used functional magnetic resonance imaging to identify visual field maps and colour responsivity on the ventral surface. We found a visual map of the complete contralateral hemifield in a 4 cm ² region adjacent to ventral V3; the foveal representation of this map is confluent with that of areas V1/2/3. Additionally, a distinct foveal representation is present on the ventral surface situated 3–5 cm anterior from the confluent V1/2/3 foveal representations. This organization is not consistent with the definition of area V8, which assumes the presence of a quarter field representation adjacent to V3v. Comparisons of responses to luminance–matched coloured and achromatic patterns show increased activity to the coloured stimuli beginning in area V1 and extending through the new hemifield representation and further anterior in the ventral occipital lobe.

Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide towards conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aim is to provide a practical understanding of predictive modeling of human brain data and to propose best-practices in conducting these analyses.

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings.