Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain

Mental state decoders: game-changers or wishful thinking?

Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.

Decoding visual fatigue in a visual search task selectively manipulated via myopia-correcting lenses

Introduction

Visual fatigue resulting from sustained, high-workload visual activities can significantly impact task performance and general wellbeing. So far, however, little is known about the underlying brain networks of visual fatigue. This study aimed to identify such potential networks using a unique paradigm involving myopia-correcting lenses known to directly modulate subjectively-perceived fatigue levels.

Methods

A sample of N = 31 myopia participants [right eye-SE: -3.77D (SD: 2.46); left eye-SE: -3.75D (SD: 2.45)] performed a demanding visual search task with varying difficulty levels, both with and without the lenses, while undergoing fMRI scanning. There were a total of 20 trials, after each of which participants rated the perceived difficulty and their subjective visual fatigue level. We used representational similarity analysis to decode brain regions associated with fatigue and difficulty, analyzing their individual and joint decoding pattern.

Results and discussion

Behavioral results showed correlations between fatigue and difficulty ratings and above all a significant reduction in fatigue levels when wearing the lenses. Imaging results implicated the cuneus, lingual gyrus, middle occipital gyrus (MOG), and declive for joint fatigue and difficulty decoding. Parts of the lingual gyrus were able to selectively decode perceived difficulty. Importantly, a broader network of visual and higher-level association areas showed exclusive decodability of fatigue (culmen, middle temporal gyrus (MTG), parahippocampal gyrus, precentral gyrus, and precuneus). Our findings enhance our understanding of processing within the context of visual search, attention, and mental workload and for the first time demonstrate that it is possible to decode subjectively-perceived visual fatigue during a challenging task from imaging data. Furthermore, the study underscores the potential of myopia-correcting lenses in investigating and modulating fatigue.

A large-scale fMRI dataset for the visual processing of naturalistic scenes

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

A large-scale fMRI dataset for human action recognition

Human action recognition is a critical capability for our survival, allowing us to interact easily with the environment and others in everyday life. Although the neural basis of action recognition has been widely studied using a few action categories from simple contexts as stimuli, how the human brain recognizes diverse human actions in real-world environments still needs to be explored. Here, we present the Human Action Dataset (HAD), a large-scale functional magnetic resonance imaging (fMRI) dataset for human action recognition. HAD contains fMRI responses to 21,600 video clips from 30 participants. The video clips encompass 180 human action categories and offer a comprehensive coverage of complex activities in daily life. We demonstrate that the data are reliable within and across participants and, notably, capture rich representation information of the observed human actions. This extensive dataset, with its vast number of action categories and exemplars, has the potential to deepen our understanding of human action recognition in natural environments.

BrainIAK: The Brain Imaging Analysis Kit

Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.