Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented.

Biases in BCI experiments: Do we really need to balance stimulus properties across categories?

Brain Computer Interfaces (BCIs) consist of an interaction between humans and computers with a specific mean of communication, such as voice, gestures, or even brain signals that are usually recorded by an Electroencephalogram (EEG). To ensure an optimal interaction, the BCI algorithm typically involves the classification of the input signals into predefined task-specific categories. However, a recurrent problem is that the classifier can easily be biased by uncontrolled experimental conditions, namely covariates, that are unbalanced across the categories.

Open and reproducible neuroimaging: From study inception to publication.

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.

Mindfulness related changes in grey matter: a systematic review and meta-analysis.

Knowing target regions undergoing strfuncti changes caused by behavioural interventions is paramount in evaluating the effectiveness of such practices. Here, using a systematic review approach, we identified 25 peer-reviewed magnetic resonance imaging (MRI) studies demonstrating grey matter changes related to mindfulness meditation. An activation likelihood estimation (ALE) analysis (n = 16) revealed the right anterior ventral insula as the only significant region with consistent effect across studies, whilst an additional functional connectivity analysis indicates that both left and right insulae, and the anterior cingulate gyrus with adjacent paracingulate gyri should also be considered in future studies.

From BIDS-Formatted EEG Data to Sensor-Space Group Results: A Fully Reproducible Workflow With EEGLAB and LIMO EEG.

Reproducibility is a cornerstone of scientific communication without which one cannot build upon each other's work. Because modern human brain imaging relies on many integrated steps with a variety of possible algorithms, it has, however, become impossible to report every detail of a data processing workflow. In response to this analytical complexity, community recommendations are to share data analysis pipelines (scripts that implement workflows).

The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging.

Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis.

EEG-BIDS, an extension to the brain imaging data structure for electroencephalography.

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. Here we present an extension to BIDS for electroencephalography (EEG) data, EEG-BIDS, along with tools and references to a series of public EEG datasets organized using this new standard.

Overlapping but Divergent Neural Correlates Underpinning Audiovisual Synchrony and Temporal Order Judgments.

Multisensory processing is a core perceptual capability, and the need to understand its neural bases provides a fundamental problem in the study of brain function. Both synchrony and temporal order judgments are commonly used to investigate synchrony perception between different sensory cues and multisensory perception in general. However, extensive behavioral evidence indicates that these tasks do not measure identical perceptual processes.

Logical and Methodological Issues Affecting Genetic Studies of Humans Reported in Top Neuroscience Journals.

Genetics and neuroscience are two areas of science that pose particular methodological problems because they involve detecting weak signals (i.e., small effects) in noisy data.

Beyond differences in means: robust graphical methods to compare two groups in neuroscience.

If many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs: to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here, we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist's toolbox, we present two powerful tools that can help us understand how groups of observations differ: the shift function and the difference asymmetry function.

A structural and functional magnetic resonance imaging dataset of brain tumour patients.

We collected high resolution structural (T1, T2, DWI) and several functional (BOLD T2*) MRI data in 22 patients with different types of brain tumours. Functional imaging protocols included a motor task, a verb generation task, a word repetition task and resting state. Imaging data are complemented by demographics (age, sex, handedness, and pathology), behavioural results to motor and cognitive tests and direct cortical electrical stimulation data (pictures of stimulation sites with outcomes) performed during surgery.

Hemispheric association and dissociation of voice and speech information processing in stroke.

As we listen to someone speaking, we extract both linguistic and non-linguistic information. Knowing how these two sets of information are processed in the brain is fundamental for the general understanding of social communication, speech recognition and therapy of language impairments. We investigated the pattern of performances in phoneme versus gender categorization in left and right hemisphere stroke patients, and found an anatomo-functional dissociation in the right frontal cortex, establishing a new syndrome in voice discrimination abilities.

The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices.

fMRI studies increasingly examine functions and properties of non-primary areas of human auditory cortex. However there is currently no standardized localization procedure to reliably identify specific areas across individuals such as the standard 'localizers' available in the visual domain. Here we present an fMRI 'voice localizer' scan allowing rapid and reliable localization of the voice-sensitive 'temporal voice areas' (TVA) of human auditory cortex.

Imaging learned fear circuitry in awake mice using fMRI.

Functional magnetic resonance imaging (fMRI) of learned behaviour in 'awake rodents' provides the opportunity for translational preclinical studies into the influence of pharmacological and genetic manipulations on brain function. fMRI has recently been employed to investigate learned behaviour in awake rats. Here, this methodology is translated to mice, so that future fMRI studies may exploit the vast number of genetically modified mouse lines that are available.

Single-subject analyses of magnetoencephalographic evoked responses to the acoustic properties of affective non-verbal vocalizations.

Magneto-encephalography (MEG) was used to examine the cerebral response to affective non-verbal vocalizations (ANVs) at the single-subject level. Stimuli consisted of non-verbal affect bursts from the Montreal Affective Voices morphed to parametrically vary acoustical structure and perceived emotional properties. Scalp magnetic fields were recorded in three participants while they performed a 3-alternative forced choice emotion categorization task (Anger, Fear, Pleasure).

Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers.

This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (1) model parameterization (modeling baseline or null events) and scaling of the design matrix; (2) hemodynamic modeling using basis functions, and (3) computing percentage signal change.

Behavioral evidence of a dissociation between voice gender categorization and phoneme categorization using auditory morphed stimuli.

Both voice gender perception and speech perception rely on neuronal populations located in the peri-sylvian areas. However, whilst functional imaging studies suggest a left vs. right hemisphere and anterior vs.

Test-retest reliability of structural brain networks from diffusion MRI.

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.

A test-retest fMRI dataset for motor, language and spatial attention functions.

Background: Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications.