Publications

Abstract

In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

Abstract

Interhemispheric anatomical asymmetries have long been thought to be related to language lateralisation. Previous studies have explored whether asymmetries in the diffusion characteristics of white matter language tracts are consistent with language lateralisation. These studies, typically with smaller cohorts, yielded mixed results. This study investigated whether connectomic analysis of quantitative anisotropy (QA) and shape features of white matter tracts across the whole brain are associated with language lateralisation. We analysed 1040 healthy individuals from the Human Connectome Project database. Hemispheric language dominance for each participant was quantified using a laterality quotient (LQ) derived from fMRI activation in regions of interest (ROIs) associated with a language comprehension task compared against a math task. A linear regression model was used to examine the relationship between structural asymmetry and functional lateralisation. Connectometry revealed that LQs were significantly negatively correlated with QA of corpus callosum tracts, including forceps minor, body, tapetum, and forceps major, indicating that reduced language dominance (more bilateral language representation) is associated with increased QA in these regions. The QA of the left arcuate fasciculus, cingulum, and right cerebellar tracts was positively associated with LQ, suggesting that stronger structural asymmetry in these tracts may identify left language dominance. Language lateralisation was not significantly associated with the shape metrics (including length, span, curl, elongation, diameter, volume, and surface area) of all white matter tracts. These results suggest that diffusion measures of microstructural architecture, and not the geometric features of reconstructed white matter tracts, are associated with lateralisation of language comprehension functions. People with increased dependence on both cerebral hemispheres for language processing may have more developed commissural fibres, which may support more efficient interhemispheric communication.

Abstract

Traditionally, the frontotemporal arcuate fasciculus (AF) is viewed as a single entity in anatomo-clinical models. However, it is unclear if distinct cortical origin and termination patterns within this bundle correspond to specific language functions. We use track-weighted dynamic functional connectivity, a hybrid imaging technique, to study the AF structure and function in two distinct datasets of healthy subjects. Here we show that the AF can be subdivided based on dynamic changes in functional connectivity at the streamline endpoints. An unsupervised parcellation algorithm reveals spatially segregated subunits, which are then functionally quantified through meta-analysis. This approach identifies three distinct clusters within the AF - ventral, middle, and dorsal frontotemporal AF - each linked to different frontal and temporal termination regions and likely involved in various language production and comprehension aspects. Our findings may have relevant implications for the understanding of the functional anatomy of the AF as well as its contribution to linguistic and non-linguistic functions.

Abstract

Stroke is common, and its consequent brain damage can cause various cognitive impairments. Associations between where and how much brain lesion damage a patient has suffered, and the particular impairments that injury has caused (lesion-symptom associations) offer potentially compelling insights into how the brain implements cognition.1 A better understanding of those associations can also fill a gap in current stroke medicine by helping us to predict how individual patients might recover from post-stroke impairments.2 Most recent work in this area employs machine learning models trained with data from stroke patients whose mid-to-long-term outcomes are known.2-4 These machine learning models are tested by predicting new outcomes—typically scores on standardized tests of post-stroke impairment—for patients whose data were not used to train the model. Traditionally, these validation results have been shared in peer-reviewed publications describing the model and its training. But recently, and for the first time in this field (as far as we know), one of these pre-trained models has been made public—The Disconnectome Symptom Discoverer model (DSD) which draws its predictors from structural disconnection information inferred from stroke patients’ brain MRI.5

Here, we test the DSD model on wholly independent data, never seen by the model authors, before they published it. Specifically, we test whether its predictive performance is just as accurate as (i.e. not significantly worse than) that reported in the original (Washington University) dataset, when predicting new patients’ outcomes at a similar time post-stroke (∼1 year post-stroke) and also in another independent sample tested later (5+ years) post-stroke. A failure to generalize the DSD model occurs if it performs significantly better in the Washington data than in our data from patients tested at a similar time point (∼1 year post-stroke). In addition, a significant decrease in predictive performance for the more chronic sample would be evidence that lesion-symptom associations differ at ∼1 year post-stroke and >5 years post-stroke.

Abstract

Over the past three decades, functional neuroimaging has amassed abundant evidence of the intricate interplay between brain structure and function. However, the potential anatomical and experimental overlap, independence, granularity, and gaps between functions remain poorly understood. Here, we show the latent structure of the current brain-cognition knowledge and its organisation. Our approach utilises the most comprehensive meta-analytic fMRI database (Neurosynth) to compute a three-dimensional embedding space–morphospace capturing the relationship between brain functions as we currently understand them. The space structure enables us to statistically test the relationship between functions expressed as the degree to which the characteristics of each functional map can be anticipated based on its similarities with others–the predictability index. The morphospace can also predict the activation pattern of new, unseen functions and decode thoughts and inner states during movie watching. The framework defined by the morphospace will spur the investigation of novel functions and guide the exploration of the fabric of human cognition.

Abstract

Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.

Abstract

Social factors play a crucial role in the advancement of science. New findings are discussed and theories emerge through social interactions, which usually take place within local research groups and at academic events such as conferences, seminars, or workshops. This system tends to amplify the voices of a select subset of the community—especially more established researchers—thus limiting opportunities for the larger community to contribute and connect. Brainhack (https://brainhack.org/) events (or Brainhacks for short) complement these formats in neuroscience with decentralized 2- to 5-day gatherings, in which participants from diverse backgrounds and career stages collaborate and learn from each other in an informal setting. The Brainhack format was introduced in a previous publication (Cameron Craddock et al., 2016; Figures 1A and 1B). It is inspired by the hackathon model (see glossary in Table 1), which originated in software development and has gained traction in science as a way to bring people together for collaborative work and educational courses. Unlike many hackathons, Brainhacks welcome participants from all disciplines and with any level of experience—from those who have never written a line of code to software developers and expert neuroscientists. Brainhacks additionally replace the sometimes-competitive context of traditional hackathons with a purely collaborative one and also feature informal dissemination of ongoing research through unconferences.

Abstract

In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain’s functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits’ anatomy to unlock and chart the first maps of functional white matter. To showcase this method’s versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.

Abstract

Clinical neuroscience research relying on animal models brought valuable translational insights into the function and pathologies of the human brain. The anatomical, physiological, and behavioural similarities between humans and mammals have prompted researchers to study cerebral mechanisms at different levels to develop and test new treatments. The vast majority of biomedical research uses rodent models, which are easily manipulable and have a broadly resembling organisation to the human nervous system but cannot satisfactorily mimic some disorders. For these disorders, macaque monkeys have been used as they have a more comparable central nervous system. Still, this research has been hampered by limitations, including high costs and reduced samples. This review argues that a squirrel monkey model might bridge the gap by complementing translational research from rodents, macaque, and humans. With the advent of promising new methods such as ultrasound imaging, tool miniaturisation, and a shift towards open science, the squirrel monkey model represents a window of opportunity that will potentially fuel new translational discoveries in the diagnosis and treatment of brain pathologies.

Abstract

Following right-hemisphere damage, a specific disorder of motor awareness can occur called anosognosia for hemiplegia, i.e. the denial of motor deficits contralateral to a brain lesion. The study of anosognosia can offer unique insights into the neurocognitive basis of awareness. Typically, however, awareness is assessed as a first person judgement and the ability of patients to think about their bodies in more ‘objective’ (third person) terms is not directly assessed. This may be important as right-hemisphere spatial abilities may underlie our ability to take third person perspectives. This possibility was assessed for the first time in the present study. We investigated third person perspective taking using both visuospatial and verbal tasks in right-hemisphere stroke patients with anosognosia ( n = 15) and without anosognosia ( n = 15), as well as neurologically healthy control subjects ( n = 15). The anosognosic group performed worse than both control groups when having to perform the tasks from a third versus a first person perspective. Individual analysis further revealed a classical dissociation between most anosognosic patients and control subjects in mental (but not visuospatial) third person perspective taking abilities. Finally, the severity of unawareness in anosognosia patients was correlated to greater impairments in such third person, mental perspective taking abilities (but not visuospatial perspective taking). In voxel-based lesion mapping we also identified the lesion sites linked with such deficits, including some brain areas previously associated with inhibition, perspective taking and mentalizing, such as the inferior and middle frontal gyri, as well as the supramarginal and superior temporal gyri. These results suggest that neurocognitive deficits in mental perspective taking may contribute to anosognosia and provide novel insights regarding the relation between self-awareness and social cognition.