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

The field of neuroanatomy of language is moving forward at a fast pace. This
progression is partially due to the development of diffusion tractography, which
has been used to describe white matter connections in the living human brain.
For the field of neurolinguistics this advancement is timely and important for
two reasons. First, it allows clinical researchers to liberate themselves from
neuroanatomical models of language derived from animal studies. Second, for
the first time, it offers the possibility of testing network correlates of
neurolinguistic models directly in the human brain. This chapter introduces the
reader to general principles of diffusion imaging and tractography. Examples of
its applications, such as tract analysis, will be used to explicate its potentials and
limitations.

Abstract

Our understanding of the functioning of the brain is primarily based on an average model of the brain's functional organisation, and any deviation from the standard is considered as random noise or a pathological appearance. Studying pathologies has, however, greatly contributed to our understanding of brain functions. For instance, the study of naturally-occurring or surgically-induced brain lesions revealed that language is predominantly lateralised to the left hemisphere while perception/action and emotion are commonly lateralised to the right hemisphere. The lateralisation of function was subsequently replicated by task-related functional neuroimaging in the healthy population. Despite its high significance and reproducibility, this pattern of lateralisation of function is true for most, but not all participants. Bilateral and flipped representations of classically lateralised functions have been reported during development and in the healthy adult population for language, perception/action and emotion. Understanding these different functional representations at an individual level is crucial to improve the sophistication of our models and account for the variance in developmental trajectories, cognitive performance differences and clinical recovery. With the availability of in vivo neuroimaging, it has become feasible to study large numbers of participants and reliably characterise individual differences, also referred to as phenotypes. Yet, we are at the beginning of inter-individual variability modelling, and new theories of brain function will have to account for these differences across participants.

Abstract

Last year was the 150th anniversary of Paul Broca's landmark case report on speech disorder that paved the way for subsequent studies of cortical localization of higher cognitive functions. However, many complex functions rely on the activity of distributed networks rather than single cortical areas. Hence, it is important to understand how brain regions are linked within large-scale networks and to map lesions onto connecting white matter tracts. To facilitate this network approach we provide a synopsis of classical neurological syndromes associated with frontal, parietal, occipital, temporal and limbic lesions. A review of tractography studies in a variety of neuropsychiatric disorders is also included. The synopsis is accompanied by a new atlas of the human white matter connections based on diffusion tensor tractography freely downloadable on http://www.natbrainlab.com. Clinicians can use the maps to accurately identify the tract affected by lesions visible on conventional CT or MRI. The atlas will also assist researchers to interpret their group analysis results. We hope that the synopsis and the atlas by allowing a precise localization of white matter lesions and associated symptoms will facilitate future work on the functional correlates of human neural networks as derived from the study of clinical populations. Our goal is to stimulate clinicians to develop a critical approach to clinico-anatomical correlative studies and broaden their view of clinical anatomy beyond the cortical surface in order to encompass the dysfunction related to connecting pathways.