Publications

Abstract

This chapter introduces the origins and development of our current anatomical terminology. It scrutinizes the historical evolution and etymological significance of the over 1900 official anatomical terms in the current nomenclature, underscoring their impact on the contemporary comprehension of cognitive neuroanatomy. The chapter traces unification efforts from the Basel Nomina Anatomica in 1895 to the 1998 Terminologia Anatomica, noting challenges arising from outdated terminology in light of recent anatomical advancements.

Highlighting the influence of terminologies on interpretations of brain anatomy, the chapter explores several anatomical mapping methods such as surface, sectional, connectional, and functional anatomy. It illuminates discrepancies and controversies, exemplified by divergent interpretations of the number of brain lobes and the definitions of 'Broca' and 'Wernicke' areas.

The chapter explores anatomical terms' historical and cultural underpinnings, encompassing mythonyms, eponyms, and cultural influences on nomenclature. It critically examines the implications of these terminologies on contemporary research and shows that Large Language Models mirror these discrepancies. It underscores the need for more inclusive and culturally sensitive approaches in anatomical education.

Lastly, we advocate for updating anatomical nomenclature, suggesting that a deeper understanding of these terminologies could provide insights and aid in resolving ongoing debates in the field. This examination sheds light on historical knowledge and emphasizes the dynamic interplay between language, culture, and anatomy in shaping our comprehension of the neurobiology of the brain and how we navigate neuroanatomy in the 21st century.

Abstract

The human brain is an intricate network of cortical regions interconnected by white matter pathways, dynamically supporting cognitive functions. While cortical asymmetries have been consistently reported, the asymmetry of white matter connections remains less explored. This chapter provides a brief overview of asymmetries observed at the cortical, subcortical, cytoarchitectural, and receptor levels before exploring the detailed connectional anatomy of the human brain. It thoroughly examines the lateralization and interindividual variability of 56 distinct white matter tracts, offering a comprehensive review of their structural characteristics and interindividual variability. Additionally, we provide an extensive update on the asymmetry of a wide range of white matter tracts using high-resolution data from the Human Connectome Project (7T HCP www.humanconnectome.org). Future research and advanced quantitative analyses are crucial to understanding fully how asymmetry contributes to interindividual variability. This comprehensive exploration enhances our understanding of white matter organization and its potential implications for brain function.

Abstract

The brain is the most magnificent structure, and we are only at the cusp of unraveling some of its complexity. Neuroanatomy is the best tool to map the brain's structural complexity. As such, neuroanatomy is not just an academic exercise; it serves our fundamental understanding of the neurobiology of cognition and improves clinical practice. A deepened anatomical understanding has advanced our conceptual grasp of the evolution of the brain, interindividual variability of cognition in health and disease, and the conceptual shift toward the emergence of cognition. For the past 20 years, diffusion imaging tractography has dramatically facilitated these advances by enabling the study of the delicate networks that orchestrate brain processes (for review, see Thiebaut de Schotten and Forkel, 2022). Several steps are consistent across all studied populations and brain states (health/disease) when analyzing tractography data. We discuss various considerations for dissections across populations and give practical tips on common pitfalls and features to improve the visualization of the dissections. We briefly discuss specific considerations for manual dissections in nonhuman primates. Lastly, we provide an atlas of regions of interest (ROIs) for the most commonly delineated white matter connections in the human brain.

Abstract

Brain tumors are classified as rare diseases, with an annual occurrence of 300,000 cases and account for an annual loss of 241,000 lives, highlighting their devastating nature. Recent advancements in diagnosis and treatment have significantly improved the management and care of brain tumors. This chapter provides an overview of the common types of primary brain tumors affecting language functions—gliomas and meningiomas. Techniques for identifying and mapping critical language areas, including the white matter language system, such as awake brain tumor surgery and diffusion-weighted tractography, are pivotal for understanding language localization and informing personalized treatment approaches. Numerous studies have demonstrated that gliomas in the dominant hemisphere can lead to (often subtle) impairments across various cognitive domains, with a particular emphasis on language. Recently, increased attention has been directed toward (nonverbal) cognitive deficits in patients with gliomas in the nondominant hemisphere, as well as cognitive outcomes in patients with meningiomas, a group historically overlooked. A patient-tailored approach to language and cognitive functions across the pre-, intra-, and postoperative phases is mandatory for brain tumor patients to preserve quality of life. Continued follow-up studies, in conjunction with advanced imaging techniques, are crucial for understanding the brain's potential for neuroplasticity and optimizing patient outcomes.

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 nineteenth century witnessed some of the greatest neuroanatomists of all times. Amongst them is the largely forgotten Heinrich Sachs, a student of Carl Wernicke in Breslau.

Abstract

This is the first complete translation of Heinrich Sachs' outstanding white matter atlas dedicated to the occipital lobe. This work is accompanied by a prologue by Prof Carl Wernicke who for many years was Sachs' mentor in Breslau and enthusiastically supported his work.