The optimism bias : a cognitive neuroscience perspective

The optimism bias is a well-established psychological phenomenon. Its study has implications that are far reaching in fields as diverse as mental health and economic theory. With the emerging field of cognitive neuroscience and the advent of advanced neuroimaging techniques, it has been possible to investigate the neural basis of the optimism bias and to understand in which neurological conditions this natural bias fails. This review first defines the optimism bias, discusses its implications and reviews the literature that investigates its neural basis. Finally some potential pitfalls in experimental design are discussed.peer-reviewe

Reconnecting with Joseph and Augusta Dejerine:100 years on

Joseph Dejerine passed away on 28 February 1917 in the midst of a world at war. One hundred years later we celebrate the legacy of this pioneer in neuroscience. In 1895, Joseph Jules Dejerine published the first volume of the seminal work, Anatomie des centres nerveux; volume 2 was published in 1901. In a major section of this tome (vol. 1 pp. 749–80), Joseph Dejerine and his wife and long-term collaborator, Augusta Dejerine-Klumpke, produced a treatise on the white matter pathways of the brain, composed of anatomical descriptions of meticulous detail and beautiful illustration (drawn by H. Gillet) that reflected a combination of the most advanced methodologies of the day and a review of leading neuroscientific research. We have selected and focused this specific output (which is provided for the first time as an English translation in the Supplementary material) from the many that the Dejerines published because its ideas and findings continue to be of relevance to modern neuroscience researchers today; especially those with an interest in connectional anatomy.peer-reviewe

Notes on fiber length measurements : a case study in the underbelly of open source neuroscience

Being on the bleeding edge of research requires the use of new and regularly updated software. The result is the occasional and inevitable occurrence of bugs. In the following work we present a case study where a feature request introduced a bug in a neuroimaging software package, which had consequences for the quality of results in a published article. We discuss the process of diagnosis, rectification and analysis replication.peer-reviewe

Diffusion MRI : from basic principles to clinical applications

Diffusion MRI (dMRI) is widely used by clinicians and radiologists to diagnose neurological disorders, in particular stroke. The most commonly encountered diffusion technique in the clinic is simple diffusion weighted imaging and apparent diffusion coefficient (ADC) mapping. However, dMRI can tap into a wealth of data that is usually overlooked by clinicians. While most of this ‘additional’ information is primarily used in a research setting, it is beginning to permeate the clinic. Despite the widespread use of dMRI, clinicians who do not have radiological training may not feel comfortable with the basic principles that underlie this modality. This paper’s aim is to make the fundamentals of the technique accessible to doctors and allied health practitioners who have an interest in dMRI and who use it clinically. It progresses to discuss how these measures can be used.peer-reviewe

Exploring the U-Net++ model for automatic brain tumor segmentation

The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain. U-Net is an example of a popular deep learning model designed specifically for biomedical image segmentation, initially proposed for cell segmentation. We propose a variation of the U-Net++ model, which is itself an adaptation of U-Net, and evaluate its brain tumor segmentation capabilities. The proposed approach obtained Dice Coefficient scores of 0.7192, 0.8712, and 0.7817 for the Enhancing Tumor, Whole Tumor and Tumor Core classes of the BraTS 2019 challenge Validation Dataset. The proposed approach differs from the standard U-Net++ model in a number of ways, including the loss function, number of convolutional blocks, and method of employing deep supervision. Data augmentation and post-processing techniques were also implemented and observed to substantially improve the model predictions. Thus, this article presents a novel adaptation of the U-Net++ architecture, which is both lightweight, and performs comparably with peer-reviewed work evaluated on the same data.peer-reviewe

Local gradient analysis of human brain function using the Vogt-Bailey Index

In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the ‘heavier’ the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.</p

The Graded Change in Connectivity across the Ventromedial Prefrontal Cortex Reveals Distinct Subregions.

The functional heterogeneity of the ventromedial prefrontal cortex (vmPFC) suggests it may include distinct functional subregions. To date these have not been well elucidated. Regions with differentiable connectivity (and as a result likely dissociable functions) may be identified using emergent data-driven approaches. However, prior parcellations of the vmPFC have only considered hard splits between distinct regions, although both hard and graded connectivity changes may exist. Here we determine the full pattern of change in structural and functional connectivity across the vmPFC for the first time and extract core distinct regions. Both structural and functional connectivity varied along a dorsomedial to ventrolateral axis from relatively dorsal medial wall regions to relatively lateral basal orbitofrontal cortex. The pattern of connectivity shifted from default mode network to sensorimotor and multimodal semantic connections. This finding extends the classical distinction between primate medial and orbital regions by demonstrating a similar gradient in humans for the first time. Additionally, core distinct regions in the medial wall and orbitofrontal cortex were identified that may show greater correspondence to functional differences than prior hard parcellations. The possible functional roles of the orbitofrontal cortex and medial wall are discussed.This work was supported by a doctoral prize from the Engineering and Physical Sciences Research Council and a British Academy Postdoctoral Fellowship (pf170068) to RLJ, a studentship from the BBSRC (BB/J014478/1) to CJB, and programme grants from the Medical Research Council (MR/J004146/1 & MR/R023883/1) to MALR

The graded change in connectivity across the ventromedial prefrontal cortex reveals distinct subregions

The functional heterogeneity of the ventromedial prefrontal cortex (vmPFC) suggests it may include distinct functional subregions. To date these have not been well elucidated. Regions with differentiable connectivity (and as a result likely dissociable functions) may be identified using emergent data-driven approaches. However, prior parcellations of the vmPFC have only considered hard splits between distinct regions, although both hard and graded connectivity changes may exist. Here we determine the full pattern of change in structural and functional connectivity across the vmPFC for the first time and extract core distinct regions. Both structural and functional connectivity varied along a dorsomedial to ventrolateral axis from relatively dorsal medial wall regions to relatively lateral basal orbitofrontal cortex. The pattern of connectivity shifted from default mode network to sensorimotor and multimodal semantic connections. This finding extends the classical distinction between primate medial and orbital regions by demonstrating a similar gradient in humans for the first time. Additionally, core distinct regions in the medial wall and orbitofrontal cortex were identified that may show greater correspondence to functional differences than prior hard parcellations. The possible functional roles of the orbitofrontal cortex and medial wall are discussed.peer-reviewe

A structural connectivity convergence zone in the ventral and anterior temporal lobes: Data-driven evidence from structural imaging.

The hub-and-spoke model of semantic cognition seeks to reconcile embodied views of a fully distributed semantic network with patient evidence, primarily from semantic dementia, who demonstrate modality-independent conceptual deficits associated with atrophy centred on the ventrolateral anterior temporal lobe. The proponents of this model have recently suggested that the temporal cortex is a graded representational space where concepts become less linked to a specific modality as they are processed farther away from primary and secondary sensory cortices and towards the ventral anterior temporal lobe. To explore whether there is evidence that the connectivity patterns of the temporal lobe converge in its ventral anterior end the current study uses three dimensional Laplacian eigenmapping, a technique that allows visualisation of similarity in a low dimensional space. In this space similarity is encoded in terms of distances between data points. We found that the ventral and anterior temporal lobe is in a unique position of being at the centre of mass of the data points within the connective similarity space. This can be interpreted as the area where the connectivity profiles of all other temporal cortex voxels converge. This study is the first to explicitly investigate the pattern of connectivity and thus provides the missing link in the evidence that the ventral anterior temporal lobe can be considered a multi-modal graded hub