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How moral decision-making occurs, matures over time and relates to behaviour is complex. To develop a full picture of moral decision-making, moral development and moral behaviour it is necessary to understand: (a) how real-time moral decisions are made (including relevant social and contextual factors), (b) what processes are required to develop to enable mature moral decisions, (c) how these processes develop over time, and (d) how moral decisions relate to behaviour. In this paper, psychological and social neuroscience theories of moral decision-making and development are briefly reviewed, as is the development of relevant component processes. Various component processes and factors are seen as required for moral decision-making and development, yet there is no comprehensive framework incorporating these components into one explanation of how real-time moral decisions are made and mature. In this paper, we integrated these components into a new framework based on social information processing (SIP) theory. Situational factors, and how both cognitive and affective processes guide moral decisions was incorporated into the Social Information Processing-Moral Decision-Making (SIP-MDM) framework, drawing upon theories and findings from developmental psychology and social neuroscience. How this framework goes beyond previous SIP models was outlined, followed by a discussion of how it can explain both real-time moral decisions and moral development. We concluded with how the SIP-MDM framework could be used to guide future research and theory development in this area. [ABSTRACT FROM AUTHOR]

Abstract The application of machine learning methods to neuroimaging data has fundamentally altered the field of cognitive neuroscience. Future progress in understanding brain function using these methods will require addressing a number of key methodological and interpretive challenges. Because these challenges often remain unseen and metaphorically “haunt” our efforts to use these methods to understand the brain, we refer to them as “ghosts”. In this paper, we describe three such ghosts, situate them within a more general framework from philosophy of science, and then describe steps to address them. The first ghost arises from difficulties in determining what information machine learning classifiers use for decoding. The second ghost arises from the interplay of experimental design and the structure of information in the brain – that is, our methods embody implicit assumptions about information processing in the brain, and it is often difficult to determine if those assumptions are satisfied. The third ghost emerges from our limited ability to distinguish information that is merely decodable from the brain from information that is represented and used by the brain. Each of the three ghosts place limits on the interpretability of decoding research in cognitive neuroscience. There are no easy solutions, but facing these issues squarely will provide a clearer path to understanding the nature of representation and computation in the human brain. Highlights • Provides a philosophical framework for thinking about applications of machine learning to cognitive neuroscience datasets. • Discussion of current challenges for neural decoding research. • Gives suggestions about how to address contemporary challenges in decoding research. [ABSTRACT FROM AUTHOR]

Abstract Does disruption of prefrontal cortical activity using transcranial magnetic stimulation (TMS) impair visual metacognition? An initial study supporting this idea (Rounis, Maniscalco, Rothwell, Passingham, & Lau, 2010) motivated an attempted replication and extension (Bor, Schwartzman, Barrett, & Seth, 2017). Bor et al. failed to replicate the initial study, concluding that there was not good evidence that TMS to dorsolateral prefrontal cortex impairs visual metacognition. This failed replication has recently been critiqued by some of the authors of the initial study (Ruby, Maniscalco, & Peters, 2018). Here we argue that these criticisms are misplaced. In our response, we encounter some more general issues concerning good practice in replication of cognitive neuroscience studies, and in setting criteria for excluding data when employing statistical analyses like signal detection theory. We look forward to further studies investigating the role of prefrontal cortex in metacognition, with increasingly refined methodologies, motivated by the discussions in this series of papers. [ABSTRACT FROM AUTHOR]

A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This “correspondence problem” affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data. [ABSTRACT FROM AUTHOR]

Granger-Geweke causality (GGC) is a powerful and popular method for identifying directed functional (‘causal’) connectivity in neuroscience. In a recent paper, Stokes and Purdon (2017b) raise several concerns about its use. They make two primary claims: (1) that GGC estimates may be severely biased or of high variance, and (2) that GGC fails to reveal the full structural/causal mechanisms of a system. However, these claims rest, respectively, on an incomplete evaluation of the literature, and a misconception about what GGC can be said to measure. Here we explain how existing approaches resolve the first issue, and discuss the frequently-misunderstood distinction between functional and effective neural connectivity which underlies Stokes and Purdon's second claim. [ABSTRACT FROM AUTHOR]

In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b -value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal–Tanner gradients on a 3 T Siemens Verio, a 3 T Siemens Connectome Skyra or a 7 T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement. [ABSTRACT FROM AUTHOR]

The goal of this paper is to establish the truth of the following conditional: if a global workspace theory of phenomenal consciousness is correct, and is fully reductive in nature, then we should stop asking questions about consciousness in nonhuman animals—not because those questions are too hard to answer, but because there are no substantive facts to discover. The argument in support of this conditional turns on the idea that while global broadcasting is all-or-nothing in the human mind, it is framed in terms that imply gradations across species. Yet our concept of phenomenal consciousness doesn’t permit mental states to be to some degree conscious. Before getting to that argument, however, and in order to motivate the subsequent discussion, some of the virtues of global workspace theory are displayed. [ABSTRACT FROM AUTHOR]

Cognitive and affective penetration of perception refers to the influence that higher mental states such as beliefs and emotions have on perceptual systems. Psychological and neuroscientific studies appear to show that these states modulate the visual system at the visuomotor, attentional, and late levels of processing. However, empirical evidence showing that similar consequences occur in early stages of visual processing seems to be scarce. In this paper, I argue that psychological evidence does not seem to be either sufficient or necessary to argue in favour of or against the cognitive penetration of perception in either late or early vision. In order to do that we need to have recourse to brain imaging techniques. Thus, I introduce a neuroscientific study and argue that it seems to provide well-grounded evidence for the cognitive penetration of early vision in face perception. I also examine and reject alternative explanations to my conclusion. [ABSTRACT FROM AUTHOR]

One of the most specific but also challenging properties of the brain is its topographic organization into distinct modules or cortical areas. In this paper, we first review the concept of topographic organization and its historical development. Next, we provide a critical discussion of the current definition of what constitutes a cortical area, why the concept has been so central to the field of neuroimaging and the challenges that arise from this view. A key aspect in this discussion is the issue of spatial scale and hierarchy in the brain. Focusing on in-vivo brain parcellation as a rapidly expanding field of research, we highlight potential limitations of the classical concept of cortical areas in the context of multi-modal parcellation and propose a revised interpretation of cortical areas building on the concept of neurobiological atoms that may be aggregated into larger units within and across modalities. We conclude by presenting an outlook on the implication of this revised concept for future mapping studies and raise some open questions in the context of brain parcellation. [ABSTRACT FROM AUTHOR]

The three goals of this paper are: 1) to evaluate the improvements in technology for increasing magnetic flux density (magnetic field) to 14 T in the next few years and eventually to 20 T; 2) to highlight neuroscience opportunities enabled by these advances; and, 3) to evaluate the physiological and biophysical effects associated with MRI at very high performance levels. Substantial recent advances in magnet technology including superconductor developments enable neuroscience goals that are not obtainable at contemporary magnetic fields. Ten areas of brain neuroscience include potential improvements in resolution for functional MRI(BOLD), diffusion weighted MRI, tractography, susceptibility weighted MR, neuronal architecture patterns related to human behavior, proton spectroscopy of small brain biochemicals, chemical exchange saturation transfer (CEST), dynamic contrast enhanced MRI, brain energy metabolism using 13 C, 17 O, and 31 P; and brain electrolyte physiology using 23 Na, 35 Cl, and 39 K. Physiological phenomena and safety aspects include: absorbed RF power, acoustic sound pressure levels, induced electric fields, Lorentz forces, magnetohydrodynamic forces, and biophysical phenomena in cells and tissues. Where feasible, effects are quantified for magnetic fields beyond 7 T with the conclusion that there are no foreseen barriers either in the technical or human safety aspects of brain MRI and MRS at fields up to 20 T. This conclusion is conditioned on results of recommended experiments to verify the predicted level of physiological effects beyond 9.4 T. This technology is predicted to enable quantification of biochemical components of the functioning brain not detectable heretofore. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide, and typically involves a robust immune response. Although a great deal of preclinical research has been conducted to identify an effective treatment, all phase III clinical trials have been unsuccessful to date. These translational shortcomings are in part due to a failure to recognize and account for the heterogeneity of TBI, including how extracranial factors can influence the aftermath of TBI. For example, most preclinical studies have utilized isolated TBI models in young adult males, while clinical trials typically involve highly heterogeneous patient populations (e.g., different mechanisms of injury, a range of ages, presence of polytrauma or infection). This paper will review the current, albeit limited literature related to how TBI is affected by common concomitant immunological stressors. In particular, discussion will focus on whether extracranial trauma (i.e., polytrauma), infection, and age/immunosenescence can influence TBI pathophysiology, and thereby may result in a different brain injury than what would have occurred in an isolated TBI. It is concluded that these immunological stressors are all likely to be TBI modifiers that should be further studied and could impact translational treatment strategies. [ABSTRACT FROM AUTHOR]

Neuroscience has a long history of inferring brain function by examining the relationship between brain injury and subsequent behavioral impairments. The primary advantage of this method over correlative methods is that it can tell us if a certain brain region is necessary for a given cognitive function. In addition, lesion-based analyses provide unique insights into clinical deficits. In the last decade, statistical voxel-based lesion behavior mapping (VLBM) emerged as a powerful method for understanding the architecture of the human brain. This review illustrates how VLBM improves our knowledge of functional brain architecture, as well as how it is inherently limited by its mass-univariate approach. A wide array of recently developed methods appear to supplement traditional VLBM. This paper provides an overview of these new methods, including the use of specialized imaging modalities, the combination of structural imaging with normative connectome data, as well as multivariate analyses of structural imaging data. We see these new methods as complementing rather than replacing traditional VLBM, providing synergistic tools to answer related questions. Finally, we discuss the potential for these methods to become established in cognitive neuroscience and in clinical applications. [ABSTRACT FROM AUTHOR]

Diffusion tensor imaging (DTI) is a promising approach for investigating the white matter microstructure of the spinal cord. However, it suffers from severe susceptibility, physiological, and instrumental artifacts present in the cord. Retrospective correction techniques are popular approaches to reduce these artifacts, because they are widely applicable and do not increase scan time. In this paper, we present a novel outlier rejection approach ( reliability masking ) which is designed to supplement existing correction approaches by excluding irreversibly corrupted and thus unreliable data points from the DTI index maps. Then, we investigate how chains of retrospective correction techniques including (i) registration, (ii) registration and robust fitting, and (iii) registration, robust fitting, and reliability masking affect the statistical power of a previously reported finding of lower fractional anisotropy values in the posterior column and lateral corticospinal tracts in cervical spondylotic myelopathy (CSM) patients. While established post-processing steps had small effect on the statistical power of the clinical finding (slice-wise registration: −0.5%, robust fitting: +0.6%), adding reliability masking to the post-processing chain increased it by 4.7%. Interestingly, reliability masking and registration affected the t-score metric differently: while the gain in statistical power due to reliability masking was mainly driven by decreased variability in both groups, registration slightly increased variability. In conclusion, reliability masking is particularly attractive for neuroscience and clinical research studies, as it increases statistical power by reducing group variability and thus provides a cost-efficient alternative to increasing the group size. [ABSTRACT FROM AUTHOR]

Behavioral neuroscience has greatly informed how we understand the formation, persistence, and plasticity of memory. Research has demonstrated that memory reactivation can induce a labile period, during which previously consolidated memories are sensitive to change, and in need of restabilization. This process is known as reconsolidation. Such findings have advanced not only our basic understanding of memory processes, but also hint at the prospect of harnessing these insights for the development of a new generation of treatments for disorders of emotional memory. However, even in simple experimental models, the conditions for inducing memory reconsolidation are complex: memory labilization appears to result from the interplay of learning history, reactivation, and also individual differences, posing difficulties for the translation of basic experimental research into effective clinical interventions. In this paper, we review a selection of influential animal and human research on memory reconsolidation to illustrate key insights these studies afford. We then consider how these findings can inform the development of new treatment approaches, with a particular focus on the transition of memory from reactivation, to reconsolidation, to new memory formation, as well as highlighting possible limitations of experimental models. If the challenges of translational research can be overcome, and if reconsolidation-based procedures become a viable treatment option, then they would be one of the first mental health treatments to be directly derived from basic neuroscience research. This would surely be a triumph for the scientific study of mind and brain. [ABSTRACT FROM AUTHOR]

A recent methodology – namely, the belief update task – used in the study of unrealistic optimism has provided a mechanistic account of how people maintain positive illusions about their future in the face of disconfirming evidence. This methodology has been used in a series of neuroscientific studies and neural moderators of unrealistic optimism have been established. A subsequent critique of the belief update task by Shah et al. (2016) has cast doubt over the validity of these findings however, with the authors instead suggesting that apparent optimistic belief updating is in fact a statistical artefact resulting from a flawed methodology. Specifically, Shah et al. assert that the inclusion of positive events in the belief update task can help test the validity of the optimistic account of belief updating, while proponents have suggested that caution should be taken when adapting this task to study positive life events because there is a lack of accurate information regarding the likely frequency of such events. Using a subset of the life events used by Shah et al., the current paper demonstrates that optimistic belief updating should still be observed when positive life events are included in the belief update task. [ABSTRACT FROM AUTHOR]

Over the past few years, the prediction error minimization (PEM) framework has increasingly been gaining ground throughout the cognitive sciences. A key issue dividing proponents of PEM is how we should conceptualize the relation between brain, body and environment. Clark advocates a version of PEM which retains, at least to a certain extent, his prior commitments to Embodied Cognition and to the Extended Mind Hypothesis. Hohwy, by contrast, presents a sustained argument that PEM actually rules out at least some versions of Embodied and Extended cognition. The aim of this paper is to facilitate a constructive debate between these two competing alternatives by explicating the different theoretical motivations underlying them, and by homing in on the relevant issues that may help to adjudicate between them. [ABSTRACT FROM AUTHOR]

This paper describes the data repository for the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) initial study cohort. The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large (approximately N = 700), cross-sectional adult lifespan (18–87 years old) population-based sample. The study is designed to characterise age-related changes in cognition and brain structure and function, and to uncover the neurocognitive mechanisms that support healthy cognitive ageing. The database contains raw and preprocessed structural MRI, functional MRI (active tasks and resting state), and MEG data (active tasks and resting state), as well as derived scores from cognitive behavioural experiments spanning five broad domains (attention, emotion, action, language, and memory), and demographic and neuropsychological data. The dataset thus provides a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition, and providing a testbed for novel analyses of multi-modal neuroimaging data. [ABSTRACT FROM AUTHOR]

A targeted effort to identify novel neurotrophic factors for midbrain dopaminergic neurons resulted in the isolation of GDNF (glial cell line-derived neurotrophic factor) from the supernatant of a rat glial cell line in 1993. Over two decades and 1200 papers later, the GDNF ligand family and their different receptor systems are now recognized as one of the major neurotrophic networks in the nervous system, important for the development, maintenance and function of a variety of neurons and glial cells. The many ways in which the four members of the GDNF ligand family can signal and function allow these factors to take part in the control of multiple types of processes, from neuronal survival to axon guidance and synapse formation in the developing nervous system, to synaptic function and regenerative responses in the adult. In this review, we will briefly summarize basic aspects of GDNF signaling mechanisms and receptor systems and then review our current knowledge of the physiology of GDNF activities in the central nervous system, with an eye to its relevance for neurodegenerative and neuropsychiatric diseases. [ABSTRACT FROM AUTHOR]

There is a real need in the neuroscience community for efficient tools to compare Diffusion Tensor Magnetic Resonance Imaging across cohorts of subjects. Most studies focus on the comparison of scalar images such as fractional anisotropy or mean diffusivity. Although different statistical frameworks have been proposed to compare the whole diffusion tensor information, they are still seldom used in neuroimaging studies. In this paper, we investigate on both simulated and real data whether there is a real added value of considering the whole tensor information for conducting voxel-based group studies. Then, we compare two statistical tests dedicated to tensor, namely the Cramér test and a tensor-based extension of the General Linear Model (GLM), the latter presenting the advantage to account for covariates. We also evaluate the impact of different metrics (Euclidean, Log-Euclidean and affine-invariant Riemannian metrics) for estimating the GLM parameters. Finally, we address the problem of interpreting the change detection maps obtained by tensor-based methods by proposing a way to characterize each of the detected clusters according to several scalar indices. Our study suggests that if there is no prior assumption about the nature of the expected changes, it is really preferable to use tensor-based rather than scalar-based statistical analysis. The Cramér test can advantageously be used when no confounding variable hampers the group comparison, otherwise the GLM should be considered. Finally, the different metrics show similar performance in the real scenario, with a significant computational overhead for the Riemannian framework. [ABSTRACT FROM AUTHOR]

In this paper, we address issues raised by Tierney and Hart (Assessing Complex Cognitive Functioning Requires Multiple Tasks) in response to our recently published findings showing that less advantageous decision-making on the Iowa Gambling Task (IGT) was associated with enhanced right ventral striatal dopamine response to intravenous amphetamine (Oswald et al., 2015). We agree with the overall premise of the paper, which was that decision-making involves multiple components, which may not be tapped by a single measure. While Tierney and Hart also bring up some important issues related to the construct validity of the IGT, we suggest that they are failing to put the findings within the context of the growing body of research that has highlighted the role of DA function in risk-taking behavior across species using a variety of tasks. It should also be noted that it was not our goal to “cover all bases” within the context of a single study. Nevertheless, we appreciate the discussion, which we believe highlights the need for further empirical refinement of the construct to facilitate detection and understanding of the differential role that specific molecular mechanisms may play in the component processes. [ABSTRACT FROM AUTHOR]

Simultaneous EEG-fMRI combines two powerful neuroimaging techniques, but the EEG signal suffers from severe artifacts in the MRI environment that are difficult to remove. These are the MR scanning artifact and the blood-pulsation artifact — strategies to remove them are a topic of ongoing research. Additionally large, unsystematic artifacts are produced across the full frequency spectrum by the magnet's helium pump (and ventilator) systems which are notoriously hard to remove. As a consequence, experimenters routinely deactivate the helium pump during simultaneous EEG-fMRI acquisitions which potentially risks damaging the MRI system and necessitates more frequent and expensive helium refills. We present a novel correction method addressing both helium pump and ballisto-cardiac (BCG) artifacts, consisting of carbon-wire loops (CWL) as additional sensors to accurately track unpredictable artifacts related to subtle movements in the scanner, and an EEGLAB plugin to perform artifact correction. We compare signal-to-noise metrics of EEG data, corrected with CWL and three conventional correction methods, for helium pump off and on measurements. Because the CWL setup records signals in real-time, it fits requirements of applications where immediate correction is necessary, such as neuro-feedback applications or stimulation time-locked to specific sleep oscillations. The comparison metrics in this paper relate to: (1) the EEG signal itself, (2) the “eyes open vs. eyes closed” effect, and (3) an assessment of how the artifact corrections impacts the ability to perform meaningful correlations between EEG alpha power and the BOLD signal. Results show that the CWL correction corrects for He pump artifact and also produces EEG data more comparable to EEG obtained outside the magnet than conventional post-processing methods. [ABSTRACT FROM AUTHOR]

The quantitative analysis of diffusion tensor image (DTI) data has attracted increasing attention in recent decades for studying white matter (WM) integrity and development. Among the current DTI analysis methods, tract-based spatial statistics (TBSS), as a pioneering approach for the voxelwise analysis of DTI data, has gained a lot of popularity due to its user-friendly framework. However, in recent years, the reliability and interpretability of TBSS have been challenged by several works, and several improvements over the original TBSS pipeline have been suggested. In this paper, we propose a new DTI statistical analysis method, named tractography atlas-based spatial statistics (TABSS). It doesn't rely on the accurate alignment of fractional anisotropy (FA) images for population analysis and gets rid of the skeletonization procedures of TBSS, which have been indicated as the major sources of error. Furthermore, TABSS improves the interpretability of results by directly reporting the resulting statistics on WM tracts, waiving the need of a WM atlas in the interpretation of the results. The feasibility of TABSS was evaluated in an example study to show age-related FA alternation pattern of healthy human brain. Through this preliminary study, it is validated that TABSS can provide detailed statistical results in a comprehensive and easy-to-understand way. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data. [ABSTRACT FROM AUTHOR]

In MRI studies, linear multi-variate methods are often employed to identify regions or connections that are affected due to disease or normal aging. Such linear models inherently assume that there is a single, homogeneous abnormality pattern that is present in all affected individuals. While kernel-based methods can implicitly model a non-linear effect, and therefore the heterogeneity in the affected group, extracting and interpreting information about affected regions is difficult. In this paper, we present a method that explicitly models and captures heterogeneous patterns of change in the affected group relative to a reference group of controls. For this purpose, we use the Mixture-of-Experts (MOE) framework, which combines unsupervised modeling of mixtures of distributions with supervised learning of classifiers. MOE approximates the non-linear boundary between the two groups with a piece-wise linear boundary, thus allowing discovery of multiple patterns of group differences. In the case of patient/control comparisons, each such pattern aims to capture a different dimension of a disease, and hence to identify patient subgroups. We validated our model using multiple simulation scenarios and performance measures. We applied this method to resting state functional MRI data from the Baltimore Longitudinal Study of Aging, to investigate heterogeneous effects of aging on brain function in cognitively normal older adults (> 85 years) relative to a reference group of normal young to middle-aged adults (< 60 years). We found strong evidence for the presence of two subgroups of older adults, with similar age distributions in each subgroup, but different connectivity patterns associated with aging. While both older subgroups showed reduced functional connectivity in the Default Mode Network (DMN), increases in functional connectivity within the pre-frontal cortex as well as the bilateral insula were observed only for one of the two subgroups. Interestingly, the subgroup showing this increased connectivity (unlike the other subgroup) was, cognitively similar at baseline to the young and middle-aged subjects in two of seven cognitive domains, and had a faster rate of cognitive decline in one of seven domains. These results suggest that older individuals whose baseline cognitive performance is comparable to that of younger individuals recruit their “cognitive reserve” later in life, to compensate for reduced connectivity in other brain regions. [ABSTRACT FROM AUTHOR]

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n = 90), which cover ages 4–82 years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation. [ABSTRACT FROM AUTHOR]

Neuroimaging data collection is inherently expensive. Maximizing the return on investment in neuroimaging studies requires that neuroimaging data be re-used whenever possible. In an effort to further scientific knowledge, the COINS Data Exchange (DX) ( http://coins.mrn.org/dx ) aims to make data sharing seamless and commonplace. DX takes a three-pronged approach towards improving the overall state of data sharing within the neuroscience community. The first prong is compiling data into one location that has been collected from all over the world in many different formats. The second prong is curating the data so that it can be stored in one consistent format and so that data QA/QC measures can be assured. The third prong is disseminating the data so that it is easy to consume and straightforward to interpret. This paper explains the concepts behind each prong and describes some challenges and successes that the Data Exchange has experienced. [ABSTRACT FROM AUTHOR]

Functional connectivity provides an informative and powerful framework for exploring brain organization. Despite this, few statistical methods are available for the accurate estimation of dynamic changes in functional network architecture. To date, the majority of existing statistical techniques have assumed that connectivity structure is stationary, which is in direct contrast to emerging data that suggests that the strength of connectivity between regions is variable over time. Therefore, the development of statistical methods that enable exploration of dynamic changes in functional connectivity is currently of great importance to the neuroscience community. In this paper, we introduce the ‘Multiplication of Temporal Derivatives’ (MTD) and then demonstrate the utility of this metric to: (i) detect dynamic changes in connectivity using data from a novel state-switching simulation; (ii) accurately estimate graph structure in a previously-described ‘ground-truth’ simulated dataset; and (iii) identify task-driven alterations in functional connectivity. We show that the MTD is more sensitive than existing sliding-window methods in detecting dynamic alterations in connectivity structure across a range of correlation strengths and window lengths in simulated data. In addition to the temporal precision offered by MTD, we demonstrate that the metric is also able to accurately estimate stationary network structure in both simulated and real task-based data, suggesting that the method may be used to identify dynamic changes in network structure as they evolve through time. [ABSTRACT FROM AUTHOR]

Neuroscientific data is typically analyzed based on the behavioral response of the participant. However, the errors made may or may not be in line with the neural processing. In particular in experiments with time pressure or studies where the threshold of perception is measured, the error distribution deviates from uniformity due to the structure in the underlying experimental set-up. When we base our analysis on the behavioral labels as usually done, then we ignore this problem of systematic and structured (non-uniform) label noise and are likely to arrive at wrong conclusions in our data analysis. This paper contributes a remedy to this important scenario: we present a novel approach for a) measuring label noise and b) removing structured label noise. We demonstrate its usefulness for EEG data analysis using a standard d2 test for visual attention (N = 20 participants). [ABSTRACT FROM AUTHOR]

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis. [ABSTRACT FROM AUTHOR]

Confidence judgments are pivotal in the performance of daily tasks and in many domains of scientific research including the behavioral sciences, psychology and neuroscience. Positive resolution i.e., the positive correlation between choice-correctness and choice-confidence is a critical property of confidence judgments, which justifies their ubiquity. In the current paper, we study the mechanism underlying confidence judgments and their resolution by investigating the source of the inputs for the confidence-calculation. We focus on the intriguing debate between two families of confidence theories. According to single stage theories, confidence is based on the same information that underlies the decision (or on some other aspect of the decision process), whereas according to dual stage theories, confidence is affected by novel information that is collected after the decision was made. In three experiments, we support the case for dual stage theories by showing that post-choice perceptual availability manipulations exert a causal effect on confidence-resolution in the decision followed by confidence paradigm. These finding establish the role of RT2, the duration of the post-choice information-integration stage, as a prime dependent variable that theories of confidence should account for. We then present a novel list of robust empirical patterns (‘hurdles’) involving RT2 to guide further theorizing about confidence judgments. Finally, we present a unified computational dual stage model for choice, confidence and their latencies namely, the collapsing confidence boundary model (CCB). According to CCB, a diffusion-process choice is followed by a second evidence-integration stage towards a stochastic collapsing confidence boundary. Despite its simplicity, CCB clears the entire list of hurdles. [ABSTRACT FROM AUTHOR]

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’. However, characterising this activity in an interpretable manner is still a very open problem. In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable. We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects. [ABSTRACT FROM AUTHOR]

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain's white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come. [ABSTRACT FROM AUTHOR]

In recent years functional neuroimaging techniques such as fMRI, MEG, EEG and PET have provided researchers with a wealth of information on human brain function. However none of these modalities can measure directly either the neuro-electrical or neuro-chemical processes that mediate brain function. This means that metrics directly reflecting brain ‘activity’ must be inferred from other metrics (e.g. magnetic fields (MEG) or haemodynamics (fMRI)). To overcome this limitation, many studies seek to combine multiple complementary modalities and an excellent example of this is the combination of MEG (which has high temporal resolution) with fMRI (which has high spatial resolution). However, the full potential of multi-modal approaches can only be truly realised in cases where the relationship between metrics is known. In this paper, we explore the relationship between measurements made using fMRI and MEG. We describe the origins of the two signals as well as their relationship to electrophysiology. We review multiple studies that have attempted to characterise the spatial relationship between fMRI and MEG, and we also describe studies that exploit the rich information content of MEG to explore differing relationships between MEG and fMRI across neural oscillatory frequency bands. Monitoring the brain at “rest” has become of significant recent interest to the neuroimaging community and we review recent evidence comparing MEG and fMRI metrics of functional connectivity. A brief discussion of the use of magnetic resonance spectroscopy (MRS) to probe the relationship between MEG/fMRI and neurochemistry is also given. Finally, we highlight future areas of interest and offer some recommendations for the parallel use of fMRI and MEG. [ABSTRACT FROM AUTHOR]

Over the last decade, the propensity for young people to take risks has been a particular focus of neuroscientific inquiries into human development. Taking population-level data about teenagers' involvement in drinking, smoking, dangerous driving and unprotected sex as indicative, a consensus has developed about the association between risk-taking and the temporal misalignment in the development of reward-seeking and executive regions of the brain. There are epistemological difficulties in this theory. Risk, the brain, and adolescence are different kinds of objects, and bringing them into the same frame for analysis is not unproblematic. In particular, risk is inextricably contextual and value-driven. The assessment of adolescent behaviour and decision-making as 'sub-optimal', and the implication that the developmental schedule of the teenage brain is dysfunctional, is also reassessed in terms of evolutionary development of the individual, the family and the human community. The paper proposes a view of adolescent development as adaptive, and a focus on young people's capacities in the profile of the needs of the community as a whole. [ABSTRACT FROM AUTHOR]

The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount of data with high-quality voxel-level annotations. However, due to the limited time clinicians can provide for the cumbersome task of manual image segmentation, semi-supervised medical image segmentation methods present an alternative solution as they require only a few labeled samples for training. In this paper, we propose a novel semi-supervised segmentation framework that combines improved mean teacher and adversarial network. Specifically, our framework consists of (i) a student model and a teacher model for segmenting the target and generating the signed distance maps of object surfaces, and (ii) a discriminator network for extracting hierarchical features and distinguishing the signed distance maps of labeled and unlabeled data. Besides, based on two different adversarial learning processes, a multi-scale feature consistency loss derived from the student and teacher models is proposed, and a shape-aware embedding scheme is integrated into our framework. We evaluated the proposed method on the public brain lesion datasets from ISBI 2015, ISLES 2015, and BRATS 2018 for the multiple sclerosis lesion, ischemic stroke lesion, and brain tumor segmentation respectively. Experiments demonstrate that our method can effectively leverage unlabeled data while outperforming the supervised baseline and other state-of-the-art semi-supervised methods trained with the same labeled data. The proposed framework is suitable for joint training of limited labeled data and additional unlabeled data, which is expected to reduce the effort of obtaining annotated images. [ABSTRACT FROM AUTHOR]

• Value of fMRI localizer protocols to NHP & cross-species neuroscience research. • Commonly used or novel localizers within NHPs, & keys implementation criteria. • Open access repository of well-established localizer on PRIME-RE platform. Functional localizers are invaluable as they can help define regions of interest, provide cross-study comparisons, and most importantly, allow for the aggregation and meta-analyses of data across studies and laboratories. To achieve these goals within the non-human primate (NHP) imaging community, there is a pressing need for the use of standardized and validated localizers that can be readily implemented across different groups. The goal of this paper is to provide an overview of the value of localizer protocols to imaging research and we describe a number of commonly used or novel localizers within NHPs, and keys to implement them across studies. As has been shown with the aggregation of resting-state imaging data in the original PRIME-DE submissions, we believe that the field is ready to apply the same initiative for task-based functional localizers in NHP imaging. By coming together to collect large datasets across research group, implementing the same functional localizers, and sharing the localizers and data via PRIME-DE, it is now possible to fully test their robustness, selectivity and specificity. To do this, we reviewed a number of common localizers and we created a repository of well-established localizer that are easily accessible and implemented through the PRIME-RE platform. [ABSTRACT FROM AUTHOR]

• Strategies to maximize rigor and reproducibility in rodent behavioral assays. • Replicability of translational rodent models as preclinical research tools. • Key methods for one example, the novel object recognition cognitive test. • Extensive literature cited will refer the reader to detailed appropriate methods. Behavioral neuroscience research incorporates the identical high level of meticulous methodologies and exacting attention to detail as all other scientific disciplines. To achieve maximal rigor and reproducibility of findings, well-trained investigators employ a variety of established best practices. Here we explicate some of the requirements for rigorous experimental design and accurate data analysis in conducting mouse and rat behavioral tests. Novel object recognition is used as an example of a cognitive assay which has been conducted successfully with a range of methods, all based on common principles of appropriate procedures, controls, and statistics. Directors of Rodent Core facilities within Intellectual and Developmental Disabilities Research Centers contribute key aspects of their own novel object recognition protocols, offering insights into essential similarities and less-critical differences. Literature cited in this review article will lead the interested reader to source papers that provide step-by-step protocols which illustrate optimized methods for many standard rodent behavioral assays. Adhering to best practices in behavioral neuroscience will enhance the value of animal models for the multiple goals of understanding biological mechanisms, evaluating consequences of genetic mutations, and discovering efficacious therapeutics. [ABSTRACT FROM AUTHOR]