Search

Highlights • Considers the social dimensions/implications for neuroimaging research in LMICs. • Technologies for enhancing understandings of ill-health are theory-laden. • Such technologies are theory-generating. • Studies of mental ill-health can introduce new idioms for understanding subjective distress. • Researchers must anticipate and potentially mitigate the social implications of neuroimaging., Drawing on insights from sociology, anthropology, and the history of science and medicine, this paper considers some of the social dimensions and implications for neuroimaging research undertaken within low- and middle-income countries (LMICs). It highlights three key inter-connected issues: (1) technologies for enhancing understandings of ill-health are theory-laden; (2) such technologies are theory-generating; and (3) studies of mental ill-health can also introduce new idioms for understanding subjective distress. The paper unpacks and explores these issues. It argues that the use of neuroimaging technologies in population research has the potential to contribute to solidifying – or even introducing – a biological (and specifically brain-based) understanding of mental ill-health within the communities under study. Examples from studies of neuroscience and society in various high-income countries (HICs) where neuroimaging is popular within public discourse illustrates how this can happen, and with what effects. The social dimensions and implications of neuroimaging are issues that all researchers using these technologies need to not only anticipate, but also explicitly plan for (and potentially seek to mitigate). Without adequate consideration, neuroimaging research carries with it particular risks in relation to extending the epistemological coloniality associated with HIC-sponsored studies conducted within LMIC settings.

Spike and wave discharges are the main electrographic characteristic of a number of epileptic brain disorders including childhood absence epilepsy and photosensitive epilepsy. The basic dynamic mechanism that underlies the occurrence of these abnormal electrical patterns in the brain is not well understood. The current paper aims to provide a dynamic explanation for features and generation mechanism of spike and wave discharges in the brain. The main proposition of this study is that epileptic seizures could be interpreted as a resonance phenomenon rather than a limit cycle behavior. To shows this, a revised version of Jansen-Rit neural mass model is employed. The system can switch between monostable and bistable regimes, which are considered in this paper as wake and sleep states of the brain, respectively. In particular, it is shown that, in monostable region, the model can depict the alpha rhythm and alpha rhythm suppression due to mental activity. Frequency responses of the model near the bistable regime demonstrate that high amplitude harmonic excitation may lead to spike and wave like oscillations. Based on the computational results and the concept of stochastic resonance, a model for absence epilepsy is presented which can simulate spontaneous transitions between ictal and interictal states. Finally, it is shown that spike and wave discharges during epileptic seizures can be explained as a resonance phenomenon in a nonlinear system.

The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses-such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI. (C) 2015 The Authors. Published by Elsevier Inc.

There is increasing interest in the use of edited proton magnetic resonance spectroscopy for the detection of GABA in the human brain. At a recent meeting held at Cardiff University, a number of spectroscopy groups met to discuss the acquisition, analysis and interpretation of GABA-edited MR spectra. This paper aims to set out the issues discussed at this meeting, reporting areas of consensus around parameters and procedures in the field and highlighting those areas where differences remain. It is hoped that this paper can fulfill two needs, providing a summary of the current ‘state-of-the-art’ in the field of GABA-edited MRS at 3 T using MEGA-PRESS and a basic guide to help researchers new to the field to avoid some of the pitfalls inherent in the acquisition and processing of edited MRS for GABA.

Papers from four different groups were published in 1993 demonstrating the ability of functional near infrared spectroscopy (fNIRS) to non-invasively measure hemoglobin concentration responses to brain function in humans. This special issue commemorates the first 20years of fNIRS research. The 9 reviews and 49 contributed papers provide a comprehensive survey of the exciting advances driving the field forward and of the myriad of applications that will benefit from fNIRS.

There have now been roughly 130,000 papers on fMRI. While these have clearly contributed to our understanding of the functional anatomy of the human brain, it is less clear that they have changed the way in which we think about the brain. The issue, in other words, is whether they have established new principles about how the brain works. In this paper we offer as an example one new principle, partly to lay down the criteria that are required for establishing a new principle, and partly to encourage others to offer other principles. Our example concerns the flexible flow of information through the cortex that must occur according to the demands of the task or current context. We suggest that this flexibility is achieved by feedback connections from the prefrontal and parietal cortex, and that these include connections to sensory and motor areas. However, the nature of the selective effect differs. The parietal cortex can select both within and across processing streams. By across streams we mean that it can have the same influence on different streams, for example the dorsal and ventral visual systems. However, only the prefrontal cortex can also select between processing streams. The difference between the prefrontal and parietal effects is due to their different positions within the processing hierarchy.

Despite the relationship between brain structure and function being of fundamental interest in cognitive neuroscience, the relationship between the brain's white matter, measured using fractional anisotropy (FA), and the functional magnetic resonance imaging (fMRI) blood oxygen level dependent (BOLD) response is poorly understood. A systematic review of literature investigating the association between FA and fMRI BOLD response was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed and Web of Knowledge databases were searched up until 22.04.2016 using a predetermined set of search criteria. The search identified 363 papers, 28 of which met the specified inclusion criteria. Positive relationships were mainly observed in studies investigating the primary sensory and motor systems and in resting state data. Both positive and negative relationships were seen in studies using cognitive tasks. This systematic review suggests that there is a relationship between FA and the fMRI BOLD response and that the relationship is task and region dependent. Behavioural and/or clinical variables were shown to be essential in interpreting the relationships between imaging measures. The results highlight the heterogeneity in the methods used across papers in terms of fMRI task, population investigated and data analysis techniques. Further investigation and replication of current findings are required before definitive conclusions can be drawn.

In 2001, we published a paper on the representation of faces and objects in ventral temporal cortex that introduced a new method for fMRI analysis, which subsequently came to be called multivariate pattern analysis (MVPA). MVPA now refers to a diverse set of methods that analyze neural responses as patterns of activity that reflect the varying brain states that a cortical field or system can produce. This paper recounts the circumstances and events that led to the original study and later developments and innovations that have greatly expanded this approach to fMRI data analysis, leading to its widespread application.

The human insula has been parcellated on the basis of resting state functional connectivity and diffusion tensor imaging. Little is known about the organization of the insula when involved in active tasks. We explored this issue using a novel meta-analytic clustering approach. We queried the BrainMap database asking for papers involving normal subjects that recorded activations in the insular cortex, retrieving 1305 papers, involving 22,872 subjects and a total of 2957 foci. Data were analyzed with several different methodologies, some of which expressly designed for this work. We used meta-analytic connectivity modeling and meta-analytic clustering of data obtained from the BrainMap database. We performed cluster analysis to subdivide the insula in areas with homogeneous connectivity, and density analysis of the activated foci using Voronoi tessellation. Our results confirm and extend previous findings obtained investigating the resting state connectivity of the anterior-posterior and left-right insulae. They indicate, for the first time, that some blocks of the anterior insula play the role of hubs between the anterior and the posterior insulae, as confirmed by their activation in several different paradigms. This finding supports the view that the network to which the anterior insula belongs is related to saliency detection. The insulae of both sides can be parcellated in two clusters, the anterior and the posterior: the anterior is characterized by an attentional pattern of connectivity with frontal, cingulate, parietal, cerebellar and anterior insular highly connected areas, whereas the posterior is characterized by a more local connectivity pattern with connections to sensorimotor, temporal and posterior cingulate areas. This antero-posterior subdivision, better characterized on the right side, results sharper with the connectivity based clusterization than with the behavioral based clusterization. The circuits belonging to the anterior insula are very homogeneous and their blocks in multidimensional scaling of MACM-based profiles are in central position, whereas those belonging to the posterior insula, especially on the left, are located at the periphery and sparse, thus suggesting that the posterior circuits bear a more heterogeneous connectivity. The anterior cluster is mostly activated by cognition, whereas the posterior is mostly activated by interoception, perception and emotion.

Development of neuronal connections relies on proper neuronal activity, and it starts during the time when early preterm babies are treated in the neonatal intensive care units. While synchrony has been a key element in visual assessment of neonatal EEG signals, there has been no unambiguous definitions for synchrony, and no objective measures available for neonatal signals. Estimation of phase locking value (PLV) has been an established paradigm in adults, but many unique characteristics of the neonatal EEG have precluded its applicability in them. In the present paper, we developed the existing PLV-based methods further to be applicable for neonatal signals at two different temporal scales, oscillations and events, where the latter refers technically to quantitating phase synchrony (PS) between band-specific amplitude envelopes (bafPS). In addition, we present a measure for quantitation based on assessing cumulative proportion of time with statistically significant synchrony between the given signal pair. The paper uses real EEG examples and the prior neurobiological knowledge in the process of defining optimal parameters in each step of the procedure. Finally, we apply the method to a set of dense array EEG recordings from very early preterm babies, recorded at conceptional age of less than 30 weeks. By comparing PS and bafPS from babies without and with major cerebrovascular lesion, we show that the effects of brain lesions may be selective both in space and in frequency. These findings do by nature escape visual detection in the conventional EEG reading, however they have intriguing correlates in the current concept of how somatosensory networks are thought to develop and/or become disorganized in the early preterm babies.

This paper proposes a shift in the way researchers currently view and use transcranial brain stimulation technologies. From a neuroscience perspective, the standard application of both transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) has been mainly to explore the function of various brain regions. These tools allow for noninvasive and painless modulation of cortical tissue. In the course of studying the function of an area, many studies often report enhanced performance of a task during or following the stimulation. However, little follow-up research is typically done to further explore these effects. Approaching this growing pool of cognitive neuroscience literature with a neuroergonomics mindset (i.e., studying the brain at work), the possibilities of using these stimulation techniques for more than simply investigating the function of cortical areas become evident. In this paper, we discuss how cognitive neuroscience brain stimulation studies may complement neuroergonomics research on human performance optimization. And, through this discussion, we hope to shift the mindset of viewing transcranial stimulation techniques as solely investigatory basic science tools or possible clinical therapeutic devices to viewing transcranial stimulation techniques as interventional tools to be incorporated in applied science research and systems for the augmentation and enhancement of human operator performance.

Social evaluative threat (SET) is a potent stressor in humans that causes autonomic changes, endocrine responses, and multiple health problems. Neuroimaging has recently begun to elucidate the brain correlates of SET, but as yet little is known about the mediating cortical-brainstem pathways in humans. This paper replicates and extends findings in a companion paper (Wager et al., 2009) using an independent cohort of participants and different image acquisition parameters. Here, we focused specifically on relationships between the medial prefrontal cortex (MPFC), midbrain periaqueductal gray (PAG), and heart rate (HR). We applied multi-level path analysis to localize brain mediators of SET effects on HR and self-reported anxiety. HR responses were mediated by opposing signals in two distinct sub-regions of the MPFC—increases in rostral dorsal anterior cingulate cortex (rdACC) and de-activation in ventromedial prefrontal cortex (vmPFC). In addition, HR responses were mediated by PAG. Additional path analyses provided support for two cortical–subcortical pathways: one linking vmPFC, PAG, and HR, and another linking rdACC, thalamus, and HR. PAG responses were linked with HR changes both before and during SET, whereas cortical regions showed stronger connectivity with HR during threat. Self-reported anxiety showed a partially overlapping, but weaker, pattern of mediators, including the vmPFC, dorsomedial prefrontal cortex, and lateral frontal cortex, as well as substantial individual differences that were largely unexplained. Taken together, these data suggest pathways for the translation of social threats into both physiological and experiential responses, and provide targets for future research on the generation and regulation of emotion.

It has been well recognized that the nonlinear hemodynamic responses of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) are important and ubiquitous in a series of experimental paradigms, especially for the event-related fMRI. Although this phenomenon has been intensively studied and it has been found that the post-capillary venous expansion is an intrinsically nonlinear mechanical process, the existence of an additional neural basis for the nonlinearity has not been clearly shown. In this paper, we assessed the correlation between the electric and vascular indices by performing simultaneous electroencephalography (EEG) and fMRI recordings in humans during a series of visual stimulation (i.e., radial checkerboard). With changes of the visual stimulation frequencies (from 0.5 to 16 Hz) and contrasts (from 1% to 100%), both the event related potentials (ERPs) and hemodynamic responses show nonlinear behaviors. In particular, the mean power of the brain electric sources and the neuronal efficacies (as originally defined in the hemodynamics model [Friston et al. Neuroimage, 12, 466-477, 2000], here represent the vascular inputs) in primary visual cortex consistently show a linear correlation for all subjects. This indicates that the hemodynamic response nonlinearity found in this paper primarily reflects the nonlinearity of underlying neural activity. Most importantly, this finding underpins a nonlinear neurovascular coupling. Specifically, it is shown that the transferring function of the neurovascular coupling is likely a power transducer, which integrates the fast dynamics of neural activity into the vascular input of slow hemodynamics.

In this paper, we describe a dynamic causal model (DCM) of steady-state responses in electrophysiological data that are summarised in terms of their cross-spectral density. These spectral data-features are generated by a biologically plausible, neural-mass model of coupled electromagnetic sources; where each source comprises three sub-populations. Under linearity and stationarity assumptions, the model's biophysical parameters (e.g., post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g., local field potentials) or indirectly through some lead-field (e.g., electroencephalographic and magnetoencephalographic data). Inversion of the ensuing DCM provides conditional probabilities on the synaptic parameters of intrinsic and extrinsic connections in the underlying neuronal network. This means we can make inferences about synaptic physiology, as well as changes induced by pharmacological or behavioural manipulations, using the cross-spectral density of invasive or non-invasive electrophysiological recordings. In this paper, we focus on the form of the model, its inversion and validation using synthetic and real data. We conclude with an illustrative application to multi-channel local field potential data acquired during a learning experiment in mice.

This paper describes algorithms that can identify patterns of brain structure and function associated with Alzheimer's disease, schizophrenia, normal aging, and abnormal brain development based on imaging data collected in large human populations. Extraordinary information can be discovered with these techniques: dynamic brain maps reveal how the brain grows in childhood, how it changes in disease, and how it responds to medication. Genetic brain maps can reveal genetic influences on brain structure, shedding light on the nature-nurture debate, and the mechanisms underlying inherited neurobehavioral disorders. Recently, we created time-lapse movies of brain structure for a variety of diseases. These identify complex, shifting patterns of brain structural deficits, revealing where, and at what rate, the path of brain deterioration in illness deviates from normal. Statistical criteria can then identify situations in which these changes are abnormally accelerated, or when medication or other interventions slow them. In this paper, we focus on describing our approaches to map structural changes in the cortex. These methods have already been used to reveal the profile of brain anomalies in studies of dementia, epilepsy, depression, childhood- and adult-onset schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, fetal alcohol syndrome, Tourette syndrome, Williams syndrome, and in methamphetamine abusers. Specifically, we describe an image analysis pipeline known as cortical pattern matching that helps compare and pool cortical data over time and across subjects. Statistics are then defined to identify brain structural differences between groups, including localized alterations in cortical thickness, gray matter density (GMD), and asymmetries in cortical organization. Subtle features, not seen in individual brain scans, often emerge when population-based brain data are averaged in this way. Illustrative examples are presented to show the profound effects of development and various diseases on the human cortex. Dynamically spreading waves of gray matter loss are tracked in dementia and schizophrenia, and these sequences are related to normally occurring changes in healthy subjects of various ages.

The premotor cortex (PM) refers to human Brodmann area 6 (BA6), and often anteriorly adjacent areas BA44 and BA8 are also included. The function traditionally attributed to the PM is the preparation and the organization of movements and actions (Wise, 1985). However, with the introduction of imaging methods, which allow the neural correlates of behavioral functions to be measured online, premotor activations have frequently found in nonmotor “cognitive” domains. As these findings were difficult to interpret in light of the classical motor view, they were typically taken to reflect some kind of latent motor processes. As such, they were experimental artifacts of either nonsuppressible or deliberately chosen behavioral strategies, like verbalizing or tapping, or simply movement noise. However, nowadays the exploration of cognitive function of the human premotor cortex has become an independent field of research, supported and also inspired by results from research in the monkey. Hence, currently a diversity of concepts of PM functions coexist, referring partly to the classical motor account, partly to the scope of nonmotor functions. With particular interest in the nonmotor domain, the present paper aims to outline current concepts of human PM functions as have emerged from imaging results. Concepts apply to functional–anatomical dissociations of right vs left, medial vs lateral, rostral vs caudal, and dorsal vs ventral PM. In view of widely missing macroanatomical borders between PM subsections, these labels can serve only as a gross orientation. Moreover, the investigation of cortical architecture in living humans on the microanatomical level using specific MR protocols is still restricted to a resolution of about 500 m (Damasio, 1991). As we lack direct evidence from individual cytoarchitectonic data in conjunction with functional results, any potential correspondences between subsections of the human and the monkey PM are based on functional rather than on anatomical homologies (e.g., Rizzolatti et al., 2002). The scope of the present paper is limited. It focuses on imaging studies (in contrast to other methods in humans and in contrast to monkey studies), on the lateral (in contrast to medial) PM, and on functional trends along the dorsal– ventral axis. Concepts are illustrated by some representative findings only.

The ongoing myelination of white-matter fiber bundles plays a significant role in brain development. However, reliable and consistent identification of these bundles from infant brain MRIs is often challenging due to inherently low diffusion anisotropy, as well as motion and other artifacts. In this paper we introduce a new tool for automated probabilistic tractography specifically designed for newborn infants. Our tool incorporates prior information about the anatomical neighborhood of white-matter pathways from a training data set. In our experiments, we evaluate this tool on data from both full-term and prematurely born infants and demonstrate that it can reconstruct known white-matter tracts in both groups robustly, even in the presence of differences between the training set and study subjects. Additionally, we evaluate it on a publicly available large data set of healthy term infants (UNC Early Brain Development Program). This paves the way for performing a host of sophisticated analyses in newborns that we have previously implemented for the adult brain, such as pointwise analysis along tracts and longitudinal analysis, in both health and disease.

In our recent studies on isoflurane-anesthetized cats (Zhao et al., 2007), we reported that post-stimulus BOLD undershoot signals induced by visual stimulation were observed both within the parenchyma and at the pial surface, but that the parenchymal signals were localized to the middle of the visual cortex, improving the spatial specificity as compared to positive gradient-echo BOLD signals. Since post-stimulus BOLD undershoots have also been observed in humans, improved spatial localization within the cortex is likely to be achieved in high-resolution human fMRI studies if signals from the inner cortex and the cortical surface can be clearly differentiated. To support this argument in the context of awake humans vs. our anesthetized animal model, we cited a paper by Martin et al. which reported awake vs. urethane-anesthetized rat studies (Martin et al., 2006). Our initial interpretation of their paper was that the detailed dynamic properties were different between awake and urethane-anesthetized animals, but that the ‘general’ pattern of responses was similar, based on gamma-variate fittings (see Fig. 6 in (Martin et al., 2006). However, the choice of anesthetic agents in Martin’s (Martin et al., 2006) and our studies (Zhao et al., 2007) is different (urethane vs. isoflurane). Therefore, the reference to Martin’s work comparing awake and urethane-anesthetized rats was not complete, and it was probably not even appropriate because their findings might be urethane-specific. In this letter we discuss isoflurane-specific findings which are much more relevant to our argument for the similarity between awake vs. anesthetized hemodynamic response patterns. Previous functional studies comparing awake vs. isoflurane-anesthetized responses have been performed with visual stimulation in both monkeys (Shtoyerman et al., 2000) and cats (Fukuda et al., 2005). Even though the magnitude of responses in both monkeys and cats is reduced with isoflurane, the general response shape of the deoxyhemoglobin (dHb)-weighted optical imaging signal time courses is similar. Note that the change in dHb-weighted optical imaging signals (ΔR/R) is thought to be analogous to the BOLD response. In the primary visual cortex of monkeys (Shtoyerman et al., 2000), 605-nm wavelength dHb-weighted signal responses to 3-s stimulation were obtained for awake and for 1% isoflurane-anesthetized conditions; times to early negative peak (2-3 s after the onset of stimulation) and later positive peak (5-7 s) were consistent (see Fig. 4 as well as Fig. 10B). Similar responses were also detected from the cat visual cortex for awake and 0.5-1.0% isoflurane-anesthetized conditions (Fukuda et al., 2005). Dr. Fukuda - currently at the University of Pittsburgh - supplied original 620-nm wavelength dHb-weighted data used in their paper (Fukuda et al., 2005), which were reprocessed to show averaged time courses of optical responses to 2-s stimulation in visual area 18 (five cats; error bars = 1 SD). Time courses from both awake (filled square) and anesthetized cats (open circles) display biphasic behavior, with an initial decrease and later increase in reflected light intensity (i.e., an initial increase and later decrease in dHb content). The times to the early dip (2-3 s after the onset of stimulation), and the later positive peak (5-6 s) were consistent for awake and anesthetized conditions. Further studies are needed which compare dHb time courses in awake and isoflurane-anesthetized animals for different stimulus types and durations in order to determine whether our isoflurane-anesthetized animal studies (Masamoto et al., 2007; Zhao et al., 2007) are directly applicable to awake conditions. Overall, evoked hemodynamic response patterns in isoflurane-anesthetized animals are similar to those in awake animals, albeit with reduced magnitude (Shtoyerman et al., 2000; Fukuda et al., 2005). Thus, our original argument for similar dHb response patterns between awake and isoflurane-anesthetized cats appears to be valid. Since different anesthetics influence neurovascular coupling differently, we completely agree with Martin’s point that caution is required when extrapolating the findings from anesthetized animal studies to human fMRI.

This paper is about neuronal dynamics and how their special complexity can be understood in terms of nonlinear dynamics. There are many aspects of neuronal interactions and connectivity that engender the complexity of brain dynamics. In this paper we consider (i) the nature of this complexity and (ii) how it depends on connections between neuronal systems (e.g., neuronal populations or cortical areas). The main conclusion is that simulated neural systems show complex behaviors, reminiscent of neuronal dynamics, when these extrinsic connections are sparse. The patterns of activity that obtain, under these conditions, show a rich form of intermittency with the recurrent and self-limiting expression of stereotyped transient-like dynamics. Despite the fact that these dynamics conform to a single (complex) attractor this metastability gives the illusion of a dynamically changing attractor manifold (i.e., a changing surface upon which the dynamics unfold). This metastability is characterized using a measure that is based on the entropy of the time series’spectral density. r 1997 Academic Press

As the number of EEG papers increases, so too do the number of guidelines for how to report what has been done. However, current guidelines and checklists appear to have limited adoption, as systematic reviews have shown the journal article format is highly prone to errors, ambiguities and omissions of methodological details. This is a problem for transparency in the scientific record, along with reproducibility and metascience. Following lessons learned in the high complexity fields of aviation and surgery, we conclude that new tools are needed to overcome the limitations of written methodology descriptions, and that these tools should be developed through community consultation to ensure that they have the most utility for EEG stakeholders. As a first step in tool development, we present the ARTEM-IS Statement describing what action will be needed to create an Agreed Reporting Template for Electroencephalography Methodology - International Standard (ARTEM-IS), along with ARTEM-IS Design Guidelines for developing tools that use an evidence-based approach to error reduction. We first launched the statement at the LiveMEEG conference in 2020 along with a draft of an ARTEM-IS template for public consultation. Members of the EEG community are invited to join this collective effort to create evidence-based tools that will help make the process of reporting methodology intuitive to complete and foolproof by design. Nanyang Technological University National Research Foundation (NRF) Published version This research was supported by the following funding sources: Singapore’s National Research Foundation under the Science of Learning call (NRF2016-SOL002-011), Nanyang Technological University, via the Centre for Research and Development in Learning, (JHU IO 90071537) and NAP-SUG to SJS (M4081215), and grant OI179033 by Ministry of Education, Science and Technological Development of the Republic of Serbia.

The human neocortex is organized radially into six layers which differ in their myelination and the density and arrangement of neuronal cells. This cortical cyto- and myeloarchitecture plays a central role in the anatomical and functional neuroanatomy but is primarily accessible through invasive histology only. To overcome this limitation, several non-invasive MRI approaches have been, and are being, developed to resolve the anatomical cortical layers. As a result, recent studies on large populations and structure-function relationships at the laminar level became possible. Early proof-of-concept studies targeted conspicuous laminar structures such as the stria of Gennari in the primary visual cortex. Recent work characterized the laminar structure outside the visual cortex, investigated the relationship between laminar structure and function, and demonstrated layer-specific maturation effects. This paper reviews the methods and in-vivo MRI studies on the anatomical layers in the human cortex based on conventional and quantitative MRI (excluding diffusion imaging). A focus is on the related challenges, promises and potential future developments. The rapid development of MRI scanners, motion correction techniques, analysis methods and biophysical modeling promise to overcome the challenges of spatial resolution, precision and specificity of systematic imaging of cortical laminae.

The past decades witnessed a surge of interest in neuroimaging study of normal and abnormal early brain development. Structural and functional studies of normal early brain development revealed massive structural maturation as well as sequential, coordinated, and hierarchical emergence of functional networks during the infancy period, providing a great foundation for the investigation of abnormal early brain development mechanisms. Indeed, studies of altered brain development associated with either genetic or environmental risks emerged and thrived. In this paper, we will review selected studies of genetic and environmental risks that have been relatively more extensively investigated-familial risks, candidate risk genes, and genome-wide association studies (GWAS) on the genetic side; maternal mood disorders and prenatal drug exposures on the environmental side. Emerging studies on environment-gene interactions will also be reviewed. Our goal was not to perform an exhaustive review of all studies in the field but to leverage some representative ones to summarize the current state, point out potential limitations, and elicit discussions on important future directions.

This special issue summarizes an impressive body of work concerned with in vivo imaging of brain microstructure. Collectively, papers included here demonstrate the power of multi-modal magnetic resonance imaging (MRI) for mapping various structural properties of brain tissue. In this introduction, I provide a user's perspective vis-a-vis motivations for these efforts, review briefly the cellular composition of grey and white matter in the human brain, and provide a few examples of how we can bridge the gap between ex vivo and in vivo datasets to facilitate interpretation of studies measuring brain macro- and microstructure.

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Using a previously described technique that can identify the onset of population-level cortical activity and a novel functional connectivity algorithm described herein, we show that the cumulative latency difference of population-level neural activity across the task-related cortical network can explain up to 41% of the trial-by-trial variance in reaction time. Furthermore, we show that reaction time variance may primarily be due to the latencies in specific brain regions and demonstrate that behavioral latency variance is accumulated across the whole task-related cortical network. Our results suggest that population-level neural activity monotonically increases prior to movement execution, and that trial-by-trial changes in that increase are, in part, accounted for by inhibitory activity indexed by low-frequency oscillations. This pre-movement neural activity explains 19% of the measured variance in neural latencies in our data. Thus, our study provides a mechanistic explanation for a sizable fraction of behavioral reaction time when the subject’s task is the same from trial to trial.

The brain is organized into networks that reorganize dynamically in response to cognitive demands and exogenous stimuli. In recent years, repetitive transcranial magnetic stimulation (rTMS) has gained increasing use as a noninvasive means to modulate cortical physiology, with effects both proximal to the stimulation site and in distal areas that are intrinsically connected to the proximal target. In light of these network-level neuromodulatory effects, there has been a rapid growth in studies attempting to leverage information about network connectivity to improve neuromodulatory control and intervention outcomes. However, the mechanisms-of-action of rTMS on network-level effects remain poorly understood and is based primarily on heuristics from proximal stimulation findings. To help bridge this gap, the current paper presents a systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC). Literature synthesis revealed variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology. Despite this variability, it is observed that active rTMS induces significant changes on RSFC, but the prevalent low-frequency-inhibition/high-frequency-facilitation heuristic endorsed for proximal rTMS effects does not fully describe distal connectivity findings. This review also points towards other important considerations, including that the majority of rTMS-induced changes were found outside the stimulated functional network, suggesting that rTMS effects tend to spread across networks. Future studies may therefore wish to adopt conventions and systematic frameworks, such as the Yeo functional connectivity parcellation atlas adopted here, to better characterize network-level effect that contribute to the efficacy of these rapidly developing noninvasive interventions.

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the ‘classical’ picture of smoothly varying oscillations has been challenged by the idea that many ‘oscillations’ may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

Reward Prediction Errors (RPEs), defined as the difference between the expected and received outcomes, are integral to reinforcement learning models and play an important role in development and psychopathology. In humans, RPE encoding can be estimated using fMRI recordings, however, a basic measurement property of RPE signals, their test-retest reliability across different time scales, remains an open question. In this paper, we examine the 3-month and 3-year reliability of RPE encoding in youth (mean age at baseline = 10.6 ± 0.3 years), a period of developmental transitions in reward processing. We show that RPE encoding is differentially distributed between the positive values being encoded predominantly in the striatum and negative RPEs primarily encoded in the insula. The encoding of negative RPE values is highly reliable in the right insula, across both the long and the short time intervals. Insula reliability for RPE encoding is the most robust finding, while other regions, such as the striatum, are less consistent. Striatal reliability appeared significant as well once covarying for factors, which were possibly confounding the signal to noise ratio. By contrast, task activation during feedback in the striatum is highly reliable across both time intervals. These results demonstrate the valence-dependent differential encoding of RPE signals between the insula and striatum, and the consistency of RPE signals or lack thereof, during childhood and into adolescence. Characterizing the regions where the RPE signal in BOLD fMRI is a reliable marker is key for estimating reward-processing alterations in longitudinal designs, such as developmental or treatment studies.

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.

The aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx-/+;Dlx5/6CRE-IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.

Functional MRI using blood oxygenation level-dependent (BOLD) contrast indirectly probes neuronal activity via evoked cerebral blood volume (CBV) and oxygenation changes. Thus, its spatio-temporal characteristics are determined by vascular physiology and MRI parameters. In this paper, we focus on the spatial distribution and time course of the fMRI signal and their magnetic field strength dependence. Even though much is still unknown, the following consistent picture is emerging: a) For high spatial resolution imaging, fMRI contrast-to-noise increases supra-linearly with field strength. b) The location and spacing of penetrating arteries and ascending veins in the cortical tissue are not correlated to cortical columns, imposing limitations on achievable point-spread function (PSF) in fMRI. c) Baseline CBV distribution may vary over cortical layers biasing fMRI signal to layers with high CBV values. d) The largest CBV change is in the tissue microvasculature, less in surface arteries and even less in pial veins. e) Venous CBV changes are only relevant for longer stimuli, and oxygenation changes are largest in post-capillary blood vessels. f) The balloon effect (i.e. slow recovery of CBV to baseline) is located in the tissue, consistent with the fact that the post-stimulus undershoot has narrower spatial PSF than the positive BOLD response. g) The onset time following stimulation has been found to be shortest in middle/lower layers, both in optical imaging and high-resolution fMRI, but we argue and demonstrate with simulations that varying signal latencies can also be caused by vascular properties and, therefore, may potentially not be interpreted as neural latencies. With simulations, we illustrate the field strength dependency of fMRI signal transients, such as the adaptation during stimulation, initial dip and the post-stimulus undershoot. In sum, vascular structure and function impose limitations on the achievable PSF of fMRI and give rise to complex fMRI transients, which contain time-varying amount of excitatory and inhibitory neuronal information. Nevertheless, non-invasive fMRI at ultra-high magnetic fields not only provides high contrast-to-noise but also an unprecedented detailed view on cognitive processes in the human brain.

The three goals of this paper are: 1) to evaluate the improvements in technology for increasing magnetic flux density (magnetic field) to 14T in the next few years and eventually to 20T; 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 13C, 17O, and 31P; and brain electrolyte physiology using 23Na, 35Cl, and 39K. 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 7T 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 20T. This conclusion is conditioned on results of recommended experiments to verify the predicted level of physiological effects beyond 9.4T. This technology is predicted to enable quantification of biochemical components of the functioning brain not detectable heretofore.

Structural and functional alterations of the brain in persons genetically at-risk for Alzheimer's disease (AD) are crucial in unravelling AD development. Filippini et al. found that the default mode network (DMN) is already affected in young APOE ε4-carriers, with increased co-activation of the DMN during rest and increased hippocampal task activation. We aimed to replicate the early findings of Filippini et al, using the APOE gene, still the principal AD risk gene, and extended this with a polygenic risk score (PRS) analysis for AD, using the Human Connectome Project dataset (HCP). We included participants from the HCP S1200 dataset (age range: 22-36 years). We studied morphometric features, functional DMN co-activation and functional task activation of recollection performance. Permutation Analysis of Linear Models (PALM) was used to test for group differences between APOE ε4-carriers and non-carriers, and to test the association with PRS. PALM controls for biases induced by the family structure of the HCP sample. Results were family-wise error rate corrected at p < 0.05. Our primary analysis did not replicate the early findings of Filippini et al. (2009). However, compared with non-carriers, APOE ε4-carriers showed increased functional activation during the encoding of subsequently recollected items in areas related to facial recognition (p756.11). This increased functional activation was also positively associated with PRS (APOE variants included) (p647.55). Our results are supportive for none to limited genetic effects on brain structure and function in young adults. Taking the methodological considerations of replication studies into account, the true effect of APOE ε4-carriership is likely smaller than indicated in the Filippini paper. However, it still holds that we may not yet be able to detect already present measurable effects decades before a clinical expression of AD. Since the mechanistic pathway of AD is likely to encompass many different factors, further research should be focused on the interactions of genetic risk, biomarkers, aging and lifestyle factors over the life course. Sensitive functional neuroimaging as used here may help disentangling these complex interactions.

Whole-brain fMRI signals are a subject of intense interest: variance in the global fMRI signal (the spatial mean of all signals in the brain) indexes subject arousal, and psychiatric conditions such as schizophrenia and autism have been characterized by differences in the global fMRI signal. Further, vigorous debates exist on whether global signals ought to be removed from fMRI data. However, surprisingly little research has focused on the empirical properties of whole-brain fMRI signals. Here we map the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans. Variance in the global fMRI signal is strongly linked to head motion, to hardware artifacts, and to respiratory patterns and their attendant physiologic changes. Many techniques used to prepare fMRI data for analysis fail to remove these uninteresting kinds of global signal fluctuations. Thus, many studies include, at the time of analysis, prominent global effects of yawns, breathing changes, and head motion, among other signals. Such artifacts will mimic dynamic neural activity and will spuriously alter signal covariance throughout the brain. Methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance are needed; this paper adopts no position on the topic of global signal regression.

Investigating neonatal brain structure and function can offer valuable insights into behaviour and cognition in healthy and clinical populations; both at term age, and longitudinally in comparison with later time points. Parcellated brain atlases for adult populations are readily available, however warping infant data to adult template space is not ideal due to morphological and tissue differences between these groups. Several parcellated neonatal atlases have been developed, although there remains strong demand for manually parcellated ground truth data with detailed cortical definition. Additionally, compatibility with existing adult atlases is favourable for use in longitudinal investigations. We aimed to address these needs by replicating the widely-used Desikan-Killiany (2006) adult cortical atlas in neonates. We also aimed to extend brain coverage by complementing this cortical scheme with basal ganglia, thalamus, cerebellum and other subcortical segmentations. Thus, we have manually parcellated these areas volumetrically using high-resolution neonatal T2-weighted MRI scans, and initial automated and manually edited tissue classification, providing 100 regions in all. Linear and nonlinear T2-weighted structural templates were also generated. In this paper we provide manual parcellation protocols, and present the parcellated probability maps and structural templates together as the Melbourne Children's Regional Infant Brain (M-CRIB) atlas.

In this paper, we consider solutions to ten of the challenges faced when trying to predict an individual's functional outcome after stroke on the basis of lesion site. A primary goal is to find lesion-outcome associations that are consistently observed in large populations of stroke patients because consistent associations maximise confidence in future individualised predictions. To understand and control multiple sources of inter-patient variability, we need to systematically investigate each contributing factor and how each factor depends on other factors. This requires very large cohorts of patients, who differ from one another in typical and measurable ways, including lesion site, lesion size, functional outcome and time post stroke (weeks to decades). These multivariate investigations are complex, particularly when the contributions of different variables interact with one another. Machine learning algorithms can help to identify the most influential variables and indicate dependencies between different factors. Multivariate lesion analyses are needed to understand how the effect of damage to one brain region depends on damage or preservation in other brain regions. Such data-led investigations can reveal predictive relationships between lesion site and outcome. However, to understand and improve the predictions we need explanatory models of the neural networks and degenerate pathways that support functions of interest. This will entail integrating the results of lesion analyses with those from functional imaging (fMRI, MEG), transcranial magnetic stimulation (TMS) and diffusor tensor imaging (DTI) studies of healthy participants and patients.

Non-invasive transcranial brain stimulation (NTBS) techniques such as transcranial magnetic stimulation (TMS) and transcranial current stimulation (TCS) are important tools in human systems and cognitive neuroscience because they are able to reveal the relevance of certain brain structures or neuronal activity patterns for a given brain function. It is nowadays feasible to combine NTBS, either consecutively or concurrently, with a variety of neuroimaging and electrophysiological techniques. Here we discuss what kind of information can be gained from combined approaches, which often are technically demanding. We argue that the benefit from this combination is twofold. Firstly, neuroimaging and electrophysiology can inform subsequent NTBS, providing the required information to optimize where, when, and how to stimulate the brain. Information can be achieved both before and during the NTBS experiment, requiring consecutive and concurrent applications, respectively. Secondly, neuroimaging and electrophysiology can provide the readout for neural changes induced by NTBS. Again, using either concurrent or consecutive applications, both “online” NTBS effects immediately following the stimulation and “offline” NTBS effects outlasting plasticity-inducing NTBS protocols can be assessed. Finally, both strategies can be combined to close the loop between measuring and modulating brain activity by means of closed-loop brain state-dependent NTBS. In this paper, we will provide a conceptual framework, emphasizing principal strategies and highlighting promising future directions to exploit the benefits of combining NTBS with neuroimaging or electrophysiology.

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a “traffic pattern” that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain’s structure-function relationship over a large cohort of 641 healthy developmental participants aged 8 to 22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.

In this perspective paper, we examine possible premises of plasticity in the neural substrates underlying cognitive change. We take the special role of the medial temporal lobe as an anchoring point, but also investigate characteristics throughout the cortex. Specifically, we examine the dimensions of evolutionary expansion, heritability, variability of morphometric change, and inter-individual variance in myelination with respect to the plastic potential of different brain regions. We argue that areas showing less evolutionary expansion, lower heritability, greater variability of cortical thickness change through the lifespan, and greater inter-individual differences in intracortical myelin content have a great extent of plasticity. While different regions of the brain show these features to varying extent, analyses converge on the medial temporal lobe including the hippocampi as the target of all these premises. We discuss implications for effects of training on brain structures, and conditions under which plasticity may be evoked.

ObjectiveAn increasing number of human in vivo magnetic resonance imaging (MRI) studies have focused on examining the structure and function of the subfields of the hippocampal formation (the dentate gyrus, CA fields 1 − 3, and the subiculum) and subregions of the parahippocampal gyrus (entorhinal, perirhinal, and parahippocampal cortices). The ability to interpret the results of such studies and to relate them to each other would be improved if a common standard existed for labeling hippocampal subfields and parahippocampal subregions. Currently, research groups label different subsets of structures and use different rules, landmarks, and cues to define their anatomical extents. This paper characterizes, both qualitatively and quantitatively, the variability in the existing manual segmentation protocols for labeling hippocampal and parahippocampal substructures in MRI, with the goal of guiding subsequent work on developing a harmonized substructure segmentation protocol.MethodMRI scans of a single healthy adult human subject were acquired both at 3 T and 7 T. Representatives from 21 research groups applied their respective manual segmentation protocols to the MRI modalities of their choice. The resulting set of 21 segmentations was analyzed in a common anatomical space to quantify similarity and identify areas of agreement.ResultsThe differences between the 21 protocols include the region within which segmentation is performed, the set of anatomical labels used, and the extents of specific anatomical labels. The greatest overall disagreement among the protocols is at the CA1/subiculum boundary, and disagreement across all structures is greatest in the anterior portion of the hippocampal formation relative to the body and tail.ConclusionsThe combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies. published

This paper introduces a Bidirectional Iterative Parcellation (BIP) procedure designed to identify the location and size of connected cortical regions (parcellations) at both ends of a white matter tract in diffusion weighted images. The procedure applies the FSL option "probabilistic tracking with classification targets" in a bidirectional and iterative manner. To assess the utility of BIP, we applied the procedure to the problem of parcellating a limited set of well-established gray matter seed regions associated with the dorsal (arcuate fasciculus/superior longitudinal fasciculus) and ventral (extreme capsule fiber system) white matter tracts in the language networks of 97 participants. These left hemisphere seed regions and the two white matter tracts, along with their right hemisphere homologues, provided an excellent test case for BIP because the resulting parcellations overlap and their connectivity via the arcuate fasciculi and extreme capsule fiber systems are well studied. The procedure yielded both confirmatory and novel findings. Specifically, BIP confirmed that each tract connects within the seed regions in unique, but expected ways. Novel findings included increasingly left-lateralized parcellations associated with the arcuate fasciculus/superior longitudinal fasciculus as a function of age and education. These results demonstrate that BIP is an easily implemented technique that successfully confirmed cortical connectivity patterns predicted in the literature, and has the potential to provide new insights regarding the architecture of the brain.

Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused onmultimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronalmechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e. g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e. g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques. (C) 2013 Elsevier Inc. All rights reserved.

The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i.e., the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.

Transcranial Electric Stimulation (tES) is a widely used non-invasive brain stimulation technique. However, strong stimulation artifacts complicate the investigation of neural activity with EEG or MEG during tES. Thus, studying brain signals during tES requires detailed knowledge about the properties of these artifacts. Recently, we characterized the phase- and amplitude-relationship between tES stimulation currents and tES artifacts in EEG and MEG and provided a mathematical model of these artifacts (Noury and Siegel, 2017, and Noury et al., 2016, respectively). Among several other features, we showed that, independent of the stimulation current, the amplitude of tES artifacts is modulated time locked to heartbeat and respiration. In response to our work, a recent paper (Neuling et al., 2017) raised several points concerning the employed stimulation device and methodology. Here, we discuss these points, explain potential misunderstandings, and show that none of the raised concerns are applicable to our results. Furthermore, we explain in detail the physics underlying tES artifacts, and discuss several approaches how to study brain function during tES in the presence of residual artifacts.