In this paper, we describe a simple set of "recipes" for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.

Significant resources are now being devoted to large-scale international studies attempting to map the connectome - the brain's wiring diagram. This review will focus on the use of human neuroimaging approaches to map the connectome at a macroscopic level. This emerging field of human connectomics brings both opportunities and challenges. Opportunities arise from the ability to apply a powerful toolkit of mathematical and computational approaches to interrogate these rich datasets, many of which are being freely shared with the scientific community. Challenges arise in methodology, interpretability and biological or clinical validity. This review discusses these challenges and opportunities and highlights potential future directions.

Studies of cortical function in the awake infant are extremely challenging to undertake with traditional neuroimaging approaches. Partly in response to this challenge, functional near-infrared spectroscopy (fNIRS) has become increasingly common in developmental neuroscience, but has significant limitations including resolution, spatial specificity and ergonomics. In adults, high-density arrays of near-infrared sources and detectors have recently been shown to yield dramatic improvements in spatial resolution and specificity when compared to typical fNIRS approaches. However, most existing fNIRS devices only permit the acquisition of ~20-100 sparsely distributed fNIRS channels, and increasing the number of optodes presents significant mechanical challenges, particularly for infant applications. A new generation of wearable, modular, high-density diffuse optical tomography (HD-DOT) technologies has recently emerged that overcomes many of the limitations of traditional, fibre-based and low-density fNIRS measurements. Driven by the development of this new technology, we have undertaken the first study of the infant brain using wearable HD-DOT. Using a well-established social stimulus paradigm, and combining this new imaging technology with advances in cap design and spatial registration, we show that it is now possible to obtain high-quality, functional images of the infant brain with minimal constraints on either the environment or on the infant participants. Our results are consistent with prior low-density fNIRS measures based on similar paradigms, but demonstrate superior spatial localization, improved depth specificity, higher SNR and a dramatic improvement in the consistency of the responses across participants. Our data retention rates also demonstrate that this new generation of wearable technology is well tolerated by the infant population.

Consciousness has been hypothesized to emerge from complex neuronal dynamics, which prevails when brain operates in a critical state. Evidence supporting this hypothesis comes mainly from studies investigating neuronal activity on a short time-scale of seconds. However, a key aspect of criticality is presence of scale-free temporal dependencies occurring across a wide range of time-scales. Indeed, robust long-range temporal correlations (LRTCs) are found in neuronal oscillations during conscious states, but it is not known how LRTCs are affected by loss of consciousness. To further test a relation between critical dynamics and consciousness, we investigated LRTCs in electrocorticography signals recorded from four macaque monkeys during resting wakefulness and general anesthesia induced by various anesthetics (ketamine, medetomidine, or propofol). Detrended Fluctuation Analysis was used to estimate LRTCs in amplitude fluctuations (envelopes) of band-pass filtered signals. We demonstrate two main findings. First, during conscious states all lateral cortical regions are characterized by significant LRTCs of alpha-band activity (7-14 Hz). LRTCs are stronger in the eyes-open than eyes-closed state, but in both states they form a spatial gradient, with anterior brain regions exhibiting stronger LRTCs than posterior regions. Second, we observed a substantial decrease of LRTCs during loss of consciousness, the magnitude of which was associated with the baseline (i.e. pre-anesthesia) state of the brain. Specifically, brain regions characterized by strongest LRTCs during a wakeful baseline exhibited greatest decreases during anesthesia (i.e. "the rich got poorer"), which consequently disturbed the posterior-anterior gradient. Therefore, our results suggest that general anesthesia affects mainly brain areas characterized by strongest LRTCs during wakefulness, which might account for lack of capacities for extensive temporal integration during loss of consciousness.

Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.

Multimodal neuroimaging plays an important role in neuroscience research. Integrated noninvasive neuroimaging modalities, such as magnetoencephalography (MEG), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), allow neural activity and related physiological processes in the brain to be precisely and comprehensively depicted, providing an effective and advanced platform to study brain function. Noncryogenic optically pumped magnetometer (OPM) MEG has high signal power due to its on-scalp sensor layout and enables more flexible configurations than traditional commercial superconducting MEG. Here, we integrate OPM-MEG with EEG and fNIRS to develop a multimodal neuroimaging system that can simultaneously measure brain electrophysiology and hemodynamics. We conducted a series of experiments to demonstrate the feasibility and robustness of our MEG-EEG-fNIRS acquisition system. The complementary neural and physiological signals simultaneously collected by our multimodal imaging system provide opportunities for a wide range of potential applications in neurovascular coupling, wearable neuroimaging, hyperscanning and brain-computer interfaces.

Hysteresis is a well-known phenomenon in physics that relates changes in a system with its prior history. It is also part of human visual experience (perceptual hysteresis), and two different neural mechanisms might explain it: persistence (a cause of positive hysteresis), which forces to keep a current percept for longer, and adaptation (a cause of negative hysteresis), which in turn favors the switch to a competing percept early on. In this study, we explore the neural correlates underlying these mechanisms and the hypothesis of their competitive balance, by combining behavioral assessment with fMRI. We used machine learning on the behavioral data to distinguish between positive and negative hysteresis, and discovered a neural correlate of persistence at a core region of the ventral attention network, the anterior insula. Our results add to the understanding of perceptual multistability and reveal a possible mechanistic explanation for the regulation of different forms of perceptual hysteresis.

Imaging ictal and interictal activity with Electrical Impedance Tomography (EIT) using intracranial electrode mats has been demonstrated in animal models of epilepsy. In human epilepsy subjects undergoing presurgical evaluation, depth electrodes are often preferred. The purpose of this work was to evaluate the feasibility of using EIT to localise epileptogenic areas with intracranial electrodes in humans. The accuracy of localisation of the ictal onset zone was evaluated in computer simulations using 9M element FEM models derived from three subjects. 5 mm radius perturbations imitating a single seizure onset event were placed in several locations forming two groups: under depth electrode coverage and in the contralateral hemisphere. Simulations were made for impedance changes of 1% expected for neuronal depolarisation over milliseconds and 10% for cell swelling over seconds. Reconstructions were compared with EEG source modelling for a radially orientated dipole with respect to the closest EEG recording contact. The best accuracy of EIT was obtained using all depth and 32 scalp electrodes, greater than the equivalent accuracy with EEG inverse source modelling. The localisation error was 5.2 ± 1.8, 4.3 ± 0 and 46.2 ± 25.8 mm for perturbations within the volume enclosed by depth electrodes and 29.6 ± 38.7, 26.1 ± 36.2, 54.0 ± 26.2 mm for those without (EIT 1%, 10% change, EEG source modelling, n = 15 in 3 subjects, p < 0.01). As EIT was insensitive to source dipole orientation, all 15 perturbations within the volume enclosed by depth electrodes were localised, whereas the standard clinical method of visual inspection of EEG voltages, only localised 8 out of 15 cases. This suggests that adding EIT to SEEG measurements could be beneficial in localising the onset of seizures.

For successful communication, we need to understand the external world consistently with others. This task requires sufficiently similar cognitive schemas or psychological perspectives that act as filters to guide the selection, interpretation and storage of sensory information, perceptual objects and events. Here we show that when individuals adopt a similar psychological perspective during natural viewing, their brain activity becomes synchronized in specific brain regions. We measured brain activity with functional magnetic resonance imaging (fMRI) from 33 healthy participants who viewed a 10-min movie twice, assuming once a 'social' (detective) and once a 'non-social' (interior decorator) perspective to the movie events. Pearson's correlation coefficient was used to derive multisubject voxelwise similarity measures (inter-subject correlations; ISCs) of functional MRI data. We used k-nearest-neighbor and support vector machine classifiers as well as a Mantel test on the ISC matrices to reveal brain areas wherein ISC predicted the participants' current perspective. ISC was stronger in several brain regions--most robustly in the parahippocampal gyrus, posterior parietal cortex and lateral occipital cortex--when the participants viewed the movie with similar rather than different perspectives. Synchronization was not explained by differences in visual sampling of the movies, as estimated by eye gaze. We propose that synchronous brain activity across individuals adopting similar psychological perspectives could be an important neural mechanism supporting shared understanding of the environment.

Spontaneous brain activity is ubiquitous across brain structures and states. Determining the role of these metabolically costly intrinsic events may be critical for understanding the brain's fundamental physiological principles that govern cognition and behavior. To date, most investigations of large-scale fluctuations and their coupling have been conducted using electro- or magneto-encephalography, modalities that are limited in their ability to spatially resolve the origin of the signals. Invasive, electrophysiological local field potential (LFP) recordings are limited in their spatial range and studies combining the approach with functional imaging have been primarily relegated to sensory/motor areas with little basis in which to extrapolate findings to evolutionarily newer prefrontal cortical regions. Here, we acquired spontaneous fMRI data in two anesthetized macaque monkeys (Macaca fascicularis) at 7 T together with simultaneous recordings of intracortical LFPs recorded bilaterally from the prefrontal cortex (area 9/46d). High (beta-low gamma) and low (delta-theta) band-limited power (BLP) ranges of the LFP frequencies were anticorrelated in the absence of any explicit stimuli. Beyond the high LFP-BLP signal being correlated with BOLD activity at the recording site, the high and low LFP-BLP envelopes were shown to be significantly correlated with spontaneous BOLD activity recorded from positively and negatively connected prefrontal network regions, respectively. The results suggest that complementary changes in low and high frequency bands may be an intrinsic property of LFPs, that local prefrontal cortical activity is related to spontaneous BOLD fluctuations, and further, that LFP-BLPs may be correlated at a network level.

Based upon cytoarchitectonic properties, the primate prefrontal cortex has been partitioned into different subregions that show unique structural connectivity patterns, with ongoing efforts to provide more fine-grained divisions. While meaningful divisions may be found within the sub-millimeter range, the subdivisions exist within an overall hierarchical architecture and at higher levels likely share similar activity patterns and functionality. Here, we used resting-state fMRI in lightly anesthetized macaque monkeys to measure the intrinsic functional connectivity of the prefrontal cortex. At a gross anatomical level, the data driven approach revealed five broad clusters that showed distinct brain-wide functional connectivity. Although each cluster encompasses several cytoarchitectonic subregions, the clusters overlap with the intrinsic structural connectivity of the prefrontal cortex and each cluster may subserve common functions.

Parallel imaging techniques are useful for fMRI studies in light of the increasing susceptibility effects at high magnetic field strength. Yet, spatially varying noise amplification constitutes a challenge for the application of these techniques. The medial temporal lobe is particularly vulnerable to susceptibility effect with increasingly strong signal reduction. We present two fMRI studies comparing SENSE single-shot (ssh) echo planar imaging (EPI) at acceleration factors of 2.0, 2.4, 2.7, and 3.0 with conventional sshEPI at TE of 22 and 35 ms. Data were acquired during a learning task which activates the medial temporal lobe bilaterally. Susceptibility related image distortion was markedly reduced with increasing SENSE acceleration. Moreover, in the group results, statistical power increased in the whole brain with SENSE compared to conventional imaging and with a TE of 35 ms compared to 22 ms. Higher SENSE acceleration factors further improved image quality and increased statistical power in the occipital lobe and fusiform gyrus, but not in the medial temporal lobe. We therefore conclude that an sshEPI acquisition protocol with a moderate SENSE acceleration factor of R = 2.0 and TE 35 ms is suitable for the detection of medial temporal activation at 3T.

Increased intraindividual variability (IIV) is a hallmark of disorders of attention. Recent work has linked these disorders to abnormalities in a "default mode" network, comprising brain regions routinely deactivated during goal-directed cognitive tasks. Findings from a study of the neural basis of attentional lapses suggest that a competitive relationship between the "task-negative" default mode network and regions of a "task-positive" attentional network is a potential locus of dysfunction in individuals with increased IIV. Resting state studies have shown that this competitive relationship is intrinsically represented in the brain, in the form of a negative correlation or antiphase relationship between spontaneous activity occurring in the two networks. We quantified the negative correlation between these two networks in 26 subjects, during active (Eriksen flanker task) and resting state scans. We hypothesized that the strength of the negative correlation is an index of the degree of regulation of activity in the default mode and task-positive networks and would be positively related to consistent behavioral performance. We found that the strength of the correlation between the two networks varies across individuals. These individual differences appear to be behaviorally relevant, as interindividual variation in the strength of the correlation was significantly related to individual differences in response time variability: the stronger the negative correlation (i.e., the closer to 180 degrees antiphase), the less variable the behavioral performance. This relationship was moderately consistent across resting and task conditions, suggesting that the measure indexes moderately stable individual differences in the integrity of functional brain networks. We discuss the implications of these findings for our understanding of the behavioral significance of spontaneous brain activity, in both healthy and clinical populations.

Networks in the prefrontal cortex (PFC) that are important for executive function are also engaged in adaptive responding to negative events. These networks are particularly vulnerable to age-related structural atrophy and an associated loss of executive function, yet existing evidence suggests preserved emotion processing ability in ageing. Using longitudinally acquired data from a battery of cognitive tasks, we defined a metric for the rate of decline of executive function. With this metric, we investigated relationships between changes in executive function and emotion reappraisal ability and brain structure, in 34 older adults, using functional and structural MRI. During task-based fMRI, participants were asked to cognitively reappraise negatively valenced images. We hypothesised one of two associations with decreasing executive function over time: 1) a decreased ability to reappraise reflected in decreased PFC and increased amygdala activation, or 2) a neural compensation mechanism characterised by increased PFC activation but no differential amygdala activation. Structurally, for a decreased reappraisal ability, we predicted a decrease in grey matter in PFC and/or a decrease of white matter integrity in amygdala-PFC pathways. Neither of the two hypotheses relating to brain function were completely supported, with the findings indicating a steeper decline in executive function associated with both increased PFC and increased left amygdala activity when reappraising negative stimuli. In addition, white matter integrity of the uncinate fasciculus, a primary white matter tract connecting the amygdala and ventromedial areas of PFC, was lower in those individuals who demonstrated a greater decrease in executive function. These findings highlight the association of diminishing cognitive ability with brain structure and function linked to emotion regulation.

Stimulus categorization is influenced by oscillations in the brain. For example, we have shown that ongoing oscillatory phase biases identification of an ambiguous syllable that can either be perceived as /da/ or /ga/. This suggests that phase is a cue for the brain to determine syllable identity and this cue could be an element of the representation of these syllables. If so, brain activation patterns for /da/ should be more unique when the syllable is presented at the /da/ biasing (i.e. its "preferred") phase. To test this hypothesis we presented non-ambiguous /da/ and /ga/ syllables at either their preferred or non-preferred phase (using sensory entrainment) while measuring 7T fMRI. Using multivariate pattern analysis in auditory regions we show that syllable decoding performance is higher when syllables are presented at their preferred compared to their non-preferred phase. These results suggest that phase information increases the distinctiveness of /da/ and /ga/ brain activation patterns.

The aim of this study is to perform a thorough comparison of quantitative susceptibility mapping (QSM) techniques and their dependence on the assumptions made. The compared methodologies were: two iterative single orientation methodologies minimizing the l2, l1TV norm of the prior knowledge of the edges of the object, one over-determined multiple orientation method (COSMOS) and a newly proposed modulated closed-form solution (MCF). The performance of these methods was compared using a numerical phantom and in-vivo high resolution (0.65 mm isotropic) brain data acquired at 7 T using a new coil combination method. For all QSM methods, the relevant regularization and prior-knowledge parameters were systematically changed in order to evaluate the optimal reconstruction in the presence and absence of a ground truth. Additionally, the QSM contrast was compared to conventional gradient recalled echo (GRE) magnitude and R2* maps obtained from the same dataset. The QSM reconstruction results of the single orientation methods show comparable performance. The MCF method has the highest correlation (corr MCF=0.95, r(2)MCF=0.97) with the state of the art method (COSMOS) with additional advantage of extreme fast computation time. The L-curve method gave the visually most satisfactory balance between reduction of streaking artifacts and over-regularization with the latter being overemphasized when the using the COSMOS susceptibility maps as ground-truth. R2* and susceptibility maps, when calculated from the same datasets, although based on distinct features of the data, have a comparable ability to distinguish deep gray matter structures.

Anterior cingulate cortex (ACC) is a nexus of information processing and regulation in the brain. Reflecting this central role, ACC is structurally and functionally heterogeneous, a fact long appreciated in studies of non-human primates. Human neuroimaging studies also recognize this functional heterogeneity, with meta-analyses and task-based studies demonstrating the existence of motor, cognitive and affective subdivisions. In contrast to task-based approaches, examinations of resting-state functional connectivity enable the characterization of task-independent patterns of correlated activity. In a novel approach to understanding ACC functional segregation, we systematically mapped ACC functional connectivity during rest. We examined patterns of functional connectivity for 16 seed ROIs systematically placed throughout caudal, rostral, and subgenual ACC in each hemisphere. First, our data support the commonly observed rostral/caudal distinction, but also suggest the existence of a dorsal/ventral functional distinction. For each of these distinctions, more fine-grained patterns of differentiation were observed than commonly appreciated in human imaging studies. Second, we demonstrate the presence of negatively predicted relationships between distinct ACC functional networks. In particular, we highlight negative relationships between rostral ACC-based affective networks (including the "default mode network") and dorsal-caudal ACC-based frontoparietal attention networks. Finally, interhemispheric activations were more strongly correlated between homologous regions than in non-homologous regions. We discuss the implications of our work for understanding ACC function and potential applications to clinical populations.

It is widely accepted that addictive drug use is related to abnormal functional organization in the user's brain. The present study aimed to identify this type of abnormality within the brain networks implicated in addiction by resting-state functional connectivity measured with functional magnetic resonance imaging (fMRI). With fMRI data acquired during resting state from 14 chronic heroin users (12 of whom were being treated with methadone) and 13 non-addicted controls, we investigated the addiction related alteration in functional connectivity between the regions in the circuits implicated in addiction with seed-based correlation analysis. Compared with controls, chronic heroin users showed increased functional connectivity between nucleus accumbens and ventral/rostral anterior cingulate cortex (ACC), between nucleus accumbens and orbital frontal cortex (OFC), and between amygdala and OFC and reduced functional connectivity between prefrontal cortex and OFC and between prefrontal cortex and ACC. These observations of altered resting-state functional connectivity suggested abnormal functional organization in the addicted brain and may provide additional evidence supporting the theory of addiction that emphasizes enhanced salience value of a drug and its related cues but weakened cognitive control in the addictive state.

Cross-sectional and longitudinal volumetric studies suggest that the corpus callosum (CC) continues to mature structurally from infancy to adulthood. Diffusion tensor imaging (DTI) provides in vivo information about the directional organization of white matter microstructure and shows potential for elucidating even more subtle brain changes during adolescent development. We used DTI to examine CC microstructure in healthy right-handed adolescents (n=92, ages 9-24 years) and correlated the imaging data with motor task performance. The primary DTI variable was fractional anisotropy (FA), which reflects the degree of white matter's directional organization. Participants completed an alternating finger tapping test to assess interhemispheric transfer and motor speed. Task performance was significantly correlated with age. Analyses of variance indicated that 9- to 11-year-olds generally performed worse than each of the older groups. Males outperformed females. Significant positive correlations between age and FA were observed in the splenium of the CC, which interconnects posterior cortical regions. Analyses of variance indicated that individuals older than 18 years had significantly higher FA than 9- to 11-year-olds. FA levels in the genu and splenium correlated significantly with task performance. Regression analyses indicated that bimanual coordination was significantly predicted by age, gender, and splenium FA. Decreases in alternating finger tapping time and increases in FA likely reflect increased myelination in the CC and more efficient neuronal signal transmission. These findings expand upon existing neuroimaging reports of CC development by showing associations between bimanual coordination and white matter microstructural organization in an adolescent sample.

Unilateral damage to the frontoparietal network typically impairs saccade target selection within the contralesional visual hemifield. Severity of deficits and the degree of recovery have been associated with widespread network dysfunction, yet it is not clear how these behavioural and functional brain changes relate with the underlying structural white matter tracts. Here, we investigated whether recovery after unilateral prefrontal cortex (PFC) lesions was associated with changes in white matter microstructure across large-scale frontoparietal cortical and thalamocortical networks. Diffusion-weighted imaging was acquired in four male rhesus macaques at pre-lesion, week 1, and week 8-16 post-lesion when target selection deficits largely recovered. Probabilistic tractography was used to reconstruct cortical frontoparietal fiber tracts, including the superior longitudinal fasciculus (SLF) and transcallosal fibers connecting the PFC or posterior parietal cortex (PPC), as well as thalamocortical fiber tracts connecting the PFC and PPC to thalamic nuclei. We found that the two animals with small PFC lesions showed increased fractional anisotropy in both cortical and thalamocortical fiber tracts when behaviour had recovered. However, we found that fractional anisotropy decreased in cortical frontoparietal tracts after larger PFC lesions yet increased in some thalamocortical tracts at the time of behavioural recovery. These findings indicate that behavioural recovery after small PFC lesions may be supported by both cortical and subcortical areas, whereas larger PFC lesions may have induced widespread structural damage and hindered compensatory remodeling in the cortical frontoparietal network.