The contribution of electrophysiology to functional connectivity mapping

Temporal ablation of the ciliary protein IFT88 alters normal brainwave patterns

The primary cilium is a hair-like organelle that hosts molecular machinery for various developmental and homeostatic signaling pathways. Its alteration can cause rare ciliopathies such as the Bardet-Biedl and Joubert syndromes, but is also linked to Alzheimer's disease, clinical depression, and autism spectrum disorder. These afflictions are caused by disturbances in a wide variety of genes but a common phenotype amongst them is cognitive impairment. While cilia-mediated neural function has been widely examined in early neurodevelopment, their function in the adult brain is not well understood. To help elucidate the role of cilia in neural activity, we temporally induced the ablation of IFT88, a gene encoding the intraflagellar transport 88 protein which is neccessary for ciliogenesis, in adult mice before performing memory-related behavioral assays and electroencephalogram/electromyogram (EEG/EMG) recordings. Inducible IFT88 KO mice exhibited severe learning deficits in trace fear conditioning and Morris water maze tests. They had strongly affected brainwave activity both under isoflurane induced anesthesia and during normal activity. And additionally, inducible IFT88 KO mice had altered sleep architecture and attenuated phase-amplitude coupling, a process that underlies learning and memory formation. These results highlight the growing significance of primary cilia for healthy neural function in the adult brain.

Temporal Ablation of the Ciliary Protein IFT88 Alters Normal Brainwave Patterns

The primary cilium is a hair-like organelle that hosts molecular machinery for various developmental and homeostatic signaling pathways. Its alteration can cause rare ciliopathies such as the Bardet-Biedl and Joubert syndromes, but is also linked to Alzheimer's disease, clinical depression, and autism spectrum disorder. These afflictions are caused by disturbances in a wide variety of genes but a common phenotype amongst them is cognitive impairment. While cilia-mediated neural function has been widely examined in early neurodevelopment, their function in the adult brain is not well understood. To help elucidate the role of cilia in neural activity, we temporally induced the ablation of IFT88, a gene encoding the intraflagellar transport 88 protein which is neccessary for ciliogenesis, in adult mice before performing memory-related behavioral assays and electroencephalogram/electromyogram (EEG/EMG) recordings. Inducible IFT88 KO mice exhibited severe learning deficits in trace fear conditioning and Morris water maze tests. They had strongly affected brainwave activity both under isoflurane induced anesthesia and during normal activity. And additionally, inducible IFT88 KO mice had altered sleep architecture and attenuated phase-amplitude coupling, a process that underlies learning and memory formation. These results highlight the growing significance of primary cilia for healthy neural function in the adult brain.

The development of aperiodic neural activity in the human brain

The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males; n electrodes = 5691), we mapped aperiodic (1/ƒ-like) activity, a proxy of excitation:inhibition (E:I) balance with steeper slopes indexing inhibition and flatter slopes indexing excitation. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are differentially associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC inhibitory control during adolescence in the development of attention and memory.

EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints

One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses.

The effect of HD-tDCS on brain oscillations and frontal synchronicity during resting-state EEG in violent offenders with a substance dependence

Violence is a major problem in our society and therefore research into the neural underpinnings of aggression has grown exponentially. Although in the past decade the biological underpinnings of aggressive behavior have been examined, research on neural oscillations in violent offenders during resting-state electroencephalography (rsEEG) remains scarce. In this study we aimed to investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) on frontal theta, alpha and beta frequency power, asymmetrical frontal activity, and frontal synchronicity in violent offenders. Fifty male violent forensic patients diagnosed with a substance dependence were included in a double-blind sham-controlled randomized study. The patients received 20 minutes of HD-tDCS two times a day on five consecutive days. Before and after the intervention, the patients underwent a rsEEG task. Results showed no effect of HD-tDCS on the power in the different frequency bands. Also, no increase in asymmetrical activity was found. However, we found increased synchronicity in frontal regions in the alpha and beta frequency bands indicating enhanced connectivity in frontal brain regions as a result of the HD-tDCS-intervention. This study has enhanced our understanding of the neural underpinnings of aggression and violence, pointing to the importance of alpha and beta frequency bands and their connectivity in frontal brain regions. Although future studies should further investigate the complex neural underpinnings of aggression in different populations and using whole-brain connectivity, it can be suggested with caution, that HD-tDCS could be an innovative method to regain frontal synchronicity in neurorehabilitation.

Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures

The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.

Combining magnetic resonance imaging with readout and/or perturbation of neural activity in animal models: Advantages and pitfalls

One of the main challenges in brain research is to link all aspects of brain function: on a cellular, systemic, and functional level. Multimodal neuroimaging methodology provides a continuously evolving platform. Being able to combine calcium imaging, optogenetics, electrophysiology, chemogenetics, and functional magnetic resonance imaging (fMRI) as part of the numerous efforts on brain functional mapping, we have a unique opportunity to better understand brain function. This review will focus on the developments in application of these tools within fMRI studies and highlight the challenges and choices neurosciences face when designing multimodal experiments.

Directed functional and structural connectivity in a large-scale model for the mouse cortex

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

We analyzed the relationship between structural and directed functional connectivity by evaluating the effectiveness of generalized partial directed coherence (GPDC) to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. We show that GPDC values provide a reasonable estimate of structural connectivity even in a typical electrophysiological recording scenario containing few areas. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

Abnormal negative feedback processing in individuals with autistic traits in the Iowa gambling task: Evidence from behavior and event-related potentials

Value-based decision making plays an important role in social interaction. Previous studies have reported that individuals with autism spectrum disorder (ASD) exhibit deficits in terms of decision making. However, it is still unknown clearly whether individuals with high autistic traits within nonclinical populations employ abnormal neural substrates in value-based decision-making. To explore this issue, we investigated value-based decision making and its neural substrates in individuals with high and low autistic traits within a typically developing population who completed the revised Iowa gambling task (IGT) based on measurements of event-related potentials (ERPs). The IGT net scores were significantly lower in the group with high autistic traits than the group with low autistic traits in the fifth and sixth blocks. The ERP results showed that the feedback-related negativity (FRN) amplitude in individuals with high autistic traits allowed slight discrimination between positive and negative feedback in the low-risk option. The event-related spectral perturbations (ERSPs) and inter-trial coherence (ITC) of the theta-band frequency were also lower in the group with high autistic traits than the group with low autistic traits in the loss low-risk option. The results obtained in this study indicate that individuals with high autistic traits exhibit an unusual negative feedback process and relevant neural substrate. The FRN amplitude and theta-band oscillation may comprise a neural index of abnormal decision-making processes in individuals with high autistic traits. This study of a small sample may be considered an important step toward a more comprehensive understanding of the autism "spectrum" within a nonclinical population based on cognitive neuroscience.

Normalization of reduced functional connectivity after revascularization of asymptomatic carotid stenosis

Internal carotid artery stenosis is a risk factor for ischemic stroke. Even in the absence of visible structural brain changes, patients with asymptomatic stenosis are prone to cognitive impairment. On a neuronal level, it was suggested that stenosis may lead to disturbed functional brain connectivity. If so, carotid revascularization should have an effect on hypothesized brain network disturbances. We studied functional connectivity in a motor network by resting-state electroencephalography in 12 patients with high grade asymptomatic carotid stenosis before and after interventional or surgical revascularization as compared to 23 controls. In patients with stenosis, functional connectivity of neural oscillations was significantly decreased prior and improved returning to normal connectivity after revascularization. In a subgroup of patients, also studied by contrast perfusion magnetic resonance imaging, reduced connectivity was associated with decreased regional brain perfusion reflected by increased mean transit time in the middle cerebral artery borderzone. Cognitive testing revealed only minor differences between patients and controls. In summary, we identified oscillatory connectivity changes in patients with asymptomatic carotid stenosis correlating with regional hypoperfusion, which both normalized after revascularization. Hence, electrophysiological changes might be a reversible precursor preceding macroscopic structural brain damage and behavioral impairment in patients with asymptomatic carotid stenosis.

Discovering dynamic task-modulated functional networks with specific spectral modes using MEG

Efficient neuronal communication between brain regions through oscillatory synchronization at certain frequencies is necessary for cognition. Such synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to support ongoing cognitive operations. However, few studies characterizing dynamic electrophysiological brain networks have simultaneously accounted for temporal non-stationarity, spectral structure, and spatial properties. Here, we propose an analysis framework for characterizing the large-scale phase-coupling network dynamics during task performance using magnetoencephalography (MEG). We exploit the high spatiotemporal resolution of MEG to measure time-frequency dynamics of connectivity between parcellated brain regions, yielding data in tensor format. We then use a tensor component analysis (TCA)-based procedure to identify the spatio-temporal-spectral modes of covariation among separate regions in the human brain. We validate our pipeline using MEG data recorded during a hand movement task, extracting a transient motor network with beta-dominant spectral mode, which is significantly modulated by the movement task. Next, we apply the proposed pipeline to explore brain networks that support cognitive operations during a working memory task. The derived results demonstrate the temporal formation and dissolution of multiple phase-coupled networks with specific spectral modes, which are associated with face recognition, vision, and movement. The proposed pipeline can characterize the spectro-temporal dynamics of functional connectivity in the brain on the subsecond timescale, commensurate with that of cognitive performance.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Resting-State Magnetoencephalography Source Imaging Pilot Study in Children with Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) accounts for the vast majority of all pediatric TBI. An important minority of children who have suffered an mTBI have enduring cognitive and emotional symptoms. However, the mechanisms of chronic symptoms in children with pediatric mTBI are not fully understood. This is in part due to the limited sensitivity of conventional neuroimaging technologies. The present study examined resting-state magnetoencephalography (rs-MEG) source images in 12 children who had mTBI and 12 age-matched control children. The rs-MEG exams were performed in children with mTBI 6 months after injury when they reported no clinically significant post-injury psychiatric changes and few if any somatic sensorimotor symptoms but did report cognitive symptoms. MEG source magnitude images were obtained for different frequency bands in alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and low-frequency (1-7 Hz) bands. In contrast to the control participants, rs-MEG source imaging in the children with mTBI showed: 1) hyperactivity from the bilateral insular cortices in alpha, beta, and low-frequency bands, from the left amygdala in alpha band, and from the left precuneus in beta band; 2) hypoactivity from the bilateral dorsolateral prefrontal cortices (dlPFC) in alpha and beta bands, from the ventromedial prefrontal cortex (vmPFC) in beta band, from the ventrolateral prefrontal cortex (vlPFC) in gamma band, from the anterior cingulate cortex (ACC) in alpha band, and from the right precuneus in alpha band. The present study showed that MEG source imaging technique revealed abnormalities in the resting-state electromagnetic signals from the children with mTBI.

Time-Frequency Based Phase-Amplitude Coupling Measure For Neuronal Oscillations

Oscillatory activity in the brain has been associated with a wide variety of cognitive processes including decision making, feedback processing, and working memory. The high temporal resolution provided by electroencephalography (EEG) enables the study of variation of oscillatory power and coupling across time. Various forms of neural synchrony across frequency bands have been suggested as the mechanism underlying neural binding. Recently, a considerable amount of work has focused on phase-amplitude coupling (PAC)– a form of cross-frequency coupling where the amplitude of a high frequency signal is modulated by the phase of low frequency oscillations. The existing methods for assessing PAC have some limitations including limited frequency resolution and sensitivity to noise, data length and sampling rate due to the inherent dependence on bandpass filtering. In this paper, we propose a new time-frequency based PAC (t-f PAC) measure that can address these issues. The proposed method relies on a complex time-frequency distribution, known as the Reduced Interference Distribution (RID)-Rihaczek distribution, to estimate both the phase and the envelope of low and high frequency oscillations, respectively. As such, it does not rely on bandpass filtering and possesses some of the desirable properties of time-frequency distributions such as high frequency resolution. The proposed technique is first evaluated for simulated data and then applied to an EEG speeded reaction task dataset. The results illustrate that the proposed time-frequency based PAC is more robust to varying signal parameters and provides a more accurate measure of coupling strength.

Method for spike detection from microelectrode array recordings contaminated by artifacts of simultaneous two-photon imaging

The simultaneous utilization of electrophysiological recordings and two-photon imaging allows the observation of neural activity in a high temporal and spatial resolution at the same time. The three dimensional monitoring of morphological features near the microelectrode array makes the observation more precise and complex. In vitro experiments were performed on mice neocortical slices expressing the GCaMP6 genetically encoded calcium indicator for monitoring the neural activity with two-photon microscopy around the implanted microelectrodes. A special filtering algorithm was used for data analysis to eliminate the artefacts caused by the imaging laser. Utilization of a special filtering algorithm allowed us to detect and sort single unit activities from simultaneous two-photon imaging and electrophysiological measurement.

Spontaneous Variation in Electrocorticographic Resting-State Connectivity

Prior studies using functional magnetic resonance imaging, electroencephalography, and magnetoencephalography have observed both structured patterns in resting-state functional connectivity and spontaneous longitudinal variation in connectivity patterns independent of a task. In this first study using electrocorticography (ECoG), we characterized spontaneous, intersession variation in resting-state functional connectivity not linked to a task. We evaluated pairwise connectivity between electrodes using three measures (phase locking value [PLV], amplitude correlation, and coherence) for six canonical frequency bands, capturing different characteristics of time-evolving signals. We grouped electrodes into 10 functional regions and used intraclass correlation (ICC) to estimate pairwise longitudinal stability. We found that stronger PLV (PLV ≥0.4) in theta through gamma bands and strong correlation in all bands (R²'s ≥0.6) are linked to substantial stability (ICC ≥0.6), but that stability does not imply strong phase locking or amplitude correlation. There was no notable link between strong coherence and high ICC. All within-region PLVs are markedly stable across frequencies. In addition, we highlight interaction patterns across several regions: parahippocampal/entorhinal cortex is characterized by stable, weak functional connectivity except self-connections. Dorsolateral prefrontal cortex connectivity is weak and unstable, except self-connections. Inferior parietal lobule has little stability despite narrow connectivity bounds. We confirm prior studies linking functional connectivity strength and intersession variability, extending into higher frequencies than other modalities, with greater spatial specificity than scalp electrophysiology. We suggest further studies quantitatively compare ECoG to other modalities and/or use these findings as a baseline to capture functional connectivity and dynamics linked to perturbations with a task or disease state.

Sex differences in insular functional connectivity in response to noxious visceral stimulation in rats

Insular cortex (INS) plays a critical role in pain processing and shows sex differences in functional activation during noxious visceral stimulation. Less is known regarding functional interactions within the INS and between this structure and other parts of the brain. Cerebral blood flow mapping was performed using [¹⁴C]-iodoantipyrine perfusion autoradiography in male and female rats during colorectal distension (CRD) or no distension (controls). Forty regions of interest (ROIs) were defined anatomically to represent the granular, dysgranular, and agranular INS along the anterior-posterior (A-P) axis. Inter-ROI correlation matrices were calculated for each group to characterize intra-insular functional connectivity (FC). Results showed a clear FC segregation within the INS into an anterior (rostral to bregma +2.4 mm), a posterior (caudal to bregma -1.2 mm), and a mid INS subregion in between. Female controls showed higher FC density compared to males. During CRD, intra-insular FC density decreased greatly in females, but only modestly in males, with a loss of long-range connections between the anterior and mid INS noted in both sexes. New functional organization was characterized in both sexes by a cluster in the mid INS and primarily short-range FC along the A-P axis. Seed correlation analysis during CRD showed sex differences in FC of the anterior and mid agranular INS with the medial prefrontal cortex, thalamus, and brainstem areas (periaqueductal gray, parabrachial nucleus), suggesting sex differences in the modulatory aspect of visceral pain processing. Our findings suggest presence of substantial sex differences in visceral pain processing at the level of the insula.

Physiological significance of R-fMRI indices: Can functional metrics differentiate structural lesions (brain tumors)?

Resting-state functional MRI (R-fMRI) research has recently entered the era of “big data”, however, few studies have provided a rigorous validation of the physiological underpinnings of R-fMRI indices. Although studies have reported that various neuropsychiatric disorders exhibit abnormalities in R-fMRI measures, these “biomarkers” have not been validated in differentiating structural lesions (brain tumors) as a concept proof. We enrolled 60 patients with intracranial tumors located in the unilateral cranialcavity and 60 matched normal controls to test whether R-fMRI indices can differentiate tumors, which represents a prerequisite for adapting such indices as biomarkers for neuropsychiatric disorders. Common R-fMRI indices of tumors and their counterpart control regions, which were defined as the contralateral normal areas (for amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and degree centrality (DC)) and ipsilateral regions surrounding the tumors (for voxel-mirrored homotopic connectivity (VMHC)), were comprehensively assessed. According to robust paired t-tests with a Bonferroni correction, only VMHC (Fisher's r-to-z transformed) could successfully differentiate substantial tumors from their counterpart normal regions in patients. Furthermore, ALFF and DC were not able to differentiate tumor from normal unless Z-standardization was employed. To validate the lower power of the between-subject design compared to the within-subject design, each metric was calculated in a matched control group, and robust two-sample t-tests were used to compare the patient tumors and the normal controls at the same place. Similarly, only VMHC succeeded in differentiating significant differences between tumors and the sham tumor areas of normal controls. This study tested the premise of R-fMRI biomarkers for differentiating lesions, and brings a new understanding to physical significance of the Z-standardization.

•

R-fMRI indices could differentiate tumors, validating their physical availability.

•

ALFF and DC could not differentiate tumors unless Z-standardization was employed.

•

Within-subject design is more powerful for R-fMRI indices in differentiating tumors.

A Tutorial for Information Theory in Neuroscience

Understanding how neural systems integrate, encode, and compute information is central to understanding brain function. Frequently, data from neuroscience experiments are multivariate, the interactions between the variables are nonlinear, and the landscape of hypothesized or possible interactions between variables is extremely broad. Information theory is well suited to address these types of data, as it possesses multivariate analysis tools, it can be applied to many different types of data, it can capture nonlinear interactions, and it does not require assumptions about the structure of the underlying data (i.e., it is model independent). In this article, we walk through the mathematics of information theory along with common logistical problems associated with data type, data binning, data quantity requirements, bias, and significance testing. Next, we analyze models inspired by canonical neuroscience experiments to improve understanding and demonstrate the strengths of information theory analyses. To facilitate the use of information theory analyses, and an understanding of how these analyses are implemented, we also provide a free MATLAB software package that can be applied to a wide range of data from neuroscience experiments, as well as from other fields of study.

The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis

Higher-order cognition emerges through the flexible interactions of large-scale brain networks, an aspect of temporal coordination that may be impaired in psychosis. Here, we map the dynamic functional architecture of the cerebral cortex in healthy young adults, leveraging this atlas of transient network configurations (states), to identify state- and network-specific disruptions in patients with schizophrenia and psychotic bipolar disorder. We demonstrate that dynamic connectivity profiles are reliable within participants, and can act as a fingerprint, identifying specific individuals within a larger group. Patients with psychotic illness exhibit intermittent disruptions within cortical networks previously associated with the disease, and the individual connectivity profiles within specific brain states predict the presence of active psychotic symptoms. Taken together, these results provide evidence for a reconfigurable dynamic architecture in the general population and suggest that prior reports of network disruptions in psychosis may reflect symptom-relevant transient abnormalities, rather than a time-invariant global deficit.

Temporal changes in brain dynamics are linked with cognitive abilities, but neither their stability nor relationship to psychosis is clear. Here, authors describe the dynamic neural architecture in healthy controls and patients with psychosis and find that they are stable over time and can predict psychotic symptoms.

An interspecies comparative study of invasive electrophysiological functional connectivity

INTRODUCTION

Resting-state connectivity patterns have been observed in humans and other mammal species, and can be recorded using a variety of different technologies. Functional connectivity has been previously compared between species using resting-state fMRI, but not in electrophysiological studies.

METHODS

We compared connectivity with implanted electrodes in humans (electrocorticography) to macaques and sheep (microelectrocorticography), which are capable of recording neural data at high frequencies with spatial precision. We specifically examined synchrony, implicated in functional integration between regions.

RESULTS

We found that connectivity strength was overwhelmingly similar in humans and monkeys for pairs of two different brain regions (prefrontal, motor, premotor, parietal), but differed more often within single brain regions. The two connectivity measures, correlation and phase locking value, were similar in most comparisons. Connectivity strength agreed more often between the species at higher frequencies. Where the species differed, monkey synchrony was stronger than human in all but one case. In contrast, human and sheep connectivity within somatosensory cortex diverged in almost all frequencies, with human connectivity stronger than sheep.

DISCUSSION

Our findings imply greater heterogeneity within regions in humans than in monkeys, but comparable functional interactions between regions in the two species. This suggests that monkeys may be effectively used to probe resting-state connectivity in humans, and that such findings can then be validated in humans. Although the discrepancy between humans and sheep is larger, we suggest that findings from sheep in highly invasive studies may be used to provide guidance for studies in other species.

What is feasible with imaging human brain function and connectivity using functional magnetic resonance imaging

When we consider all of the methods we employ to detect brain function, from electrophysiology to optical techniques to functional magnetic resonance imaging (fMRI), we do not really have a 'golden technique' that meets all of the needs for studying the brain. We have methods, each of which has significant limitations but provide often complimentary information. Clearly, there are many questions that need to be answered about fMRI, which unlike other methods, allows us to study the human brain. However, there are also extraordinary accomplishments or demonstration of the feasibility of reaching new and previously unexpected scales of function in the human brain. This article reviews some of the work we have pursued, often with extensive collaborations with other co-workers, towards understanding the underlying mechanisms of the methodology, defining its limitations, and developing solutions to advance it. No doubt, our knowledge of human brain function has vastly expanded since the introduction of fMRI. However, methods and instrumentation in this dynamic field have evolved to a state that discoveries about the human brain based on fMRI principles, together with information garnered at a much finer spatial and temporal scale through other methods, are poised to significantly accelerate in the next decade.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

On the role of the corpus callosum in interhemispheric functional connectivity in humans

The relation between structural and functional connectivity has profound implications for our understanding of cerebral physiology and cognitive neuroscience. Yet, this relation remains incompletely understood. Cases in which the corpus callosum is sectioned for medical reasons provide a unique opportunity to study this question. We report functional connectivity assessed before and after surgical section of the corpus callosum, including multiyear follow-up in a limited subsample. Our results demonstrate a causal role for the corpus callosum in maintaining functional connectivity between the hemispheres. Additionally, comparison of results obtained in complete vs. partial callosotomy demonstrate that polysynaptic connections also play a role in maintaining interhemispheric functional connectivity.

Resting state functional connectivity is defined in terms of temporal correlations between physiologic signals, most commonly studied using functional magnetic resonance imaging. Major features of functional connectivity correspond to structural (axonal) connectivity. However, this relation is not one-to-one. Interhemispheric functional connectivity in relation to the corpus callosum presents a case in point. Specifically, several reports have documented nearly intact interhemispheric functional connectivity in individuals in whom the corpus callosum (the major commissure between the hemispheres) never develops. To investigate this question, we assessed functional connectivity before and after surgical section of the corpus callosum in 22 patients with medically refractory epilepsy. Section of the corpus callosum markedly reduced interhemispheric functional connectivity. This effect was more profound in multimodal associative areas in the frontal and parietal lobe than primary regions of sensorimotor and visual function. Moreover, no evidence of recovery was observed in a limited sample in which multiyear, longitudinal follow-up was obtained. Comparison of partial vs. complete callosotomy revealed several effects implying the existence of polysynaptic functional connectivity between remote brain regions. Thus, our results demonstrate that callosal as well as extracallosal anatomical connections play a role in the maintenance of interhemispheric functional connectivity.

Electrophysiological impact of multiple concussions in asymptomatic athletes: A re-analysis based on alpha activity during a visual-spatial attention task

Most EEG studies used event-related potentials to assess long-term and cumulative effects of sport-related concussions on brain activity. Time-frequency methods provide another approach that allows the detection of subtle shifts in types and patterns of brain oscillations. We sought to discover whether event-related alpha activity would be significantly affected in asymptomatic multi-concussed athletes. We measured the amplitude of alpha activity (8-12Hz) from the EEG recorded during a visual-spatial attention task to compare event-related alpha perturbations in 13 multi-concussed athletes and 14 age-equivalent, non-concussed teammates. Relative to non-concussed athletes, multi-concussed athletes showed significantly less event-related perturbations time-locked to stimulus presentation. Alpha activity alterations were closely related to the number of concussions sustained. Event-related alpha activity differed in asymptomatic multi-concussed athletes when compared to controls. Our study suggests that low-level neurophysiological underpinnings of the deployment of visual-spatial attention are affected in multi-concussed athletes even though their last concussion occurred on average 30 months prior to testing.

BCI Use and Its Relation to Adaptation in Cortical Networks

Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control. We discuss the previous research identifying patterns of neural activity associated with BCI skill acquisition and use that closely resembles those associated with learning traditional native motor tasks. We then discuss recent work in animals probing changes in connectivity of the BCI control site, which were linked to BCI skill acquisition, and use this as a foundation for our original work in humans. We present our novel work showing changes in resting state connectivity across cortex following the BCI learning process. We find substantial, heterogeneous changes in connectivity across regions and frequencies, including interactions that do not involve the BCI control site. We conclude from our review and original work that BCI skill acquisition may potentially lead to significant changes in evoked and resting state connectivity across multiple cortical regions. We recommend that future studies of BCIs look beyond motor regions to fully describe the cortical networks involved and long-term adaptations resulting from BCI skill acquisition.

The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.

Frequency-specific electrophysiologic correlates of resting state fMRI networks

Resting state functional MRI (R-fMRI) studies have shown that slow (< 0.1 Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4–8 Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8–12 Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs.

Investigating resting-state functional connectivity in the cervical spinal cord at 3T

The study of spontaneous fluctuations in the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord. Two ultra-high field functional magnetic resonance imaging (fMRI) studies in humans have provided evidence for reproducible resting-state connectivity between the dorsal horns as well as between the ventral horns, and a study in non-human primates has shown that these resting-state signals are impacted by spinal cord injury. As these studies were carried out at ultra-high field strengths using region-of-interest (ROI) based analyses, we investigated whether such resting-state signals could also be observed at the clinically more prevalent field strength of 3 T. In a reanalysis of a sample of 20 healthy human participants who underwent a resting-state fMRI acquisition of the cervical spinal cord, we were able to observe significant dorsal horn connectivity as well as ventral horn connectivity, but no consistent effects for connectivity between dorsal and ventral horns, thus replicating the human 7 T results. These effects were not only observable when averaging along the acquired length of the spinal cord, but also when we examined each of the acquired spinal segments separately, which showed similar patterns of connectivity. Finally, we investigated the robustness of these resting-state signals against variations in the analysis pipeline by varying the type of ROI creation, temporal filtering, nuisance regression and connectivity metric. We observed that – apart from the effects of band-pass filtering – ventral horn connectivity showed excellent robustness, whereas dorsal horn connectivity showed moderate robustness. Together, our results provide evidence that spinal cord resting-state connectivity is a robust and spatially consistent phenomenon that could be a valuable tool for investigating the effects of pathology, disease progression, and treatment response in neurological conditions with a spinal component, such as spinal cord injury.

Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies

The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.

Resting-State Magnetoencephalography Reveals Different Patterns of Aberrant Functional Connectivity in Combat-Related Mild Traumatic Brain Injury

Blast mild traumatic brain injury (mTBI) is a leading cause of sustained impairment in military service members and veterans. However, the mechanism of persistent disability is not fully understood. The present study investigated disturbances in brain functioning in mTBI participants using a source-imaging-based approach to analyze functional connectivity (FC) from resting-state magnetoencephalography (rs-MEG). Study participants included 26 active-duty service members or veterans who had blast mTBI with persistent post-concussive symptoms, and 22 healthy control active-duty service members or veterans. The source time courses from regions of interest (ROIs) were used to compute ROI to whole-brain (ROI-global) FC for different frequency bands using two different measures: 1) time-lagged cross-correlation and 2) phase-lock synchrony. Compared with the controls, blast mTBI participants showed increased ROI-global FC in beta, gamma, and low-frequency bands, but not in the alpha band. Sources of abnormally increased FC included the: 1) prefrontal cortex (right ventromedial prefrontal cortex [vmPFC], right rostral anterior cingulate cortex [rACC]), and left ventrolateral and dorsolateral prefrontal cortex; 2) medial temporal lobe (bilateral parahippocampus, hippocampus, and amygdala); and 3) right putamen and cerebellum. In contrast, the blast mTBI group also showed decreased FC of the right frontal pole. Group differences were highly consistent across the two different FC measures. FC of the left ventrolateral prefrontal cortex correlated with executive functioning and processing speed in mTBI participants. Altogether, our findings of increased and decreased regionalpatterns of FC suggest that disturbances in intrinsic brain connectivity may be the result of multiple mechanisms, and are associated with cognitive sequelae of the injury.

Optimising experimental design for MEG resting state functional connectivity measurement

The study of functional connectivity using magnetoencephalography (MEG) is an expanding area of neuroimaging, and adds an extra dimension to the more common assessments made using fMRI. The importance of such metrics is growing, with recent demonstrations of their utility in clinical research, however previous reports suggest that whilst group level resting state connectivity is robust, single session recordings lack repeatability. Such robustness is critical if MEG measures in individual subjects are to prove clinically valuable. In the present paper, we test how practical aspects of experimental design affect the intra-subject repeatability of MEG findings; specifically we assess the effect of co-registration method and data recording duration. We show that the use of a foam head-cast, which is known to improve co-registration accuracy, increased significantly the between session repeatability of both beamformer reconstruction and connectivity estimation. We also show that recording duration is a critical parameter, with large improvements in repeatability apparent when using ten minute, compared to five minute recordings. Further analyses suggest that the origin of this latter effect is not underpinned by technical aspects of source reconstruction, but rather by a genuine effect of brain state; short recordings are simply inefficient at capturing the canonical MEG network in a single subject. Our results provide important insights on experimental design and will prove valuable for future MEG connectivity studies.

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

Relationships between cortical myeloarchitecture and electrophysiological networks

The human brain relies upon the dynamic formation and dissolution of a hierarchy of functional networks to support ongoing cognition. However, how functional connectivities underlying such networks are supported by cortical microstructure remains poorly understood. Recent animal work has demonstrated that electrical activity promotes myelination. Inspired by this, we test a hypothesis that gray-matter myelin is related to electrophysiological connectivity. Using ultra-high field MRI and the principle of structural covariance, we derive a structural network showing how myelin density differs across cortical regions and how separate regions can exhibit similar myeloarchitecture. Building upon recent evidence that neural oscillations mediate connectivity, we use magnetoencephalography to elucidate networks that represent the major electrophysiological pathways of communication in the brain. Finally, we show that a significant relationship exists between our functional and structural networks; this relationship differs as a function of neural oscillatory frequency and becomes stronger when integrating oscillations over frequency bands. Our study sheds light on the way in which cortical microstructure supports functional networks. Further, it paves the way for future investigations of the gray-matter structure/function relationship and its breakdown in pathology.

Measurement of dynamic task related functional networks using MEG

The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

•

A method is developed to track dynamic electrophysiological networks using MEG.

•

Method based on ICA applied to timecourses measuring evolution of connectivity.

•

Method allows a unique picture of transient networks that support cognition.

•

Method validated in MEG data recorded during a Sternberg working memory task.

•

Sensory networks observed include visual and sensorimotor.

•

Cognitive networks relate to semantic processing, pattern recognition and language.

Preliminary differences in resting state MEG functional connectivity pre- and post-ketamine in major depressive disorder

Functional neuroimaging techniques including magnetoencephalography (MEG) have demonstrated that the brain is organized into networks displaying correlated activity. Group connectivity differences between healthy controls and participants with major depressive disorder (MDD) can be detected using temporal independent components analysis (ICA) on beta-bandpass filtered Hilbert envelope MEG data. However, the response of these networks to treatment is unknown. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, exerts rapid antidepressant effects. We obtained MEG recordings before and after open-label infusion of 0.5mg/kg ketamine in MDD subjects (N=13) and examined networks previously shown to differ between healthy individuals and those with MDD. Connectivity between the amygdala and an insulo-temporal component decreased post-ketamine in MDD subjects towards that observed in control subjects at baseline. Decreased baseline connectivity of the subgenual anterior cingulate cortex (sgACC) with a bilateral precentral network had previously been observed in MDD compared to healthy controls, and the change in connectivity post-ketamine was proportional to the change in sgACC glucose metabolism in a subset (N=8) of subjects receiving [11F]FDG-PET imaging. Ketamine appeared to reduce connectivity, regardless of whether connectivity was abnormally high or low compared to controls at baseline. These preliminary findings suggest that sgACC connectivity may be directly related to glutamate levels.

On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study

Magnetoencephalography (MEG) is a sophisticated tool which yields rich information on the spatial, spectral and temporal signatures of human brain function. Despite unique potential, MEG is limited by a low signal-to-noise ratio (SNR) which is caused by both the inherently small magnetic fields generated by the brain, and the scalp-to-sensor distance. The latter is limited in current systems due to a requirement for pickup coils to be cryogenically cooled. Recent work suggests that optically-pumped magnetometers (OPMs) might be a viable alternative to superconducting detectors for MEG measurement. They have the advantage that sensors can be brought to within ~4 mm of the scalp, thus offering increased sensitivity. Here, using simulations, we quantify the advantages of hypothetical OPM systems in terms of sensitivity, reconstruction accuracy and spatial resolution. Our results show that a multi-channel whole-head OPM system offers (on average) a fivefold improvement in sensitivity for an adult brain, as well as clear improvements in reconstruction accuracy and spatial resolution. However, we also show that such improvements depend critically on accurate forward models; indeed, the reconstruction accuracy of our simulated OPM system only outperformed that of a simulated superconducting system in cases where forward field error was less than 5%. Overall, our results imply that the realisation of a viable whole-head multi-channel OPM system could generate a step change in the utility of MEG as a means to assess brain electrophysiological activity in health and disease. However in practice, this will require both improved hardware and modelling algorithms.

Ongoing Slow Fluctuations in V1 Impact on Visual Perception

The human brain’s ongoing activity is characterized by intrinsic networks of coherent fluctuations, measured for example with correlated functional magnetic resonance imaging signals. So far, however, the brain processes underlying this ongoing blood oxygenation level dependent (BOLD) signal orchestration and their direct relevance for human behavior are not sufficiently understood. In this study, we address the question of whether and how ongoing BOLD activity within intrinsic occipital networks impacts on conscious visual perception. To this end, backwardly masked targets were presented in participants’ left visual field only, leaving the ipsi-lateral occipital areas entirely free from direct effects of task throughout the experiment. Signal time courses of ipsi-lateral BOLD fluctuations in visual areas V1 and V2 were then used as proxies for the ongoing contra-lateral BOLD activity within the bilateral networks. Magnitude and phase of these fluctuations were compared in trials with and without conscious visual perception, operationalized by means of subjective confidence ratings. Our results show that ipsi-lateral BOLD magnitudes in V1 were significantly higher at times of peak response when the target was perceived consciously. A significant difference between conscious and non-conscious perception with regard to the pre-target phase of an intrinsic-frequency regime suggests that ongoing V1 fluctuations exert a decisive impact on the access to consciousness already before stimulation. Both effects were absent in V2. These results thus support the notion that ongoing slow BOLD activity within intrinsic networks covering V1 represents localized processes that modulate the degree of readiness for the emergence of visual consciousness.

Resting-state test-retest reliability of a priori defined canonical networks over different preprocessing steps

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test-retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test-retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test-retest reliability and removing variance that may be attributable to non-neuronal sources.

In Vivo Tumour Mapping Using Electrocorticography Alterations During Awake Brain Surgery: A Pilot Study

During awake brain surgery for tumour resection, in situ EEG recording (ECoG) is used to identify eloquent areas surrounding the tumour. We used the ECoG setup to record the electrical activity of cortical and subcortical tumours and then performed frequency and connectivity analyses in order to identify ECoG impairments and map tumours. We selected 16 patients with cortical (8) and subcortical (8) tumours undergoing awake brain surgery. For each patient, we computed the spectral content of tumoural and healthy areas in each frequency band. We computed connectivity of each electrode using connectivity markers (linear and non-linear correlations, phase-locking and coherence). We performed comparisons between healthy and tumour electrodes. The ECoG alterations were used to implement automated classification of the electrodes using clustering or neural network algorithms. ECoG alterations were used to image cortical tumours.Cortical tumours were found to profoundly alter all frequency contents (normalized and absolute power), with an increase in the δ activity and a decreases for the other bands (P < 0.05). Cortical tumour electrodes showed high level of connectivity compared to surrounding electrodes (all markers, P < 0.05). For subcortical tumours, a relative decrease in the γ1 band and in the alpha band in absolute amplitude (P < 0.05) were the only abnormalities. The neural network algorithm classification had a good performance: 93.6 % of the electrodes were classified adequately on a test subject. We found significant spectral and connectivity ECoG changes for cortical tumours, which allowed tumour recognition. Artificial neural algorithm pattern recognition seems promising for electrode classification in awake tumour surgery.

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

Regional Patterns of Cortical Phase Synchrony in the Resting State

Synchronized phase estimates between oscillating neuronal signals at the macroscale level reflect coordinated activities between neuronal assemblies. Recent electrophysiological evidence suggests the presence of significant spontaneous phase synchrony within the resting state. The purpose of this study was to investigate phase synchrony, including directional interactions, in resting state subdural electrocorticographic recordings to better characterize patterns of regional phase interactions across the lateral cortical surface during the resting state. We estimated spontaneous phase locking value (PLV) as a measure of functional connectivity, and phase slope index (PSI) as a measure of pseudo-causal phase interactions, across a broad range of canonical frequency bands and the modulation of the amplitude envelope of high gamma (amHG), a band that is believed to best reflect the physiological processes giving rise to the functional magnetic resonance imaging BOLD signal. Long-distance interactions had higher PLVs in slower frequencies (≤theta) than in higher ones (≥beta) with amHG behaving more like slow frequencies, and a general trend of increasing frequency band of significant PLVs when moving across the lateral surface along an anterior-posterior axis. Moreover, there was a strong trend of frontal-to-parietal directional phase synchronization, measured by PSI across multiple frequencies. These findings, which are likely indicative of coordinated and structured spontaneous cortical interactions, are important in the study of time scales and directional nature of resting state functional connectivity, and may ultimately contribute to a better understanding of how spontaneous synchrony is linked to variation in regional architecture across the lateral cortical surface.

MEG Connectivity and Power Detections with Minimum Norm Estimates Require Different Regularization Parameters

Minimum Norm Estimation (MNE) is an inverse solution method widely used to reconstruct the source time series that underlie magnetoencephalography (MEG) data. MNE addresses the ill-posed nature of MEG source estimation through regularization (e.g., Tikhonov regularization). Selecting the best regularization parameter is a critical step. Generally, once set, it is common practice to keep the same coefficient throughout a study. However, it is yet to be known whether the optimal lambda for spectral power analysis of MEG source data coincides with the optimal regularization for source-level oscillatory coupling analysis. We addressed this question via extensive Monte-Carlo simulations of MEG data, where we generated 21,600 configurations of pairs of coupled sources with varying sizes, signal-to-noise ratio (SNR), and coupling strengths. Then, we searched for the Tikhonov regularization coefficients (lambda) that maximize detection performance for (a) power and (b) coherence. For coherence, the optimal lambda was two orders of magnitude smaller than the best lambda for power. Moreover, we found that the spatial extent of the interacting sources and SNR, but not the extent of coupling, were the main parameters affecting the best choice for lambda. Our findings suggest using less regularization when measuring oscillatory coupling compared to power estimation.

Spontaneous Neural Dynamics and Multi-scale Network Organization

Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire.

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology.

•

We introduce a mathematical model to predict fMRI-based RSNs using MEG.

•

Our model is based on a multi-variate Taylor series expansion.

•

The electrophysiological basis of RSNs goes beyond frequency-band specific analysis.

•

RSNs result 1) from multiple frequency bands and cross-frequency coupling.

•

RSNs result 2) from direct and shared electrophysiological connectivity.

Gamma oscillation in functional brain networks is involved in the spontaneous remission of depressive behavior induced by chronic restraint stress in mice

BACKGROUND

Depression is one of the most prevalent mood disorders, and is known to be associated with abnormal functional connectivity in neural networks of the brain. Interestingly, a significant proportion of patients with depression experience spontaneous remission without any treatment. However, the relationship between electroencephalographic (EEG) functional connectivity and the spontaneous remission in depression remains poorly understood. Here, we investigated regional and network brain activity using EEG signals from a chronic restraint stress (CRS)-induced mouse model of depression. After 1 (CRS1W) or 3 weeks (CRS3W) following the cessation of a 4-week-long CRS, mice were subjected to depression-associated behavioral tasks. EEG signals were obtained from eight cortical regions (frontal, somatosensory, parietal, and visual cortices in each hemisphere).

RESULTS

The CRS1W group exhibited behavioral dysfunctions in the open field and forced swim tasks, whereas the CRS3W group displayed normal levels of behaviors in those tasks. In a linear correlation analysis, the CRS1W group exhibited increased correlation coefficient values at all frequency bands (delta, 1.5-4; theta, 4-8; alpha, 8-12; beta, 12-30; gamma, 30-80 Hz) compared with the control group. However, the differences in delta- and gamma-frequency bands between the control and CRS1W groups were no longer observed in the CRS3W group. Persistent brain network homology revealed significantly different functional connectivity between the control and CRS1W groups, and it demonstrated a huge restoration of the decreased distances in the gamma-frequency band for the CRS3W group. Moreover, the CRS3W group displayed a similar strength of connectivity among somatosensory and frontal cortices as the control group.

CONCLUSION

A mouse model of CRS-induced depression showed spontaneous behavioral remission of depressive behavior. Using persistent brain network homology analysis of EEG signals from eight cortical regions, we found that restoration of gamma activity at the network level is associated with behavioral remission.

Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity

Functional imaging investigations into the brain's resting state interactions have yielded a wealth of insight into the intrinsic and dynamic neural architecture supporting cognition and behavior. Electrophysiological studies however have highlighted the fact that synchrony across large-scale cortical systems is composed of spontaneous interactions occurring at timescales beyond the traditional resolution of fMRI, a feature that limits the capacity of fMRI to draw inference on the true directional relationship between network nodes. To approach the question of directionality in resting state signals, we recorded resting state functional MRI (rsfMRI) and electrocorticography (ECoG) from four human subjects undergoing invasive epilepsy monitoring. Using a seed-point based approach, we employed phase-amplitude coupling (PAC) and biPhase Locking Values (bPLV), two measures of cross-frequency coupling (CFC) to explore both outgoing and incoming connections between the seed and all non-seed, site electrodes. We observed robust PAC between a wide range of low-frequency phase and high frequency amplitude estimates. However, significant bPLV, a CFC measure of phase-phase synchrony, was only observed at specific narrow low and high frequency bandwidths. Furthermore, the spatial patterns of outgoing PAC connectivity were most closely associated with the rsfMRI connectivity maps. Our results support the hypothesis that PAC is relatively ubiquitous phenomenon serving as a mechanism for coordinating high-frequency amplitudes across distant neuronal assemblies even in absence of overt task structure. Additionally, we demonstrate that the spatial distribution of a seed-point rsfMRI sensorimotor network is strikingly similar to specific patterns of directional PAC. Specifically, the high frequency activities of distal patches of cortex owning membership in a rsfMRI sensorimotor network were most likely to be entrained to the phase of a low frequency rhythm engendered from the neural populations at the seed-point, suggestive of greater directional coupling from the seed out to the site electrodes.