The role of sub-second neural events in spontaneous brain activity

Differential cortical activation patterns: pioneering sub-classification of tinnitus with and without hyperacusis by combining audiometry, gamma oscillations, and hemodynamics

The ongoing controversies about the neural basis of tinnitus, whether linked with central neural gain or not, may hamper efforts to develop therapies. We asked to what extent measurable audiometric characteristics of tinnitus without (T) or with co-occurrence of hyperacusis (TH) are distinguishable on the level of cortical responses. To accomplish this, electroencephalography (EEG) and concurrent functional near-infrared spectroscopy (fNIRS) were measured while patients performed an attentionally demanding auditory discrimination task using stimuli within the individual tinnitus frequency (fTin) and a reference frequency (fRef). Resting-state-fMRI-based functional connectivity (rs-fMRI-bfc) in ascending auditory nuclei (AAN), the primary auditory cortex (AC-I), and four other regions relevant for directing attention or regulating distress in temporal, parietal, and prefrontal cortex was compiled and compared to EEG and concurrent fNIRS activity in the same brain areas. We observed no group differences in pure-tone audiometry (PTA) between 10 and 16 kHz. However, the PTA threshold around the tinnitus pitch was positively correlated with the self-rated tinnitus loudness and also correlated with distress in T-groups, while TH experienced their tinnitus loudness at minimal loudness levels already with maximal suffering scores. The T-group exhibited prolonged auditory brain stem (ABR) wave I latency and reduced ABR wave V amplitudes (indicating reduced neural synchrony in the brainstem), which were associated with lower rs-fMRI-bfc between AAN and the AC-I, as observed in previous studies. In T-subjects, these features were linked with elevated spontaneous and reduced evoked gamma oscillations and with reduced deoxygenated hemoglobin (deoxy-Hb) concentrations in response to stimulation with lower frequencies in temporal cortex (Brodmann area (BA) 41, 42, 22), implying less synchronous auditory responses during active auditory discrimination of reference frequencies. In contrast, in the TH-group gamma oscillations and hemodynamic responses in temporoparietal regions were reversed during active discrimination of tinnitus frequencies. Our findings suggest that T and TH differ in auditory discrimination and memory-dependent directed attention during active discrimination at either tinnitus or reference frequencies, offering a test paradigm that may allow for more precise sub-classification of tinnitus and future improved treatment approaches.

Cortical network formation based on subthalamic beta bursts in Parkinson's disease

Recent evidence suggests that beta bursts in subthalamic nucleus (STN) play an important role in Parkinsonian pathophysiology. We studied the spatio-temporal relationship between STN beta bursts and cortical activity in 26 Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery. Postoperatively, we simultaneously recorded STN local field potentials (LFP) from externalized DBS leads and cortical activity using whole-brain magnetoencephalography. Event-related magnetic fields (ERF) were averaged time-locked to STN beta bursts and subjected to source localization. Our results demonstrate that ERF exhibiting activity significantly different from baseline activity were localized within areas functionally related to associative, limbic, and motor systems as well as regions pertinent for visual and language processing. Our data suggest that STN beta bursts are involved in network formation between STN and cortex. This interaction is in line with the idea of parallel processing within the basal ganglia-cortex loop, specifically within the functional subsystems of the STN (i.e., associative, limbic, motor, and the related cortical areas). ERFs within visual and language-related cortical areas indicate involvement of beta bursts in STN-cortex networks beyond the associative, limbic, and motor loops. In sum, our results highlight the involvement of STN beta bursts in the formation of multiple STN - cortex loops in patients with PD.

Differential dopaminergic modulation of spontaneous cortico-subthalamic activity in Parkinson's disease

Pathological oscillations including elevated beta activity in the subthalamic nucleus (STN) and between STN and cortical areas are a hallmark of neural activity in Parkinson’s disease (PD). Oscillations also play an important role in normal physiological processes and serve distinct functional roles at different points in time. We characterised the effect of dopaminergic medication on oscillatory whole-brain networks in PD in a time-resolved manner by employing a hidden Markov model on combined STN local field potentials and magnetoencephalography (MEG) recordings from 17 PD patients. Dopaminergic medication led to coherence within the medial and orbitofrontal cortex in the delta/theta frequency range. This is in line with known side effects of dopamine treatment such as deteriorated executive functions in PD. In addition, dopamine caused the beta band activity to switch from an STN-mediated motor network to a frontoparietal-mediated one. In contrast, dopamine did not modify local STN–STN coherence in PD. STN–STN synchrony emerged both on and off medication. By providing electrophysiological evidence for the differential effects of dopaminergic medication on the discovered networks, our findings open further avenues for electrical and pharmacological interventions in PD.

Shared and Distinct Functional Architectures of Brain Networks Across Psychiatric Disorders

Brain network alterations have increasingly been implicated in schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). However, little is known about the similarities and differences in functional brain networks among patients with SCZ, BD, and MDD. A total of 512 participants (121 with SCZ, 100 with BD, 108 with MDD, and 183 healthy controls, matched for age and sex) completed resting-state functional magnetic resonance imaging at a single site. Four global measures (the clustering coefficient, the characteristic shortest path length, the normalized clustering coefficient, and the normalized characteristic path length) were computed at a voxel level to quantify segregated and integrated configurations. Inter-regional functional associations were examined based on the Euclidean distance between regions. Distance strength maps were used to localize regions with altered distances based on functional connectivity. Patient groups exhibited shifts in their network architectures toward randomized configurations, with SCZ>BD>MDD in the degree of randomization. Patient groups displayed significantly decreased short-range connectivity and increased medium-/long-range connectivity. Decreases in short-range connectivity were similar across the SZ, BD, and MDD groups and were primarily distributed in the primary sensory and association cortices and the thalamus. Increases in medium-/long-range connectivity were differentially localized within the prefrontal cortices among the patient groups. We highlight shared and distinct connectivity features in functional brain networks among patients with SCZ, BD, and MDD, which expands our understanding of the common and distinct pathophysiological mechanisms and provides crucial insights into neuroimaging-based methods for the early diagnosis of and interventions for psychiatric disorders.

A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates

Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.

How networks communicate: propagation patterns in spontaneous brain activity

Initially regarded as 'noise', spontaneous (intrinsic) activity accounts for a large portion of the brain's metabolic cost. Moreover, it is now widely known that infra-slow (less than 0.1 Hz) spontaneous activity, measured using resting state functional magnetic resonance imaging of the blood oxygen level-dependent (BOLD) signal, is correlated within functionally defined resting state networks (RSNs). However, despite these advances, the temporal organization of spontaneous BOLD fluctuations has remained elusive. By studying temporal lags in the resting state BOLD signal, we have recently shown that spontaneous BOLD fluctuations consist of remarkably reproducible patterns of whole brain propagation. Embedded in these propagation patterns are unidirectional 'motifs' which, in turn, give rise to RSNs. Additionally, propagation patterns are markedly altered as a function of state, whether physiological or pathological. Understanding such propagation patterns will likely yield deeper insights into the role of spontaneous activity in brain function in health and disease.This article is part of the themed issue 'Interpreting blood oxygen level-dependent: a dialogue between cognitive and cellular neuroscience'.

Differing Time of Onset of Concurrent TMS-fMRI during Associative Memory Encoding: A Measure of Dynamic Connectivity

There has been a distinct shift in neuroimaging from localization of function into a more network based approach focused on connectivity. While fMRI has proven very fruitful for this, the hemodynamic signal is inherently slow which limits the temporal resolution of fMRI-only connectivity measures. The brain, however, works on a time scale of milliseconds. This study utilized concurrent transcranial magnetic stimulation (TMS)-fMRI in a novel way to obtain measures of dynamic connectivity by measuring changes in fMRI signal amplitude in regions distal to the site of stimulation following differing TMS onset times. Seventeen healthy subjects completed an associative memory encoding task known to involve the DLPFC, viewing pairs of objects which could be semantically related or unrelated. Three pulses of 10 Hz repetitive TMS were applied over the left DLPFC starting either at 200, 600, or 1000 ms after stimulus onset. Associations for related pairs were better remembered than unrelated pairs in a post-scan cued recall test. Differences in neural activity were assessed across different TMS onsets, separately for related and unrelated pairs. Time specific TMS effects were observed in several regions, including those associated with higher-level processing (lateral frontal, anterior cingulate), visual areas (occipital), and regions involved in semantic processing (e.g., left mid-temporal and medial frontal). Activity in the frontal cortex was decreased at 200 ms post-stimulus for unrelated pairs, and 1000 ms post-stimulus for related pairs. This suggests differences in the timing across conditions in which the DLFPC interacts with other PFC regions, consistent with the notion that the DLPFC is facilitating extended semantic processing for related items. This study demonstrates that time-varying TMS onset inside the MRI can be used to reliably measure fast dynamic connectivity with a temporal resolution in the hundreds of milliseconds.

Functional connectomics from a "big data" perspective

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field.

Hippocampal-prefrontal connectivity as a translational phenotype for schizophrenia

Finding novel biological targets in psychiatry has been difficult, partly because current diagnostic categories are not defined by pathophysiology and difficult to model in animals. The study of species-conserved systems-level mechanisms implicated in psychiatric disease could be a promising strategy to address some of these difficulties. Altered hippocampal-prefrontal (HC-PFC) connectivity during working memory (WM) processing is a candidate for such a translational phenotype as it has been repeatedly associated with impaired cognition in schizophrenia patients and animal models for psychiatric risk factors. Specifically, persistent hippocampus-dorsolateral prefrontal cortex (HC-DLPFC) coupling during WM is an intermediate phenotype for schizophrenia that has been observed in patients, healthy relatives and carriers of two different risk polymorphisms identified in genome-wide association studies. Rodent studies report reduced coherence between HC and PFC during anesthesia, sleep and task performance in both genetic, environmental and neurodevelopmental models for schizophrenia. We discuss several challenges for translation including differences in anatomy, recording modalities and WM paradigms and suggest that a better understanding of HC-PFC coupling across species can be achieved if translational neuroimaging is used to control for task differences. The evidence for potential neurobiological substrates underlying HC-PFC dysconnectivity is evaluated and research strategies are proposed that aim to bridge the gap between findings from large-scale association studies and disease mechanisms.

Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science

When blood oxygenation level-dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) was discovered in the early 1990s, it provoked an explosion of interest in exploring human cognition, using brain mapping techniques based on MRI. Standards for data acquisition and analysis were rapidly put in place, in order to assist comparison of results across laboratories. Recently, MRI data acquisition capabilities have improved dramatically, inviting a rethink of strategies for relating functional brain activity at the systems level with its neuronal substrates and functional connections. This paper reviews the established capabilities of BOLD contrast fMRI, the perceived weaknesses of major methods of analysis, and current results that may provide insights into improved brain modelling. These results have inspired the use of in vivo myeloarchitecture for localizing brain activity, individual subject analysis without spatial smoothing and mapping of changes in cerebral blood volume instead of BOLD activation changes. The apparent fundamental limitations of all methods based on nuclear magnetic resonance are also discussed.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.

N-methyl-D-aspartate receptor encephalitis mediates loss of intrinsic activity measured by functional MRI

Spontaneous brain activity is required for the development and maintenance of normal brain function. Many disease processes disrupt the organization of intrinsic brain activity, but few pervasively reduce the amplitude of resting state blood oxygen level dependent (BOLD) fMRI fluctuations. We report the case of a female with anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, longitudinally studied during the course of her illness to determine the contribution of NMDAR signaling to spontaneous brain activity. Resting state BOLD fMRI was measured at the height of her illness and 18 weeks following discharge from hospital. Conventional resting state networks were defined using established methods. Correlation and covariance matrices were calculated by extracting the BOLD time series from regions of interest and calculating either the correlation or covariance quantity. The intrinsic activity was compared between visits, and to expected activity from 45 similarly aged healthy individuals. Near the height of the illness, the patient exhibited profound loss of consciousness, high-amplitude slowing of the electroencephalogram, and a severe reduction in the amplitude of spontaneous BOLD fMRI fluctuations. The patient's neurological status and measures of intrinsic activity improved following treatment. We conclude that NMDAR-mediated signaling plays a critical role in the mechanisms that give rise to organized spontaneous brain activity. Loss of intrinsic activity is associated with profound disruptions of consciousness and cognition.

The brain timewise: how timing shapes and supports brain function

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

Propagation of slow intrinsic brain activity has been widely observed in electrophysiogical studies of slow wave sleep (SWS). However, in human resting state fMRI (rs-fMRI), intrinsic activity has been understood predominantly in terms of zero-lag temporal synchrony (functional connectivity) within systems known as resting state networks (RSNs). Prior rs-fMRI studies have found that RSNs are generally preserved across wake and sleep. Here, we use a recently developed analysis technique to study propagation of infra-slow intrinsic blood oxygen level dependent (BOLD) signals in normal adults during wake and SWS. This analysis reveals marked changes in propagation patterns in SWS vs. wake. Broadly, ordered propagation is preserved within traditionally defined RSNs but lost between RSNs. Additionally, propagation between cerebral cortex and subcortical structures reverses directions, and intra-cortical propagation becomes reorganized, especially in visual and sensorimotor cortices. These findings show that propagated rs-fMRI activity informs theoretical accounts of the neural functions of sleep.