Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

AbstractQuasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain’s intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual’s global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion (or even high motion) and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

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Opposing Changes in the Functional Architecture of Large-Scale Networks in Bipolar Mania and Depression

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa004 ◽

2020 ◽

Vol 46 (4) ◽

pp. 971-980

Author(s):

Daniel Russo ◽

Matteo Martino ◽

Paola Magioncalda ◽

Matilde Inglese ◽

Mario Amore ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Depressive Symptomatology ◽

Brain Activity ◽

Intrinsic Activity ◽

Functional Architecture ◽

Sensorimotor Network ◽

Large Scale Networks ◽

Intrinsic Brain Activity ◽

Affective Dimensions

Abstract Objective Manic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. Methods Using resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated—in the standard frequency band (SFB: 0.01–0.10 Hz), as well as in Slow5 (0.01–0.027 Hz) and Slow4 (0.027–0.073 Hz)—and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. Results Mania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. Conclusions Our data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).

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A dynamical systems framework to uncover the drivers of large-scale cortical activity

10.1101/638718 ◽

2019 ◽

Cited By ~ 3

Author(s):

Arian Ashourvan ◽

Sérgio Pequito ◽

Maxwell Bertolero ◽

Jason Z. Kim ◽

Danielle S. Bassett ◽

...

Keyword(s):

Large Scale ◽

Computational Models ◽

Functional Neuroimaging ◽

Brain Activity ◽

Intrinsic Activity ◽

Model Parameters ◽

Sensory Stimuli ◽

Human Connectome Project ◽

External Stimuli ◽

And Task

ABSTRACTA fundamental challenge in neuroscience is to uncover the principles governing complex interactions between the brain and its external environment. Over the past few decades, the development of functional neuroimaging techniques and tools from graph theory, network science, and computational neuroscience have markedly expanded opportunities to study the intrinsic organization of brain activity. However, many current computational models are fundamentally limited by little to no explicit assessment of the brain’s interactions with external stimuli. To address this limitation, we propose a simple scheme that jointly estimates the intrinsic organization of brain activity and extrinsic stimuli. Specifically, we adopt a linear dynamical model (intrinsic activity) under unknown exogenous inputs (e.g., sensory stimuli), and jointly estimate the model parameters and exogenous inputs. First, we demonstrate the utility of this scheme by accurately estimating unknown external stimuli in a synthetic example. Next, we examine brain activity at rest and task for 99 subjects from the Human Connectome Project, and find significant task-related changes in the identified system, and task-related increases in the estimated external inputs showing high similarity to known task regressors. Finally, through detailed examination of fluctuations in the spatial distribution of the oscillatory modes of the estimated system during the resting state, we find an apparent non-stationarity in the profile of modes that span several brain regions including the visual and the dorsal attention systems. The results suggest that these brain structures display a time-varying relationship, or alternatively, receive non-stationary exogenous inputs that can lead to apparent system non-stationarities. Together, our embodied model of brain activity provides an avenue to gain deeper insight into the relationship between cortical functional dynamics and their drivers.

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Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

10.1101/193862 ◽

2017 ◽

Cited By ~ 8

Author(s):

Matthew F. Glasser ◽

Timothy S. Coalson ◽

Janine D. Bijsterbosch ◽

Samuel J. Harrison ◽

Michael P. Harms ◽

...

Keyword(s):

Brain Activity ◽

Resting State Fmri ◽

Fmri Data ◽

Physiological Noise ◽

Temporal Noise ◽

Global Signal ◽

Human Connectome Project ◽

Before And After ◽

Components Analysis ◽

Subject Motion

AbstractTemporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal “noise” from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is mathematically, by design, unable to separate spatially widespread “global” structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary—to do or not to do global signal regression—given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the global positive biases caused by global physiological noise without inducing the network-specific negative biases of global signal regression. We believe that temporal ICA cleanup provides a “best of both worlds” solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

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Resting-state “Physiological Networks”

10.1101/660787 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jingyuan E. Chen ◽

Laura D. Lewis ◽

Catie Chang ◽

Nina E. Fultz ◽

Ned A. Ohringer ◽

...

Keyword(s):

Resting State ◽

Large Scale ◽

Physiological Responses ◽

Brain Regions ◽

Systematic Investigation ◽

Brain Physiology ◽

Temporal Correlations ◽

Global Signal ◽

Human Connectome Project ◽

Large Scale Networks

AbstractSlow changes in systemic brain physiology can elicit large fluctuations in fMRI time series, which may manifest as structured spatial patterns of temporal correlations between distant brain regions. These correlations can appear similar to large-scale networks typically attributed to coupled neuronal activity. However, little effort has been devoted to a systematic investigation of such “physiological networks”—sets of segregated brain regions that exhibit similar physiological responses—and their potential influence on estimates of resting-state brain networks. Here, by analyzing a large group of subjects from the 3T Human Connectome Project database, we demonstrate brain-wide and noticeably heterogenous dynamics attributable to either respiratory variation or heart rate changes. We show that these physiologic dynamics can give rise to apparent “connectivity” patterns that resemble previously reported resting-state networks derived from fMRI data. Further, we show that this apparent “physiological connectivity” cannot be removed by the use of a single nuisance regressor for the entire brain (such as global signal regression) due to the clear regional heterogeneity of the physiological responses. Possible mechanisms causing these apparent “physiological networks”, and their broad implications for interpreting functional connectivity studies are discussed.

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Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

10.1101/798819 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jianfeng Zhang ◽

Zirui Huang ◽

Shankar Tumati ◽

Georg Northoff

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Peak Period ◽

Physiological Noise ◽

Activation Patterns ◽

Global Signal ◽

Co Activation ◽

Human Connectome Project ◽

Partial Dissociation ◽

Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

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Brainstem modulation of large-scale intrinsic cortical activity correlations

10.31219/osf.io/tczwn ◽

2019 ◽

Author(s):

Ruud L. van den Brink ◽

Thomas Pfeffer ◽

Tobias Donner

Keyword(s):

Psychiatric Disorders ◽

Large Scale ◽

Brain Activity ◽

Cortical Activity ◽

Brain Regions ◽

Intrinsic Activity ◽

Non Invasive ◽

Correlated Activity ◽

Intrinsic Fluctuations ◽

Specific Receptors

Brain activity fluctuates continuously, even in the absence of changes in sensory input or motor output. These intrinsic activity fluctuations are correlated across brain regions and are spatially organized in macroscale networks. Variations in the strength, topography, and topology of correlated activity occur over time, and unfold upon a backbone of long-range anatomical connections. Subcortical neuromodulatory systems send widespread ascending projections to the cortex, and are thus ideally situated to shape the temporal and spatial structure of intrinsic correlations. These systems are also the targets of the pharmacological treatment of major neurological and psychiatric disorders, such as Parkinson’s disease, depression, and schizophrenia. Here, we review recent work that has investigated how neuromodulatory systems shape correlations of intrinsic fluctuations of large-scale cortical activity. We discuss studies in the human, monkey, and rodent brain, with a focus on non-invasive recordings of human brain activity. We provide a structured but selective overview of this work and distill a number of emerging principles. Future efforts to chart the effect of specific neuromodulators and, in particular, specific receptors, on intrinsic correlations may help identify shared or antagonistic principles between different neuromodulatory systems. Such principles can inform models of healthy brain function and may provide an important reference for understanding altered cortical dynamics that are evident in neurological and psychiatric disorders, potentially paving the way for mechanistically-inspired biomarkers and individualized treatments of these disorders.

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Scale-resolved analysis of brain functional connectivity networks with spectral entropy

10.1101/813162 ◽

2019 ◽

Author(s):

Carlo Nicolini ◽

Giulia Forcellini ◽

Ludovico Minati ◽

Angelo Bifone

Keyword(s):

Functional Connectivity ◽

Large Scale ◽

Brain Activity ◽

Ground Truth ◽

Structural Features ◽

Von Neumann Entropy ◽

Complete Graphs ◽

Theoretic Approach ◽

Physiological Noise ◽

Brain Functional Connectivity

Functional connectivity is derived from inter-regional correlations in spontaneous fluctuations of brain activity, and can be represented in terms of complete graphs with continuous (real-valued) edges. The structure of functional connectivity networks is strongly affected by signal processing procedures to remove the effects of motion, physiological noise and other sources of experimental error. However, in the absence of an established ground truth, it is difficult to determine the optimal procedure, and no consensus has been reached on the most effective approach to remove nuisance signals without unduly affecting the network intrinsic structural features. Here, we use a novel information-theoretic approach, based on von Neumann entropy, which provides a measure of information encoded in the networks at different scales. We also define a measure of distance between networks, based on information divergence, and optimal null models appropriate for the description of functional connectivity networks, to test for the presence of nontrivial structural patterns that are not the result of simple local constraints. This formalism enables a scale-resolved analysis of the distance between an empirical functional connectivity network and its maximally random counterpart, thus providing a means to assess the effects of noise and image processing on network structure.We apply this novel approach to address a few open questions in the analysis of brain functional connectivity networks. Specifically, we demonstrate a strongly beneficial effect of network sparsification by removal of the weakest links, and the existence of an optimal threshold that maximizes the ability to extract information on large-scale network structures. Additionally, we investigate the effects of different degrees of motion at different scales, and compare the most popular processing pipelines designed to mitigate its deleterious effect on functional connectivity networks.

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Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

10.1101/2020.06.01.128306 ◽

2020 ◽

Cited By ~ 1

Author(s):

Michalis Kassinopoulos ◽

Georgios D. Mitsis

Keyword(s):

Heart Rate ◽

Brain Activity ◽

Low Frequency ◽

Blood Oxygenation ◽

Breathing Patterns ◽

Physiological Noise ◽

Model Based ◽

Human Connectome Project ◽

Contrast Mechanism ◽

Noise Correction

AbstractThe blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with RETROICOR, which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS – defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

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Capturing the spatiotemporal dynamics of task-initiated thoughts with combined EEG and fMRI

10.1101/346346 ◽

2018 ◽

Cited By ~ 3

Author(s):

Lucie Bréchet ◽

Denis Brunet ◽

Gwénaël Birot ◽

Rolf Gruetter ◽

Christoph M. Michel ◽

...

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Mental Arithmetic ◽

Brain Activity ◽

Activity Patterns ◽

Mental States ◽

Spatiotemporal Dynamics ◽

Source Analysis ◽

Eeg Source Analysis ◽

Large Scale Networks

AbstractWhen at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 20s. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective activity changes in temporal, parietal and occipital areas (“posterior hot zone”), evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts of the default mode network, previously associated to “scene-reconstruction” and “self-experience” subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in both conditions. EEG source analysis revealed similar spatial distributions between the sources of these microstates and the regions identified with fMRI. These findings support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring, integrated brief periods of synchronized neuronal activity, lasting only fractions of seconds.SignificanceWe investigated the spatiotemporal dynamics of large-scale brain networks related to specific conscious thoughts. We demonstrate here that instructing participants to direct their thoughts to either episodic autobiographic memory or to mental arithmetic modulates distinct networks both in terms of highly spatially-specific BOLD signal oscillations as well as fast sub-second dynamics of EEG microstates. The combined findings from the two modalities evince a clear link between hemodynamic and electrophysiological signatures of spontaneous brain activity by the occurrence of thoughts that last for fractions of seconds, repeatedly appearing over time as integrated coherent activities of specific large-scale networks.

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The impact of real-time fMRI denoising on online evaluation of brain activity and functional connectivity

10.1101/2021.03.02.433573 ◽

2021 ◽

Author(s):

Masaya Misaki ◽

Jerzy Bodurka

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Noise Reduction ◽

Real Time ◽

Brain Activity ◽

Time Estimation ◽

Blood Oxygenation ◽

Physiological Noise ◽

Global Signal ◽

The Impact

AbstractObjectiveComprehensive denoising is imperative in fMRI analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity.ApproachWe performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT).Main resultsAll the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETORICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time.SignificanceThe results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.

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