scholarly journals Organization of Propagated Intrinsic Brain Activity in Individual Humans

2019 ◽  
Vol 30 (3) ◽  
pp. 1716-1734 ◽  
Author(s):  
Ryan V Raut ◽  
Anish Mitra ◽  
Scott Marek ◽  
Mario Ortega ◽  
Abraham Z Snyder ◽  
...  

Abstract Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.

2019 ◽  
Author(s):  
Matthew F. Singh ◽  
Todd S. Braver ◽  
Michael W. Cole ◽  
ShiNung Ching

AbstractA key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.


2021 ◽  
Author(s):  
Gregory Scott ◽  
Robert Leech

A widespread assumption of fMRI-derived large-scale intrinsic connectivity networks (ICNs) is that they are spatially static over time. However, the assumption of spatial stationarity of ICNs has been challenged by a range of techniques that allow for time-varying connectivity between brain regions and demonstration that canonical networks like the default model network (DMN) can be fractionated according to time-varying connectivity relationships of their subcomponents. Previously, we developed a simple spatiotemporal ICA (stICA) technique to allow the discovery of patterns of spatiotemporal evolution in task fMRI data in a way that avoided the traditional constraint of spatial stationarity on brain networks, and we validated the approach in fMRI of task-to-rest transitions. Here, we apply our stICA technique to resting-state fMRI datasets to explore whether spatiotemporally evolving components of brain activity can be identified in the absence of an overt behavioural task. We found that stICA components could generally be described in terms of graded onsets and offsets of ICNs that had been calculated based on techniques that assumed spatial stationarity. Our results suggest that, to a reasonable approximation, stable ICNs can be taken to be building blocks of the spatiotemporal patterns measured with resting-state fMRI.


2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.


2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.


2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chen Chen ◽  
Jian Zhang ◽  
Xiao-Wei Li ◽  
Wenqing Xia ◽  
Xu Feng ◽  
...  

Objective. Subjective tinnitus is hypothesized to arise from aberrant neural activity; however, its neural bases are poorly understood. To identify aberrant neural networks involved in chronic tinnitus, we compared the resting-state functional magnetic resonance imaging (fMRI) patterns of tinnitus patients and healthy controls.Materials and Methods. Resting-state fMRI measurements were obtained from a group of chronic tinnitus patients (n=29) with normal hearing and well-matched healthy controls (n=30). Regional homogeneity (ReHo) analysis and functional connectivity analysis were used to identify abnormal brain activity; these abnormalities were compared to tinnitus distress.Results. Relative to healthy controls, tinnitus patients had significant greater ReHo values in several brain regions including the bilateral anterior insula (AI), left inferior frontal gyrus, and right supramarginal gyrus. Furthermore, the left AI showed enhanced functional connectivity with the left middle frontal gyrus (MFG), while the right AI had enhanced functional connectivity with the right MFG; these measures were positively correlated with Tinnitus Handicap Questionnaires (r=0.459,P=0.012andr=0.479,P=0.009, resp.).Conclusions. Chronic tinnitus patients showed abnormal intra- and interregional synchronization in several resting-state cerebral networks; these abnormalities were correlated with clinical tinnitus distress. These results suggest that tinnitus distress is exacerbated by attention networks that focus on internally generated phantom sounds.


2021 ◽  
Author(s):  
Georgia Mary Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.


2021 ◽  
Author(s):  
Taylor S Bolt ◽  
Jason Nomi ◽  
Danilo Bzdok ◽  
Catie Chang ◽  
B.T. Thomas Yeo ◽  
...  

The characterization of intrinsic functional brain organization has been approached from a multitude of analytic techniques and methods. We are still at a loss of a unifying conceptual framework for capturing common insights across this patchwork of empirical findings. By analyzing resting-state fMRI data from the Human Connectome Project using a large number of popular analytic techniques, we find that all results can be seamlessly reconciled by three fundamental low-frequency spatiotemporal patterns that we have identified via a novel time-varying complex pattern analysis. Overall, these three spatiotemporal patterns account for a wide variety of previously observed phenomena in the resting-state fMRI literature including the task-positive/task-negative anticorrelation, the global signal, the primary functional connectivity gradient and the network community structure of the functional connectome. The shared spatial and temporal properties of these three canonical patterns suggest that they arise from a single hemodynamic mechanism.


Author(s):  
Benedikt Sundermann ◽  
Mona Olde lütke Beverborg ◽  
Bettina Pfleiderer

Information derived from functional magnetic resonance imaging (fMRI) during wakeful rest has been introduced as a candidate diagnostic biomarker in unipolar major depressive disorder (MDD). Multiple reports of resting state fMRI in MDD describe group effects. Such prior knowledge can be adopted to pre-select potentially discriminating features, for example for diagnostic classification models with the aim to improve diagnostic accuracy. Purpose of this analysis was to consolidate spatial information about alterations of spontaneous brain activity in MDD to serve such feature selection and as a secondary aim to improve understanding of disease mechanisms. 32 studies were included in final analyses. Coordinates extracted from the original reports were assigned to two categories based on directionality of findings. Meta-analyses were calculated using the non-additive activation likelihood estimation approach with coordinates organized by subject group to account for non-independent samples. Results were compared with established resting state networks (RSNs) and spatial representations of recently introduced temporally independent functional modes (TFMs) of spontaneous brain activity. Converging evidence revealed a distributed pattern of brain regions with increased or decreased spontaneous activity in MDD. The most distinct finding was hyperactivity/ hyperconnectivity presumably reflecting the interaction of cortical midline structures (posterior default mode network components associated with self-referential processing and the subgenual anterior cingulate cortex) with lateral frontal areas related to externally-directed cognition. One particular TFM seems to better comprehend the findings than classical RSNs. Alterations that can be captured by resting state fMRI show considerable overlap with those identifiable with other neuroimaging modalities though differing in some aspects.


2017 ◽  
Author(s):  
Giri P. Krishnan ◽  
Oscar C. González ◽  
Maxim Bazhenov

AbstractResting or baseline state low frequency (0.01-0.2 Hz) brain activity has been observed in fMRI, EEG and LFP recordings. These fluctuations were found to be correlated across brain regions, and are thought to reflect neuronal activity fluctuations between functionally connected areas of the brain. However, the origin of these infra-slow fluctuations remains unknown. Here, using a detailed computational model of the brain network, we show that spontaneous infra-slow (< 0.05 Hz) fluctuations could originate due to the ion concentration dynamics. The computational model implemented dynamics for intra and extracellular K+ and Na+ and intracellular Cl- ions, Na+/K+ exchange pump, and KCC2 co-transporter. In the network model representing resting awake-like brain state, we observed slow fluctuations in the extracellular K+ concentration, Na+/K+ pump activation, firing rate of neurons and local field potentials. Holding K+ concentration constant prevented generation of these fluctuations. The amplitude and peak frequency of this activity were modulated by Na+/K+ pump, AMPA/GABA synaptic currents and glial properties. Further, in a large-scale network with long-range connections based on CoCoMac connectivity data, the infra-slow fluctuations became synchronized among remote clusters similar to the resting-state networks observed in vivo. Overall, our study proposes that ion concentration dynamics mediated by neuronal and glial activity may contribute to the generation of very slow spontaneous fluctuations of brain activity that are observed as the resting-state fluctuations in fMRI and EEG recordings.


2020 ◽  
Author(s):  
Nan Xu ◽  
Peter C. Doerschuk ◽  
Shella D. Keilholz ◽  
R. Nathan Spreng

AbstractThe macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting-state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N=200) we applied prediction correlation techniques to four resting state fMRI runs (total TRs = 4800). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region’s role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.


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