Does functional connectivity explain?

Predicting depressive mood and its longitudinal changes using multimodal brain networks in young healthy adults.

This paper seeks to discover common change-point patterns, associated with functional connectivity (FC) in human brain, across multiple subjects. FC, represented as a covariance or a correlation matrix, relates to the similarity of fMRI responses across different brain regions, when a brain is simply resting or performing a task under an external stimulus. While the dynamical nature of FC is well accepted, this paper develops a formal statistical test for finding change-points in times series associated with FC observed over time. It represents instantaneous connectivity by a symmetric positive-definite matrix, and uses a Riemannian metric on this space to develop a graphical method for detecting change-points in a time series of such matrices. It also provides a graphical representation of estimated FC for stationary subintervals in between detected change-points. Furthermore, it uses a temporal alignment of the test statistic, viewed as a real-valued function over time, to remove temporal variability and to discover common change-point patterns across subjects, tasks, and regions. This method is illustrated using HCP database for multiple subjects and tasks.

Functional brain activity and connectivity have been studied by calculating intersubject and seed-based correlations of hemodynamic data acquired with functional magnetic resonance imaging (fMRI). To inspect temporal dynamics, these correlation measures have been calculated over sliding time windows with necessary restrictions on the length of the temporal window that compromises the temporal resolution. Here, we show that it is possible to increase temporal resolution by using instantaneous phase synchronization (PS) as a measure of dynamic (time-varying) functional connectivity. We applied PS on an fMRI dataset obtained while 12 healthy volunteers watched a feature film. Narrow frequency band (0.04-0.07 Hz) was used in the PS analysis to avoid artifactual results. We defined three metrics for computing time-varying functional connectivity and time-varying intersubject reliability based on estimation of instantaneous PS across the subjects: (1) seed-based PS, (2) intersubject PS, and (3) intersubject seed-based PS. Our findings show that these PS-based metrics yield results consistent with both seed-based correlation and intersubject correlation methods when inspected over the whole time series, but provide an important advantage of maximal single-TR temporal resolution. These metrics can be applied both in studies with complex naturalistic stimuli (e.g., watching a movie or listening to music in the MRI scanner) and more controlled (e.g., event-related or blocked design) paradigms. A MATLAB toolbox FUNPSY ( http://becs.aalto.fi/bml/software.html ) is openly available for using these metrics in fMRI data analysis.

Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude--the electric potential that is measured in all cases--might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literature.

Information processing in the brain spans from localised sensorimotor processes to higher-level cognition that integrates across multiple regions. Interactions between and within these subsystems enable multiscale information processing. Despite this multiscale characteristic, functional brain connectivity is often either estimated based on 10–30 distributed modes or parcellations with 100–1000 localised parcels, both missing across-scale functional interactions. We present Multiscale Probabilistic Functional Modes (mPFMs), a new mapping which comprises modes over various scales of granularity, thus enabling direct estimation of functional connectivity within- and across-scales. Crucially, mPFMs were not formulated, but emerged from data-driven multilevel Bayesian modelling of large functional MRI (fMRI) populations and every individual. We demonstrate that mPFMs capture both distributed brain modes and their co-existing subcomponents. In addition to validating mPFMs using simulations and real data, we show that mPFMs can predict ~900 personalised traits from UK Biobank more accurately than current standard techniques. Therefore, mPFMs can offer a new basis for functional connectivity modelling and yield enhanced fMRI biomarkers for traits and diseases.

Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain–behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone. These neural measurements were used to construct models to predict four key language-cognitive factors: (i) phonology; (ii) semantics; (iii) executive function; and (iv) fluency. Our results showed that each factor (except executive ability) could be significantly related to each neural measurement alone; however, structural and functional connectivity models did not explain additional variance above the lesion models. We did find evidence that the structural and functional predictors may be linked to the core lesion sites. First, the predictive functional connectivity features were found to be located within functional resting-state networks identified in healthy controls, suggesting that the result might reflect functionally specific reorganization (damage to a node within a network can result in disruption to the entire network). Second, predictive structural connectivity features were located within core lesion sites, suggesting that multimodal information may be redundant in prediction modelling. In addition, we observed that the optimum sparsity within the regularized regression models differed for each behavioural component and across different imaging features, suggesting that future studies should consider optimizing hyperparameters related to sparsity per target. Together, the results indicate that the observed network-level disruption was predicted by the lesion alone and does not significantly improve model performance in predicting the profile of language impairment.

MEG Based Functional Connectivity: Application of ICA to Alleviate Signal Leakage

Previous work using transcranial magnetic stimulation (TMS) demonstrated that the right pre-supplementary motor area (preSMA), a node in the fronto-basal-ganglia network, is critical for response inhibition. However, TMS influences interconnected regions, raising the possibility of a link between the preSMA activity and the functional connectivity within the network. To understand this relationship, we applied single-pulse TMS to the right preSMA during functional magnetic resonance imaging when the subjects were at rest to examine changes in neural activity and functional connectivity within the network in relation to the efficiency of response inhibition evaluated with a stop-signal task. The results showed that preSMA-TMS increased activation in the right inferior-frontal cortex (rIFC) and basal ganglia and modulated their task-free functional connectivity. Both the TMS-induced changes in the basal-ganglia activation and the functional connectivity between rIFC and left striatum, and of the overall network correlated with the efficiency of response inhibition and with the white-matter microstructure along the preSMA – rIFC pathway. These results suggest that the task-free functional and structural connectivity between the rIFCop and basal ganglia are critical to the efficiency of response inhibition.

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering.

BackgroundTraditional functional connectivity is calculated as the correlation between resting‐state fMRI time series from pairs of brain regions or networks. Our innovative method extracts time series information from cross‐correlations curves between brain regions, thus deriving the relative duration of functional connections. These temporal analysis methods provide a means for examining how the timing of brain connections is affected across the dementia spectrum. This study extends previous research efforts into understanding the neural correlates of dementia by applying innovative resting‐state functional connectivity analysis methods to a sample of cognitively intact older adults (HC), individuals with mild cognitive impairment (MCI), and Alzheimer’s disease (AD).Method109 participants were included in this analysis (HC: n=47, 29 female, age=72.13±4.68; MCI: n=32, 18 female, age=73.84±5.14; AD: n=30, 19 female, age=77.37±6.45). All study participants were administered the Mini Mental Status Examination (MMSE), and completed MP2RAGE structural imaging and two 15‐minute resting fMRI scans. Cross‐correlation curves were constructed utilizing the Pearson correlation coefficient between pairs of times series for each of the 17 brain networks in the Yeo et al. parcellation. Correlation values were recorded at each of ±20 lags by shifting one of the time points by one volume. Sustained connectivity is derived from the full width at half maximum of the curve (Figure 1A) whereas lag‐based connectivity utilizes the correlation value at each lag. T statistics and p‐values are reported for across‐group connectivity values. Partial correlations were computed between connectivity measures and MMSE scores. All results include controlling for the effects of mean head motion, age, and sex.ResultSustained connectivity demonstrated distributed increases across groups (Figure 1B; HC<MCI<AD; pFDR<.05). At zero‐lag, we see decreased connectivity in MCI and AD compared to HC; however, as lag‐based connectivity is increased, we see increases in connectivity corresponding to diagnostic groups (Figure 2; HC<MCI<AD). Across study participants, both sustained and lag‐based connectivity were negatively correlated with MMSE scores (Figure 3; pFDR <.05).ConclusionTaken together, these findings suggest that functional connections between brain networks persist longer across disease states and that this longer duration of connectivity may reflect increased cognitive demand related to underlying cognitive processes.

There are indicates that raphe nuclei may be involved in the occurrence of chronic pain in Parkinson's disease (PD). In the study, we investigated the functional connectivity pattern of raphe nuclei in Parkinson's disease with chronic pain (PDP) to uncover its possible pathophysiology. Fifteen PDP, who suffered from pain, lasted longer than 3months, sixteen Parkinson's disease patients with no pain (nPDP) and eighteen matched normal health controls (NCs) were recruited. All subjects completed the King's Parkinson's Pain Scale (KPPS) besides Parkinson-related scale and demographics. We performed a seed-based resting-state analysis of functional magnetic resonance imaging to explore whole-brain functional connectivity of the raphe nuclei. Multiple regression model was used to explore the related factors of pain including disease duration, disease severity, Hamilton Depression Rating Scale, age, sex, levodopa equivalent dose and the strength of network functional connectivity. Compared with the nPDP, the PDP group showed stronger functional connectivity between raphe nuclei and pain-related brain regions, including parietal lobe, insular lobe, cingulum cortex and prefrontal cortex, and the functional connectivity values of those areas were significantly positively correlated with KPPS independent of the clinical variables. Compared with NCs, the combined PD groups showed decreased functional connectivity including prefrontal cortex and cingulum cortex. Abnormal functional connectivity model of raphe nuclei may be partly involved in pathophysiological mechanism of pain in PD.

Is schizophrenia a spatiotemporal disorder of the brain's resting state?

Naltrexone, an opioid receptor antagonist that is Food and Drug Administration approved for treating alcohol use disorder (AUD), reduces alcohol craving and intake. Despite known pharmacological properties, little is known regarding the effects of naltrexone on neural circuit function. Thus, a data-driven examination of the neural effects of naltrexone in human subjects may offer novel insight into its treatment mechanisms. Twenty-one alcohol using males (22 to 39) participated in a double-blind, placebo-controlled crossover study of the effects of naltrexone on brain voxel-wise functional connectivity (FC) using intersubject FC correlation mapping. We first cross-correlated the time series from each gray matter voxel to produce a 6,356×6,356 FC matrix for each subject and session. We then subtracted the placebo FC matrix from the naltrexone FC matrix. To identify brain regions demonstrating significant reconfiguration of whole-brain FC patterns following naltrexone treatment, we statistically quantified the consistency of patterns of voxel FC changes across subjects. Permutation testing identified significant clusters of voxels undergoing significant reconfiguration. Using the identified clusters in a seed-based FC analysis, we then compared the FC patterns of affected brain areas on placebo versus naltrexone in a paired t-test. Ridge regression analyses identified self-report measures, including substance use, that significantly predicted individual differences in FC among naltrexone-modulated regions. Two clusters in the rostral anterior cingulate cortex (rACC)/ventromedial prefrontal cortex (vmPFC) demonstrated significant modulation of FC by naltrexone. Using these 2 proximal clusters as a single seed, specific FC changes were identified in regions associated with a left frontoparietal network (increasing), as well as visual and motor regions (decreasing). Stronger FC between the rACC/vmPFC and this set of regions on placebo was associated with more external locus of control, whereas weaker connectivity was associated with greater substance use problems. Naltrexone strengthened these connections most among individuals who reported greater drinking to cope. Enhancing connectivity between the rACC/vmPFC, implicated in alcohol craving, and components of a left frontoparietal network involved in executive control may represent an effective strategy for the treatment of AUD.

Objective. The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain’s networks and its alterations in diseased brains. However, little is known about the underlying dynamic mechanism with which such a dynamic FC is flexibly organized under the constraint of structural connections. In this work, we explore the relationship between critical dynamics and FC flexibility based on both functional magnetic resonance imaging data and computer models. Approach. First, we proposed the connectivity number entropy (CNE), which was an entropy measure for the flexibility of FC. Through an analysis of resting-state fMRI (rs-fMRI) data from 95 healthy participants, we explored the correlation between CNE and long-range temporal correlations (LRTCs), which can represent the critical dynamics. Then, we employed a whole-brain computer model based on diffusion tensor imaging (DTI) to further demonstrate this relationship. Main results. We found that the most flexible FC is present when the brain is operating close to the critical point of a phase transition. Additionally, around this point, our model can yield the best prediction for the regional distribution of CNE because structural information is reflected the most by the CNE through critical dynamics. Significance. Our results not only reveal the underlying dynamic mechanism for the organization of time-dependent FC but also provide a possible pathway to model the flexible functional organization in the human brain and may have potential application in the analysis of altered dynamic FC in diseased brains.

Functional and effective connectivity refer to the observed connectivity between distinct anatomical brain regions. This neural synchrony found to unveil the short-range as well as the long-range distance coordinated communication between neural populations during cognitive and perceptual tasks. In this work, we present a new Python neuroimaging module, called “dyfunconn” specifically tailored for functional connectivity, synchrony estimators and graph analysis in the context of Functional Connectivity Microstates (FCμstates) analyzing Time-Varying Functional Connectivity Graphs (TVFCGs). The primary focus in the FCμstates paradigm is twofold; to represent the input time-varying connectivity into a small number-repertoire (data reduction) of features (feature extraction) while preserving the temporal dynamics of the connectivity patterns. The broader goal is to provide a module, with a high level and consistent API that implements various and well-defined synchrony estimators (about fifteen of the time of this poster). These estimators are implemented in such fashion that can be utilized through the concept of Time-Varying Functional Connectivity Graphs; thus, our module, grants an additional level of graph analysis. The other important aspect of our module is data mining. We have implemented various advanced clustering algorithms/frameworks as proposed in the literature, such as Neural Gas. In the same spirit, we provide two important submodules for extracting and analyzing symbolic time series and employing statistical methods for significance evaluation, i.e. Surrogate Analysis with False Discovery Rate. dyfunconn is continuously developed and is publicly available (https://github.com/makism/dyfunconn); built on top of other mature and well known open-source scientific libraries, namely numpy, scipy and networkx. Following the example set by these modules, all methods in the module contain high-quality scientific documentation with references and citations to the original papers. Finally, extensive unit tests and examples cover all the methods.