Sliding Window Correlation Analysis Research Articles

Brain Temporal-Spectral Functional Variability Reveals Neural Improvements of DBS Treatment for Disorders of Consciousness.

Deep brain stimulation (DBS) is establishing itself as a promising treatment for disorders of consciousness (DOC). Measuring consciousness changes is crucial in the optimization of DBS therapy for DOC patients. However, conventional measures use subjective metrics that limit the investigations of treatment-induced neural improvements. The focus of this study is to analyze the regulatory effects of DBS and explain the regulatory mechanism at the brain functional level for DOC patients. Specifically, this paper proposed a dynamic brain temporal-spectral analysis method to quantify DBS-induced brain functional variations in DOC patients. Functional near-infrared spectroscopy (fNIRS) that promised to evaluate consciousness levels was used to monitor brain variations of DOC patients. Specifically, a fNIRS-based experimental procedure with auditory stimuli was developed, and the brain activities during the procedure from thirteen DOC patients before and after the DBS treatment were recorded. Then, dynamic brain functional networks were formulated with a sliding-window correlation analysis of phase lag index. Afterwards, with respect to the temporal variations of global and regional networks, the variability of global efficiency, local efficiency, and clustering coefficient were extracted. Further, dynamic networks were converted into spectral representations by graph Fourier transform, and graph energy and diversity were formulated to assess the spectral global and regional variability. The results showed that DOC patients under DBS treatment exhibited increased global and regional functional variability that was significantly associated with consciousness improvements. Moreover, the functional variability in the right brain regions had a stronger correlation with consciousness enhancements than that in the left brain regions. Therefore, the proposed method well signifies DBS-induced brain functional variations in DOC patients, and the functional variability may serve as promising biomarkers for consciousness evaluations in DOC patients.

Time-Varying Functional Connectivity of the Hippocampus Is Associated With Cognitive Performance in Multiple Sclerosis Patients (P10-3.014)

<h3>Objective:</h3> To explore hippocampal static (sFC) and time-varying functional connectivity (TVFC) and assess their association with cognition in multiple sclerosis (MS). <h3>Background:</h3> The hippocampus has a key role in cognition. Hippocampal TVFC in MS needs to be further explored. <h3>Design/Methods:</h3> 3T structural and resting-state (RS) functional MRI scans were acquired from 108 MS patients and 63 healthy controls (HC). Subjects underwent neuropsychological evaluation. Sliding-window correlation analysis using the left (L) and right (R) hippocampus as seed regions assessed TVFC, quantified by the standard deviation of connectivity across windows. Mean connectivity indicated sFC. <h3>Results:</h3> MS patients had decreased sFC <i>vs</i> HC between L hippocampus and temporo-parietal regions, and increased sFC between L and R hippocampus and thalamus, precuneus and superior frontal regions. TVFC was decreased in MS patients between L hippocampus and temporo-parietal regions. Conversely, TVFC was increased in MS patients between L and R hippocampus and L pre- and postcentral gyri, cuneus, orbitofrontal cortex and inferior temporal gyrus (ITG). In MS patients, better global cognition correlated with higher TVFC between L hippocampus and L pre- and postcentral gyri (r=0.21–0.28; p=0.04–0.006). Better verbal memory correlated with higher TVFC between L hippocampus and L precentral gyrus (r=0.21, p=0.03), and better visuospatial memory correlated with higher TVFC between L and R hippocampus and L cuneus, pre- and postcentral gyri and ITG (r=0.19–0.23, p=0.02–0.04). Better information processing speed correlated with higher TVFC between L hippocampus and L postcentral gyrus (r=0.21, p=0.03) and with higher sFC between R hippocampus and L superior frontal cortex (SFC) (r=0.21, p=0.03). Finally, better attention scores correlated with higher TVFC between L hippocampus and L temporal cortex (r=0.24, p=0.01) and with higher sFC between R hippocampus and L SFC (r=0.20, p=0.05). <h3>Conclusions:</h3> Increased hippocampal connectivity contributed to explain better cognitive performances in MS, with a peculiar association between higher hippocampal TVFC and better memory scores. <b>Disclosure:</b> Monica Margoni has received research support from MAGNIMS. Paola Valsasina has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for ACCMED. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bristol Myers Squibb . Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Sanofi Genzyme. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Novartis. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Roche. Mr. Preziosa has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Merck. Olga Marchesi has nothing to disclose. Carmen Vizzino has nothing to disclose. Damiano Mistri has nothing to disclose. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). Maria Assunta Rocca has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bayer, Biogen, Bristol Myers Squibb, Celgene, Genzyme, Merck Serono, Novartis, Roche, and Teva. The institution of Maria Assunta Rocca has received research support from Italian Ministry of Health, MS Society of Canada and Fondazione Italiana Sclerosi Multipla.

An fNIRS-Based Dynamic Functional Connectivity Analysis Method to Signify Functional Neurodegeneration of Parkinson's Disease.

Parkinson's disease (PD) is a prevalent brain disorder, and PD diagnosis is crucial for treatment. Existing methods for PD diagnosis are mainly focused on behavior analysis, while the functional neurodegeneration of PD has not been well investigated. This paper proposes a method to signify functional neurodegeneration of PD with dynamic functional connectivity analysis. A functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation from 50 PD patients and 41 age-matched healthy controls in clinical walking tests. Dynamic functional connectivity was constructed with sliding-window correlation analysis, and k-means clustering was applied to generate the key brain connectivity states. Dynamic state features including state occurrence probability, state transition percentage and state statistical features were extracted to quantify the variations of brain functional networks. A support vector machine was trained to classify PD patients and healthy controls. Statistical analysis was conducted to investigate the difference between PD patients and healthy controls as well as the relationship between dynamic state features and the MDS-UPDRS sub-score of gait. The results showed that PD patients had a higher probability of transiting to brain connectivity states with high levels of information transmission compared with healthy controls. The MDS-UPDRS sub-score of gait and the dynamics state features showed a significant correlation. Moreover, the proposed method had better classification performances than the available fNIRS-based methods in terms of accuracy and F1 score. Thus, the proposed method well signified functional neurodegeneration of PD, and the dynamic state features may serve as promising functional biomarkers for PD diagnosis.

Anhedonia and dysregulation of an angular gyrus-centred and dynamic functional network in adolescent-onset depression

BackgroundAnhedonia is an important aspect of adolescent-onset major depressive disorder (MDD) and is associated with increased risk of suicidal behaviors and poor treatment outcomes. However, the neural circuitry underlying this deficit has not been well defined. This study aims to identify the relationships between anhedonia and changes in static and dynamic functional connectivity (FC) in adolescent-onset MDD patients compared with healthy control subjects (HCs) and adult-onset MDD patients. MethodsA total of 157 participants completed the Snaith-Hamilton Pleasure Scale (SHAPS) to assess hedonic capacity. Resting-state functional imaging scans were analysed using graph theoretical analysis, network-based statistics (NBS) and sliding window correlation analysis to explore the potential patterns of neural network brain disruptions in adolescent-onset MDD. Pearson correlations and support vector machines regression (SVR) were used to explore correlations and predict network measures with SHAPS scores. ResultsCompared with those with adult-onset MDD, adolescent-onset MDD patients showed decreased FC in 7 nodes and 6 connections, with the right angular gyrus (AG), left AG and left paracentral lobule having the largest number of connected edges (P = 0.0396, NBS-corrected). Their average FC and SHAPS scores were positively correlated (r = 0.309, P = 0.035). Regarding dynamic FC, compared with HCs, adolescent-onset MDD patients showed a tendency towards a decreased frequency in moderate-intensity brain FC states (P = 0.014), which was significantly and positively correlated with SHAPS scores (r = 0.425, P = 0.003). SVR also revealed AG-centred FC and dynamic FC could predict SHAPS scores (MSE = 27.233, P = 0.001). ConclusionsThese findings provide distinct evidence on the physiological mechanisms of adolescent-onset MDD and anhedonia.

Time-varying connectivity of the precuneus and its association with cognition and depressive symptoms in neuromyelitis optica: A pilot MRI study

Background: The precuneus is involved in cognition and depression; static functional connectivity (SFC) abnormalities of this region have been observed in neuromyelitis optica spectrum disorders (NMOSD). Time-varying functional connectivity (TVC) underpins dynamic variations of brain connectivity. Objective: The aim of this study was to explore precuneus SFC and TVC in NMOSD patients and their associations with neuropsychological features. Methods: This retrospective study includes 27 NMOSD patients and 30 matched healthy controls undergoing resting state functional magnetic resonance imaging (MRI) and a neuropsychological evaluation of cognitive performance and depressive symptoms. A sliding-window correlation analysis using bilateral precuneus as seed region assessed TVC, which was quantified by the standard deviation of connectivity across windows. Mean connectivity indicated SFC. Results: Compared to controls, patients had reduced SFC between precuneus, temporal lobe, putamen and cerebellum, and reduced TVC between precuneus and prefronto-parietal-temporo-occipital cortices and caudate. Patients also had increased intra-precuneal TVC and increased TVC between the precuneus and the temporal cortex. More severe depressive symptoms correlated with increased TVC between the precuneus and the temporal lobe; worse cognitive performance mainly correlated with higher TVC between the precuneus and the parietal lobe. Conclusion: TVC rather than SFC of the precuneus correlates with NMOSD neuropsychological features; different TVC abnormalities underlie depressive symptoms and cognitive impairment.

Differential patterns of dynamic functional connectivity variability in major depressive disorder treated with cognitive behavioral therapy

BackgroundNumerous studies have shown that major depressive disorder (MDD) is characterized by a range of impairments in emotional and cognitive functions that are closely related to abnormalities in brain structure and function. Cognitive behavioral therapy (CBT) can be used as treatment for mild to moderate MDD, which can assist with ameliorating the symptoms. Previous studies have assumed that the internal fluctuations throughout the entire scan are static. However, it has recently been suggested that the brain connectivity is dynamic and relative to continuous rhythmic activity. The effect of dynamic changes in CBT on MDD patients is unknown. MethodsNineteen first-episode, unmedicated MDD patients and twenty-two healthy controls (HC) participated in the study. The patients received early CBT treatment once a week for 6 weeks. Symptom examinations and magnetic resonance imaging (MRI) scans were performed pre and post treatment. Degree centrality (DC) was used to investigate the whole-brain connectivity differences between patients with MDD and healthy controls, and sliding window correlation analysis was applied to investigate the dynamic changes of functional connectivity among MDD patients treated with CBT. The variance of dynamic functional connectivity (dFC) was calculated to evaluate the temporal variability along the time. ResultsPatients with MDD showed abnormal DC in dorsolateral prefrontal cortex (dlPFC), insula and postcentral gyrus. Correlation analysis revealed that degree centrality of dlPFC was negatively correlated with the course of disease in patients with MDD. Results of dynamic functional connectivity showed that, compared to HC, MDD patients-remained excessively stable in dlPFC and precuneus connectivity, which is associated with emotional cognitive symptoms. After CBT, patients showed increased dFC variability in dlPFC and precuneus (p < 0.01, GRF corrected). ConclusionDLPFC plays an important role in pathophysiological mechanism of MDD. CBT helped patients suppress redundant thoughts and negative self-focus. As a connecting node, dlPFC participates in the mechanism of action of CBT.

Whole-brain electrophysiological functional connectivity dynamics in resting-state EEG

Objective. Functional connectivity (FC) dynamics have been studied in functional magnetic resonance imaging (fMRI) data, while it is largely unknown in electrophysiological data, e.g. EEG. Approach. The present study proposed a novel analytic framework to study spatiotemporal dynamics of FC (dFC) in resting-state human EEG data, including independent component analysis, cortical source imaging, sliding-window correlation analysis, and k-means clustering. Main results. Our results confirm that major fMRI intrinsic connectivity networks (ICNs) can be successfully reconstructed from EEG using our analytic framework. Prominent spatial and temporal variability were revealed in these ICNs. The mean dFC spatial patterns of individual ICNs resemble their corresponding static FC (sFC) patterns but show fewer cross-talks among distinct ICNs. Our investigation unveils evidences of time-domain variations in individual ICNs comparable to their mean FC level in terms of magnitude. The major contributors to these variations are from the frequency below 0.0156 Hz, in the similar range of FC dynamics from fMRI data. Among different ICNs, larger temporal variabilities are observed in the frontal attention and auditory/visual ICNs, while sensorimotor, salience, and default model networks showed less. Our analytic framework for the first time revealed quasi-stable states within individual EEG ICNs, with various strengths or spatial patterns that were reliably detected at both group and individual levels. These states all together reveal a more complete picture of EEG ICNs: (1) quasi-stable state spatial patterns as a whole for each EEG ICN are more consistent with the corresponding fMRI ICN in terms of the bilateral distribution and multi-nodes structure; (2) EEG ICNs reveal more transient patterns about within-ICN between-node communications than fMRI ICNs. Significance. The present findings highlight the fact that rich temporal and spatial dynamics exist in ICN that can be detected from EEG data. Future studies might extend investigations towards spectral dynamics of EEG ICNs.

Trends in precipitation days in the United States

AbstractChanges in precipitation frequency can have a major impact on many different sectors including agriculture, tourism, and recreation. This study investigates trends in precipitation days at first‐order weather stations across the conterminous United States from 1951 to 2015. The Mann‐Kendall test and sliding window correlation analysis are used to examine trends over time. Future precipitation days are forecasted via usage of a stepwise auto‐regressive model. The Mann‐Kendall test found that the majority of the Northeast and Midwestern states show upward trends in precipitation days, while negative trends are located in the Southeast and in clusters throughout the Northwest. Sliding window correlation analysis was used to detect the decade when most of the change in precipitation days occurred. The northeastern United States had more significant changes during the earlier decades whereas the centre part of the country had more significant changes in the later decades. Most stations saw more decades with positive (increasing) trends in precipitation days. Precipitation days are expected to increase for most of the United States into the future.

Multidecadal dynamics of the Arctic copepod Limnocalanus macrurus in relation to environmental variability in the Baltic Sea

Abstract The Arctic Limnocalanus macrurus is a prominent representative of large copepods which performs several essential functions in freshwater and marine ecosystems. Being a cold stenotherm species, its distribution is primarily confined to deeper water layers. Based on the long-term observations from one of the largest spatially confined natural populations of this species in the Baltic Sea, we detected profound long-term variability of L. macrurus during 1958–2016: high abundances before the 1980s, then nearly disappearance in the 1990s and recovery in the 2000s. The main environmental parameters explaining the interannual variability of L. macrurus in spring were herring spawning stock biomass in preceding year, winter severity, and bottom water temperature in preceding summer. The effect of winter severity and water temperature was also non-linear. The sliding window correlation analysis pointed to a non-stationary relationship between the abundance of L. macrurus and the key variables. Given the observed pronounced seasonality in the population structure of L. macrurus (young stages dominated in the beginning of the year and only adults were left in the population in summer and autumn) we identified the dynamics of key environmental variables to understand this species under different ecosystem configurations and different combinations of drivers of change.

Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis

Background: In multiple sclerosis (MS), abnormalities of brain network dynamics and their relevance for cognitive impairment have never been investigated. Objectives: The aim of this study was to assess the dynamic resting state (RS) functional connectivity (FC) on 62 relapsing-remitting MS patients and 65 sex-matched healthy controls enrolled at 7 European sites. Methods: MS patients underwent clinical and cognitive evaluation. Between-group network FC differences were evaluated using a dynamic approach (based on sliding-window correlation analysis) and grouping correlation matrices into recurrent FC states. Results: Dynamic FC analysis revealed, in healthy controls and MS patients, three recurrent FC states: two characterized by strong intra- and inter-network connectivity and one characterized by weak inter-network connectivity (State 3). A total of 23 MS patients were cognitively impaired (CI). Compared to cognitively preserved (CP), CI-MS patients had reduced RS-FC between subcortical and default-mode networks in the low-connectivity State 3 and lower dwell time (i.e. time spent in a given state) in the high-connectivity State 2. CI-MS patients also exhibited a lower number and a less frequent switching between meta-states, as well as a smaller distance traveled through connectivity states. Conclusion: Time-varying RS-FC was markedly less dynamic in CI- versus CP-MS patients, suggesting that slow inter-network connectivity contributes to cognitive dysfunction in MS.

Sliding window correlation analysis: Modulating window shape for dynamic brain connectivity in resting state

The sliding window correlation (SWC) analysis is a straightforward and common approach for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses have been long used, there are still considerable technical issues associated with the approach. A great effort has recently been dedicated to investigate the window setting effects on dynamic connectivity estimation. In this direction, tapered windows have been proposed to alleviate the effect of sudden changes associated with the edges of rectangular windows. Nevertheless, the majority of the windows exploited to estimate brain connectivity tend to suppress dynamic correlations, especially those with faster variations over time. Here, we introduced a window named modulated rectangular (mRect) to address the suppressing effect associated with the conventional windows. We provided a frequency domain analysis using simulated time series to investigate how sliding window analysis (using the regular window functions, e.g. rectangular and tapered windows) may lead to unwanted spectral modulations, and then we showed how this issue can be alleviated through the mRect window. Moreover, we created simulated dynamic network data with altering states over time using simulated fMRI time series, to examine the performance of different windows in tracking network states. We quantified the state identification rate of different window functions through the Jaccard index, and observed superior performance of the mRect window compared to the conventional window functions. Overall, the proposed window function provides an approach that improves SWC estimations, and thus the subsequent inferences and interpretations based on the connectivity network analyses.

Differences in Large-scale and Sliding-window-based Functional Networks of Reappraisal and Suppression

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

Modulating the resting-state functional connectivity patterns of language processing areas in the human brain with anodal transcranial direct current stimulation applied over the Broca's area.

.Cortical circuit reorganization induced by anodal transcranial direct current stimulation (tDCS) over the Broca’s area of the dominant language hemisphere in 13 healthy adults was quantified by functional near-infrared spectroscopy (fNIRS). Transient cortical reorganization patterns in steady-state functional connectivity (seed-based and graph theory analysis) and temporal functional connectivity (sliding window correlation analysis) were recorded before, during, and after applying high current tDCS (1 mA, 8 min). fNIRS connectivity mapping showed that tDCS induced significantly () increased functional connectivity between Broca’s area and its neighboring cortical regions while it simultaneously decreased the connectivity to remote cortical regions. Furthermore, the anodal stimulation caused significant increases to the functional connectivity variability (FCV) of remote cortical regions related to language processing. In addition to the high current tDCS, low current tDCS (0.5 mA, 2 min 40 s) was also applied to test whether the transient effects of lower stimulation current could qualitatively predict cortical connectivity alterations induced by the higher currents. Interestingly, low current tDCS could qualitatively predict the increase in clustering coefficient and FCV but not the enhancement of local connectivity. Our findings indicate the possibility of combining future studies fNIRS with tDCS at lower currents to help guide therapeutic interventions.

Multifractal dynamics of resting-state functional connectivity in the prefrontal cortex

Brain function is organized as a network of functional connections between different neuronal populations with connection strengths dynamically changing in time and space. Studies investigating functional connectivity (FC) usually follow a static approach when describing FC by considering the connectivity strengths constant, however a dynamic approach seems more reasonable, as this way the spatio-temporal dynamics of the underlying system can also be captured. Objective: The scale-free, i.e. fractal nature of neural dynamics is an inherent property of the nervous system. The aim of this study was to determine if dynamic functional connectivity (DFC) in the prefrontal cortex shows not only scale-free but indeed multifractal dynamics. Approach: Functional near-infrared spectroscopy (fNIRS) was used to monitor resting-state brain activity in young healthy volunteers. Sliding window correlation (SWC) analysis and graph theory approach were utilized to capture the functional connection networks for every time point, whose topology was subsequently characterized with three network metrics—Density, Clustering Coefficient and Efficiency—each capturing a different aspect of the given network. The temporal structuring of the obtained network metric time series was then described by multifractal time series analysis. Main results: We found the DFC in the prefrontal cortex fluctuating according to scale-free, specifically multifractal dynamics. Moreover, different topological properties of the network showed different multifractal characteristics. All the results were reproducible in all window sizes used in the SWC analysis, however we found that the actual values of the given multifractal properties depended significantly on the window size. Significance: Our results may well be another indication of a self-organized critical state underlying resting-state brain activity. The proposed analysis of functional brain dynamics can also open new perspectives for future clinical applications.

Realistic models of apparent dynamic changes in resting-state connectivity in somatosensory cortex.

Variations over time in resting-state correlations in blood oxygenation level-dependent (BOLD) signals from different cortical areas may indicate changes in brain functional connectivity. However, apparent variations over time may also arise from stationary signals when the sample duration is finite. Recently, a vector autoregressive (VAR) null model has been proposed to simulate real functional magnetic resonance imaging (fMRI) data, which provides a robust stationary model for identifying possible temporal dynamic changes in functional connectivity. In this work, we propose a simpler model that uses a filtered stationary dataset. The filtered stationary model generates statistically stationary time series from random data with a single prescribed correlation coefficient that is calculated as the average over the entire time series. In addition, we propose a dynamic model, which is better able to replicate real fMRI connectivity, estimated from monkey brain studies, than the two stationary models. We compare simulated results using these three models with the behavior of primary somatosensory cortex (S1) networks in anesthetized squirrel monkeys at high field (9.4 T), using a sliding window correlation analysis. We found that at short window sizes, both stationary models reproduced the distribution of correlations of real signals well, but at longer window sizes, a dynamic model reproduced the distribution of correlations of real signals better than the stationary models. While stationary models replicate several features of real data, a close representation of the behavior of resting-state data acquired from somatosensory cortex of non-human primates is obtained only when a dynamic correlation is introduced, suggesting dynamic variations in connectivity are real. Hum Brain Mapp 37:3897-3910, 2016. © 2016 Wiley Periodicals, Inc.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants' eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc.

Dynamic regional phase synchrony (DRePS): An Instantaneous Measure of Local fMRI Connectivity Within Spatially Clustered Brain Areas.

Dynamic functional brain connectivity analysis is a fast expanding field in computational neuroscience research with the promise of elucidating brain network interactions. Sliding temporal window based approaches are commonly used in order to explore dynamic behavior of brain networks in task-free functional magnetic resonance imaging (fMRI) data. However, the low effective temporal resolution of sliding window methods fail to capture the full dynamics of brain activity at each time point. These also require subjective decisions regarding window size and window overlap. In this study, we introduce dynamic regional phase synchrony (DRePS), a novel analysis approach that measures mean local instantaneous phase coherence within adjacent fMRI voxels. We evaluate the DRePS framework on simulated data showing that the proposed measure is able to estimate synchrony at higher temporal resolution than sliding windows of local connectivity. We applied DRePS analysis to task-free fMRI data of 20 control subjects, revealing ultra-slow dynamics of local connectivity in different brain areas. Spatial clustering based on the DRePS feature time series reveals biologically congruent local phase synchrony networks (LPSNs). Taken together, our results demonstrate three main findings. Firstly, DRePS has increased temporal sensitivity compared to sliding window correlation analysis in capturing locally synchronous events. Secondly, DRePS of task-free fMRI reveals ultra-slow fluctuations of ∼0.002-0.02 Hz. Lastly, LPSNs provide plausible spatial information about time-varying brain local phase synchrony. With the DRePS method, we introduce a framework for interrogating brain local connectivity, which can potentially provide biomarkers of human brain function in health and disease. Hum Brain Mapp 37:1970-1985, 2016. © 2016 Wiley Periodicals, Inc.

Indian summer monsoon rainfall and its relation with SST in the equatorial Atlantic and Pacific Oceans

ABSTRACTA better understanding of the factors responsible for the variability in Indian summer monsoon rainfall (ISMR) is important in the present day climate because it is vulnerable to climate change. Here, we report the relationship between the Atlantic and Pacific sea surface temperature (SST) anomalies with ISMR. We developed a new ISMR index based on the high correlation of the SST anomaly over the oceanic regions during March–April, prior to the southwest monsoon season. The study was performed by utilizing rainfall data from the India Meteorological Department and SST anomaly derived from monthly extended reconstruction sea surface temperature data. We found that the relationship of the SST anomaly in the southwest Pacific region during the March–April period with the following ISMR is persistent in the 31‐year sliding window correlation analysis. SST anomaly in the Equatorial Atlantic during the March–April period is related almost in a similar manner to the following ISMR. However, persistence of the significance level is fluctuating. This indicates that the ISMR and the SST anomalies have varying relationships. To find out the fluctuation in the relationship between ISMR and the SSTs, we subjected the parameters to harmonic analysis. We noticed that the SST anomalies have similar kind of variability in the multidecadal (32 year), decadal (10.5) and interdecadal (5.6) periodicities in both Atlantic and southwest Pacific regions. The harmonics of SST and ISMR in the multidecadal time scale is out of phase and that for decadal and interdecadal time scales are in phase.

High-speed real-time resting-state FMRI using multi-slab echo-volumar imaging.

We recently demonstrated that ultra-high-speed real-time fMRI using multi-slab echo-volumar imaging (MEVI) significantly increases sensitivity for mapping task-related activation and resting-state networks (RSNs) compared to echo-planar imaging (Posse et al., 2012). In the present study we characterize the sensitivity of MEVI for mapping RSN connectivity dynamics, comparing independent component analysis (ICA) and a novel seed-based connectivity analysis (SBCA) that combines sliding-window correlation analysis with meta-statistics. This SBCA approach is shown to minimize the effects of confounds, such as movement, and CSF and white matter signal changes, and enables real-time monitoring of RSN dynamics at time scales of tens of seconds. We demonstrate highly sensitive mapping of eloquent cortex in the vicinity of brain tumors and arterio-venous malformations, and detection of abnormal resting-state connectivity in epilepsy. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. The fast acquisition speed of MEVI enabled segregation of cardiac-related signal pulsation using ICA, which revealed distinct regional differences in pulsation amplitude and waveform, elevated signal pulsation in patients with arterio-venous malformations and a trend toward reduced pulsatility in gray matter of patients compared with healthy controls. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. This novel fMRI methodology is particularly promising for mapping eloquent cortex in patients with neurological disease, having variable degree of cooperation in task-based fMRI. In conclusion, ultra-high-real-time speed fMRI enhances the sensitivity of mapping the dynamics of resting-state connectivity and cerebro-vascular pulsatility for clinical and neuroscience research applications.