The effect of model order selection in group PICA

Functional connectivity dynamics slow with descent from wakefulness to sleep

The transition from wakefulness to sleep is accompanied by widespread changes in brain functioning. Here we investigate the implications of this transition for interregional functional connectivity and their dynamic changes over time. Simultaneous EEG-fMRI was used to measure brain functional activity of 21 healthy participants as they transitioned from wakefulness into sleep. fMRI volumes were independent component analysis (ICA)-decomposed, yielding 42 neurophysiological sources. Static functional connectivity (FC) was estimated from independent component time courses. A sliding window method and k-means clustering (k = 7, L2-norm) were used to estimate dynamic FC. Static FC in Wake and Stage-2 Sleep (NREM2) were largely similar. By contrast, FC dynamics across wake and sleep differed, with transitions between FC states occurring more frequently during wakefulness than during NREM2. Evidence of slower FC dynamics during sleep is discussed in relation to sleep-related reductions in effective connectivity and synaptic strength.

Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

A study of neural activity and functional connectivity within the olfactory brain network in Parkinson's disease

Olfactory dysfunction is an early manifestation of Parkinson's disease (PD). The present study aimed to illustrate potential differences between PD patients and healthy controls in terms of neural activity and functional connectivity within the olfactory brain network. Twenty PD patients and twenty healthy controls were examined with olfactory fMRI and resting-state fMRI. Data analysis of olfactory fMRI included data-driven tensorial independent component (ICA) and task-driven general linear model (GLM) analyses. Data analysis of resting-state fMRI included probabilistic ICA based on temporal concatenation and functional connectivity analysis within the olfactory network. ICA of olfactory fMRI identified an olfactory network consisting of the posterior piriform cortex, insula, right orbitofrontal cortex and thalamus. Recruitment of this network was less significant for PD patients. GLM analysis revealed significantly lower activity in the insula bilaterally and the right orbitofrontal cortex in PD compared to healthy controls but no significant differences in the olfactory cortex itself. Analysis of resting-state fMRI did not reveal any differences in the functional connectivity within the olfactory, default mode, salience or central executive networks between the two groups. In conclusion, olfactory dysfunction in PD is associated with less significant recruitment of the olfactory brain network. ICA could demonstrate differences in both the olfactory cortex and its main projections, compared to GLM that revealed differences only on the latter. Resting-state fMRI did not reveal any significant differences in functional connectivity within the olfactory, default mode, salience and central executive networks in this cohort.

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Less significant recruitment of the olfactory brain network was found in Parkinson's disease.

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Independent component analysis reveals differences in both olfactory cortex and its projections.

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Differences in functional connectivity within the olfactory network were not significant.

The Nature of the Task Influences Intrinsic Connectivity Networks: An Exploratory fMRI Study in Healthy Subjects

Task-induced variations in neural activity and their effects on the topological architecture of intrinsic connectivity networks (ICNs) of the brain are still a matter of ongoing research. In this exploratory study, we used spatial independent component analysis (ICA) as a data-driven technique to characterize ICNs related to two different tasks in healthy subjects who underwent 3T blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI). The fMRI tasks consisted of (a) a viscerosensory stimulation of an internal organ (interoceptive task), and (b) passive viewing of emotionally expressive faces and pictures from the International Affective Picture System (exteroceptive emotion task). Comparison of the network volumes and peak activations during each task condition demonstrated that changes in ICN volume and corresponding peak activation differed between the interoceptive and exteroceptive emotion tasks when compared to the baseline rest. Further, salience network was the most task-activated ICN for both fMRI task conditions. However, different spatial characteristics were observed between the salience networks derived from the interoceptive task and the one derived from the exteroceptive emotion task. This study is a step in the direction of better understanding the influence of task condition on ICN topology. Future research with a larger sample size and task variations should delve deeper into what aspects of network topology really matter, with further investigations regarding the observed differences due to gender and age.

Alterations in functional networks during cue-reactivity in Internet gaming disorder

BACKGROUND

Cue-induced brain reactivity has been suggested to be a fundamental and important mechanism explaining the development, maintenance, and relapse of addiction, including Internet gaming disorder (IGD). Altered activity in addiction-related brain regions has been found during cue-reactivity in IGD using functional magnetic resonance imaging (fMRI), but less is known regarding the alterations of coordinated whole brain activity patterns in IGD.

METHODS

To investigate the activity of temporally coherent, large-scale functional brain networks (FNs) during cue-reactivity in IGD, independent component analysis was applied to fMRI data from 29 male subjects with IGD and 23 matched healthy controls (HC) performing a cue-reactivity task involving Internet gaming stimuli (i.e., game cues) and general Internet surfing-related stimuli (i.e., control cues).

RESULTS

Four FNs were identified that were related to the response to game cues relative to control cues and that showed altered engagement/disengagement in IGD compared with HC. These FNs included temporo-occipital and temporo-insula networks associated with sensory processing, a frontoparietal network involved in memory and executive functioning, and a dorsal-limbic network implicated in reward and motivation processing. Within IGD, game versus control engagement of the temporo-occipital and frontoparietal networks were positively correlated with IGD severity. Similarly, disengagement of temporo-insula network was negatively correlated with higher game-craving.

DISCUSSION

These findings are consistent with altered cue-reactivity brain regions reported in substance-related addictions, providing evidence that IGD may represent a type of addiction. The identification of the networks might shed light on the mechanisms of the cue-induced craving and addictive Internet gaming behaviors.

Atypical Functional Connectivity Associated with Autonomous Sensory Meridian Response: An Examination of Five Resting-State Networks

Autonomous sensory meridian response (ASMR) is a perceptual phenomenon in which specific auditory and/or visual stimuli consistently elicit tingling sensations on the neck, scalp, and shoulders, as well as a positive and relaxed emotional state. The “ASMR triggers” that initiate these responses generally consist of soft sounds (e.g., whispering), repetitive noises (e.g., tapping sounds), or videos of people performing socially intimate acts (e.g., watching someone brush her hair). Despite being a relatively common phenomenon, little is known about the neural substrates of ASMR. In the current research, resting-state functional magnetic resonance imaging (fMRI) was used to examine whether ASMR was associated with atypical patterns of functional connectivity. Seventeen individuals with ASMR and 17 matched control participants underwent an anatomical MRI scan and a resting-state fMRI scan. An independent components analysis was used to identify the default mode, salience, central executive, sensorimotor, and visual networks. An analysis of variance with group (ASMR vs. control) as a between-subjects variable was performed to contrast the functional connectivity of each of these networks. The results demonstrated that ASMR was associated with reduced functional connectivity in the salience and visual networks, and with atypical patterns of connectivity in the default mode, central executive, and sensorimotor networks.

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning.

Migraine in the Young Brain: Adolescents vs. Young Adults

Migraine is a disease that peaks in late adolescence and early adulthood. The aim of this study was to evaluate age-related brain changes in resting state functional connectivity (rs-FC) in migraineurs vs. age-sex matched healthy controls at two developmental stages: adolescence vs. young adulthood. The effect of the disease was assessed within each developmental group and age- and sex-matched healthy controls and between developmental groups (migraine-related age effects). Globally the within group comparisons indicated more widespread abnormal rs-FC in the adolescents than in the young adults and more abnormal rs-FC associated with sensory networks in the young adults. Direct comparison of the two groups showed a number of significant changes: (1) more connectivity changes in the default mode network in the adolescents than in the young adults; (2) stronger rs-FC in the cerebellum network in the adolescents in comparison to young adults; and (3) stronger rs-FC in the executive and sensorimotor network in the young adults. The duration and frequency of the disease were differently associated with baseline intrinsic connectivity in the two groups. fMRI resting state networks demonstrate significant changes in brain function at critical time point of brain development and that potentially different treatment responsivity for the disease may result.

Brain networks of happiness: dynamic functional connectivity among the default, cognitive and salience networks relates to subjective well-being

Subjective well-being (SWB) reflects the cognitive and emotional evaluations of an individual's life and plays an important role in individual's success in health, work and social relationships. Although previous studies have revealed the spontaneous brain activity underlying SWB, little is known about the relationship between brain network interactions and SWB. The present study investigated the static and dynamic functional connectivity among large-scale brain networks during resting state functional magnetic resonance imaging (fMRI) in relation to SWB in two large independent datasets. The results showed that SWB is negatively correlated with static functional connectivity between the salience network (SN) and the anterior default mode network (DMN). Dynamic functional network connectivity (dFNC) analysis found that SWB is negatively correlated with the fraction of time that participants spent in a brain state characterized by weak cross-network connectivity (between the DMN, SN and frontal-parietal network [FPN]) and strong within-network connectivity (within the DMN and within the FPN). This connectivity profile may account for the good mental adaptability and flexible information communication of people with high levels of SWB. The dFNC results were well replicated with different analysis parameters and further validated in an independent sample. Taken together, these findings reveal that the dynamic interaction between networks involved in self-reflection, emotional regulation and cognitive control underlies SWB.

Spatial source phase: A new feature for identifying spatial differences based on complex-valued resting-state fMRI data

Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data-driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex-valued resting-state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post-ICA phase de-ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel-wise F-test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.

Brain resting-state connectivity in the development of secondary hyperalgesia in healthy men

Central sensitization is a condition in which there is an abnormal responsiveness to nociceptive stimuli. As such, the process may contribute to the development and maintenance of pain. Factors influencing the propensity for development of central sensitization have been a subject of intense debate and remain elusive. Injury-induced secondary hyperalgesia can be elicited by experimental pain models in humans, and is believed to be a result of central sensitization. Secondary hyperalgesia may thus reflect the individual level of central sensitization. The objective of this study was to investigate possible associations between increasing size of secondary hyperalgesia area and brain connectivity in known resting-state networks. We recruited 121 healthy participants (male, age 22, SD 3.35) who underwent resting-state functional magnetic resonance imaging. Prior to the scan session, areas of secondary hyperalgesia following brief thermal sensitization (3 min. 45 °C heat stimulation) were evaluated in all participants. 115 participants were included in the final analysis. We found a positive correlation (increasing connectivity) with increasing area of secondary hyperalgesia in the sensorimotor- and default mode networks. We also observed a negative correlation (decreasing connectivity) with increasing secondary hyperalgesia area in the sensorimotor-, fronto-parietal-, and default mode networks. Our findings indicate that increasing area of secondary hyperalgesia is associated with increasing and decreasing connectivity in multiple networks, suggesting that differences in the propensity for central sensitization, assessed as secondary hyperalgesia areas, may be expressed as differences in the resting-state central neuronal activity.

Replication of Resting State-Task Network Correspondence and Novel Findings on Brain Network Activation During Task fMRI in the Human Connectome Project Study

There have been many recent reports highlighting a crisis in replication and reliability of research in psychology, neuroscience, and neuroimaging. After a series of reports uncovered various methodological problems with functional magnetic resonance imaging (fMRI) research, considerable attention has been given to principles and practices to improve reproducibility of neuroimaging findings, including promotion of openness, transparency, and data sharing. However, much less attention has been given to use of open access neuroimaging datasets to conduct replication studies. A major barrier to reproducing neuroimaging studies is their high cost, in money and labor, and utilizing such datasets is an obvious solution for breaking down this barrier. The Human Connectome Project (HCP) is an open access dataset consisting of extensive neurological, behavioral, and genetics assessments and neuroimaging data from over 1,100 individuals. In the present study, findings supporting the replication of a highly cited neuroimaging study that showed correspondence between resting state and task brain networks, and novel findings on activation of brain networks during task performance that arose with this exercise are presented as a demonstration of use of the HCP for replication studies.

Response inhibition and fronto-striatal-thalamic circuit dysfunction in cocaine addiction

BACKGROUND

Many studies have investigated how cognitive control may be compromised in cocaine addiction. Here, we extend this literature by employing spatial Independent Component Analysis (ICA) to describe circuit dysfunction in relation to impairment in response inhibition in cocaine addiction.

METHODS

Fifty-five cocaine-dependent (CD) and 55 age- and sex-matched non-drug-using healthy control individuals (HC) participated in the study. Task-relatedness of 40 independent components (ICs) was assessed using multiple regression analyses of component time courses with the modeled time courses of hemodynamic activity convolved with go success (GS), stop success (SS) and stop error (SE). This procedure produced beta-weights that represented the degree to which each IC was temporally associated with, or 'engaged', by each task event.

RESULTS

Behaviorally, CD participants showed prolonged stop signal reaction times (SSRTs) as compared to HC participants (p < 0.01). ICA identified two networks that showed differences in engagement related to SS between CD and HC (p < 0.05, FDR-corrected). The activity of the fronto-striatal-thalamic network was negatively correlated with SSRTs in HC but not in CD, suggesting a specific role of this network in mediating deficits of response inhibition in CD individuals. In contrast, the engagement of the fronto-parietal-temporal network did not relate to SSRTs, was similarly less engaged for both SS and SE trials, and may reflect attentional dysfunction in cocaine addiction.

CONCLUSIONS

This study highlights the utility of ICA in identifying neural circuitry engagement related to SST performance and suggests that specific networks may represent important targets in remedying executive-control impairment in cocaine addiction.

An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia

Independent component analysis (ICA) and seed-based analyses are widely used techniques for studying intrinsic neuronal activity in task-based or resting scans. In this work, we show there is a direct link between the two, and show that there are some important differences between the two approaches in terms of what information they capture. We developed an enhanced connectivity-matrix independent component analysis (cmICA) for calculating whole brain voxel maps of functional connectivity, which reduces the computational complexity of voxel-based connectivity analysis on performing many temporal correlations. We also show there is a mathematical equivalency between parcellations on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this cost-efficient data-driven method to examine the resting state fMRI connectivity in schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further quantified the relationship between brain functional connectivity and cognitive performances measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality, primarily a decrease, in functional connectivity both between networks and within different network hubs. Specific functional connectivity decreases were associated with MATRICS performance deficits. In addition, we found that resting state functional connectivity decreases was extensively associated with aging regardless of groups. In contrast, there was no relationship between positive and negative symptoms in the patients and functional connectivity. In sum, we have developed a novel mathematical relationship between ICA and seed-based connectivity that reduces computational complexity, which has broad applicability, and showed a specific application of this approach to characterize connectivity changes associated with cognitive scores in SZ.

The role of diversity in data-driven analysis of multi-subject fMRI data: Comparison of approaches based on independence and sparsity using global performance metrics

Data-driven methods have been widely used in functional magnetic resonance imaging (fMRI) data analysis. They extract latent factors, generally, through the use of a simple generative model. Independent component analysis (ICA) and dictionary learning (DL) are two popular data-driven methods that are based on two different forms of diversity-statistical properties of the data-statistical independence for ICA and sparsity for DL. Despite their popularity, the comparative advantage of emphasizing one property over another in the decomposition of fMRI data is not well understood. Such a comparison is made harder due to the differences in the modeling assumptions between ICA and DL, as well as within different ICA algorithms where each algorithm exploits a different form of diversity. In this paper, we propose the use of objective global measures, such as time course frequency power ratio, network connection summary, and graph theoretical metrics, to gain insight into the role that different types of diversity have on the analysis of fMRI data. Four ICA algorithms that account for different types of diversity and one DL algorithm are studied. We apply these algorithms to real fMRI data collected from patients with schizophrenia and healthy controls. Our results suggest that no one particular method has the best performance using all metrics, implying that the optimal method will change depending on the goal of the analysis. However, we note that in none of the scenarios we test the highly popular Infomax provides the best performance, demonstrating the cost of exploiting limited form of diversity.

Intrinsic Functional Connectivity in Preterm Infants with Fetal Growth Restriction Evaluated at 12 Months Corrected Age

Fetal growth restriction (FGR) affects brain development in preterm infants, but little is known about its effects on resting-state functional connectivity. We compared 20 preterm infants, born at <34 weeks of gestation with abnormal antenatal Doppler measurements and birth weights <10th percentile, with 20 appropriate for gestational age preterm infants of similar gestational age and 20 term infants. They were scanned without sedation at 12 months of age and screened for autistic traits at 26 months. Resting functional connectivity was assessed using group independent component analysis and seed-based correlation analysis. The groups showed 10 common resting-state networks involving cortical, subcortical regions, and the cerebellum. Only infants with FGR showed patterns of increased connectivity in the visual network and decreased connectivity in the auditory/language and dorsal attention networks. No significant differences between groups were found using seed-based correlation analysis. FGR infants displayed a higher frequency of early autism features, related to decreased connectivity involving the salience network, than term infants. These data suggest that FGR is an independent risk factor for disrupted intrinsic functional connectivity in preterm infants when they are 1-year old and provide more clues about the neurodevelopmental abnormalities reported in this population.

Network Analysis in Disorders of Consciousness: Four Problems and One Proposed Solution (Exponential Random Graph Models)

In recent years, the study of the neural basis of consciousness, particularly in the context of patients recovering from severe brain injury, has greatly benefited from the application of sophisticated network analysis techniques to functional brain data. Yet, current graph theoretic approaches, as employed in the neuroimaging literature, suffer from four important shortcomings. First, they require arbitrary fixing of the number of connections (i.e., density) across networks which are likely to have different "natural" (i.e., stable) density (e.g., patients vs. controls, vegetative state vs. minimally conscious state patients). Second, when describing networks, they do not control for the fact that many characteristics are interrelated, particularly some of the most popular metrics employed (e.g., nodal degree, clustering coefficient)-which can lead to spurious results. Third, in the clinical domain of disorders of consciousness, there currently are no methods for incorporating structural connectivity in the characterization of functional networks which clouds the interpretation of functional differences across groups with different underlying pathology as well as in longitudinal approaches where structural reorganization processes might be operating. Finally, current methods do not allow assessing the dynamics of network change over time. We present a different framework for network analysis, based on Exponential Random Graph Models, which overcomes the above limitations and is thus particularly well suited for clinical populations with disorders of consciousness. We demonstrate this approach in the context of the longitudinal study of recovery from coma. First, our data show that throughout recovery from coma, brain graphs vary in their natural level of connectivity (from 10.4 to 14.5%), which conflicts with the standard approach of imposing arbitrary and equal density thresholds across networks (e.g., time-points, subjects, groups). Second, we show that failure to consider the interrelation between network measures does lead to spurious characterization of both inter- and intra-regional brain connectivity. Finally, we show that Separable Temporal ERGM can be employed to describe network dynamics over time revealing the specific pattern of formation and dissolution of connectivity that accompany recovery from coma.

Cortical Statistical Correlation Tomography of EEG Resting State Networks

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. A new computational framework for reconstructing RSNs with human EEG data has been developed in the present study. The proposed framework utilizes independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was evaluated on three sets of resting-state EEG data obtained in the comparison of two conditions: (1) healthy controls with eyes closed and eyes open; (2) healthy controls and individuals with a balance disorder; (3) individuals with a balance disorder before and after receiving repetitive transcranial magnetic stimulation (rTMS) treatment. In these analyses, the same group of five RSNs with similar spatial and spectral patterns were successfully reconstructed by the proposed framework from each individual EEG dataset. These EEG RSN tomographic maps showed significant similarity with RSN templates derived from functional magnetic resonance imaging (fMRI). Furthermore, significant spatial and spectral differences of RSNs among compared conditions were observed in tomographic maps as well as their spectra, which were consistent with findings reported in the literature. Beyond the success of reconstructing EEG RSNs spatially on the cortical surface as in fMRI studies, this novel approach defines RSNs further with spectra, providing a new dimension in understanding and probing basic neural mechanisms of RSNs. The findings in patients' data further demonstrate its potential in identifying biomarkers for the diagnosis and treatment evaluation of neuropsychiatric disorders.

Diminished neural network dynamics in amnestic mild cognitive impairment

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.

Shared facial emotion processing functional network findings in medication-naïve major depressive disorder and healthy individuals: detection by sICA

BACKGROUND

The fundamental mechanism underlying emotional processing in major depressive disorder (MDD) remains unclear. To better understand the neural correlates of emotional processing in MDD, we investigated the role of multiple functional networks (FNs) during emotional stimuli processing.

METHODS

Thirty-two medication-naïve subjects with MDD and 36 healthy controls (HCs) underwent an emotional faces fMRI task that included neutral, happy and fearful expressions. Spatial independent component analysis (sICA) and general linear model (GLM) were conducted to examine the main effect of task condition and group, and two-way interactions of group and task conditions.

RESULTS

In sICA analysis, MDD patients and HCs together showed significant differences in task-related modulations in five FNs across task conditions. One FN mainly involving the ventral medial prefrontal cortex showed lower activation during fearful relative to happy condition. Two FNs mainly involving the bilateral inferior frontal gyrus and temporal cortex, showed opposing modulation relative to the ventral medial prefrontal cortex FN, i.e., greater activation during fearful relative to happy condition. Two remaining FNs involving the fronto-parietal and occipital cortices, showed reduced activation during both fearful and happy conditions relative to the neutral condition. However, MDD and HCs did not show significant differences in expression-related modulations in any FNs in this sample.

CONCLUSIONS

SICA revealed differing functional activation patterns than typical GLM-based analyses. The sICA findings demonstrated unique FNs involved in processing happy and fearful facial expressions. Potential differences between MDD and HCs in expression-related FN modulation should be investigated further.

Modulation of Functional Connectivity in Auditory-Motor Networks in Musicians Compared with Nonmusicians

Correlation of spontaneous fluctuations at rest between anatomically distinct brain areas are proposed to reflect the profile of individual a priori cognitive biases, coded as synaptic efficacies in cortical networks. Here, we investigate functional connectivity at rest (rs-FC) in musicians and nonmusicians to test for differences in auditory, motor, and audiomotor connectivity. As expected, musicians had stronger rs-FC between the right auditory cortex (AC) and the right ventral premotor cortex than nonmusicians, and this stronger rs-FC was greater in musicians with more years of practice. We also found reduced rs-FC between the motor areas that control both hands in musicians compared with nonmusicians, which was more evident in the musicians whose instrument required bimanual coordination and as a function of hours of practice. Finally, we replicated previous morphometric data to show an increased volume in the right AC in musicians, which was greater in those with earlier musical training, and that this anatomic feature was in turn related to greater rs-FC between auditory and motor systems. These results show that functional coupling within the motor system and between motor and auditory areas is modulated as a function of musical training, suggesting a link between anatomic and functional brain features.

PreSurgMapp: a MATLAB Toolbox for Presurgical Mapping of Eloquent Functional Areas Based on Task-Related and Resting-State Functional MRI

The main goal of brain tumor surgery is to maximize tumor resection while minimizing the risk of irreversible postoperative functional sequelae. Eloquent functional areas should be delineated preoperatively, particularly for patients with tumors near eloquent areas. Functional magnetic resonance imaging (fMRI) is a noninvasive technique that demonstrates great promise for presurgical planning. However, specialized data processing toolkits for presurgical planning remain lacking. Based on several functions in open-source software such as Statistical Parametric Mapping (SPM), Resting-State fMRI Data Analysis Toolkit (REST), Data Processing Assistant for Resting-State fMRI (DPARSF) and Multiple Independent Component Analysis (MICA), here, we introduce an open-source MATLAB toolbox named PreSurgMapp. This toolbox can reveal eloquent areas using comprehensive methods and various complementary fMRI modalities. For example, PreSurgMapp supports both model-based (general linear model, GLM, and seed correlation) and data-driven (independent component analysis, ICA) methods and processes both task-based and resting-state fMRI data. PreSurgMapp is designed for highly automatic and individualized functional mapping with a user-friendly graphical user interface (GUI) for time-saving pipeline processing. For example, sensorimotor and language-related components can be automatically identified without human input interference using an effective, accurate component identification algorithm using discriminability index. All the results generated can be further evaluated and compared by neuro-radiologists or neurosurgeons. This software has substantial value for clinical neuro-radiology and neuro-oncology, including application to patients with low- and high-grade brain tumors and those with epilepsy foci in the dominant language hemisphere who are planning to undergo a temporal lobectomy.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.

Attention Shifts Recruit the Monkey Default Mode Network

A unifying function associated with the default mode network (DMN), which is more active during rest than under active task conditions, has been difficult to define. The DMN is activated during monitoring the external world for unexpected events, as a sentinel, and when humans are engaged in high-level internally focused tasks. The existence of DMN correlates in other species, such as mice, challenge the idea that internally focused, high-level cognitive operations, such as introspection, autobiographical memory retrieval, planning the future, and predicting someone else's thoughts, are evolutionarily preserved defining properties of the DMN. A recent human study demonstrated that demanding cognitive shifts could recruit the DMN, yet it is unknown whether this holds for nonhuman species. Therefore, we tested whether large changes in cognitive context would recruit DMN regions in female and male nonhuman primates. Such changes were measured as displacements of spatial attentional weights based on internal rules of relevance (spatial shifts) compared with maintaining attentional weights at the same location (stay events). Using fMRI in macaques, we detected that a cortical network, activated during shifts, largely overlapped with the DMN. Moreover, fMRI time courses sampled from independently defined DMN foci showed significant shift selectivity during the demanding attention task. Finally, functional clustering based on independent resting state data revealed that DMN and shift regions clustered conjointly, whereas regions activated during the stay events clustered apart. We therefore propose that cognitive shifting in primates generally recruits DMN regions. This might explain a breakdown of the DMN in many neurological diseases characterized by declined cognitive flexibility.SIGNIFICANCE STATEMENT Activation of the human default mode network (DMN) can be measured with fMRI when subjects shift thoughts between high-level internally directed cognitive states, when thinking about the self, the perspective of others, when imagining future and past events, and during mind wandering. Furthermore, the DMN is activated as a sentinel, monitoring the environment for unexpected events. Arguably, these cognitive processes have in common fast and substantial changes in cognitive context. As DMN activity has also been reported in nonhuman species, we tested whether shifts in spatial attention activated the monkey DMN. Core monkey DMN and shift-selective regions shared several functional properties, indicating that cognitive shifting, in general, might constitute one of the evolutionarily preserved functions of the DMN.

Salience network dynamics underlying successful resistance of temptation

Self-control and the ability to resist temptation are critical for successful completion of long-term goals. Contemporary models in cognitive neuroscience emphasize the primary role of prefrontal cognitive control networks in aligning behavior with such goals. Here, we use gaze pattern analysis and dynamic functional connectivity fMRI data to explore how individual differences in the ability to resist temptation are related to intrinsic brain dynamics of the cognitive control and salience networks. Behaviorally, individuals exhibit greater gaze distance from target location (e.g. higher distractibility) during presentation of tempting erotic images compared with neutral images. Individuals whose intrinsic dynamic functional connectivity patterns gravitate toward configurations in which salience detection systems are less strongly coupled with visual systems resist tempting distractors more effectively. The ability to resist tempting distractors was not significantly related to intrinsic dynamics of the cognitive control network. These results suggest that susceptibility to temptation is governed in part by individual differences in salience network dynamics and provide novel evidence for involvement of brain systems outside canonical cognitive control networks in contributing to individual differences in self-control.

ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks

Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.

Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to openness to experience

Imagination and creative cognition are often associated with the brain's default network (DN). Recent evidence has also linked cognitive control systems to performance on tasks involving imagination and creativity, with a growing number of studies reporting functional interactions between cognitive control and DN regions. We sought to extend the emerging literature on brain dynamics supporting imagination by examining individual differences in large-scale network connectivity in relation to Openness to Experience, a personality trait typified by imagination and creativity. To this end, we obtained personality and resting-state fMRI data from two large samples of participants recruited from the United States and China, and we examined contributions of Openness to temporal shifts in default and cognitive control network interactions using multivariate structural equation modeling and dynamic functional network connectivity analysis. In Study 1, we found that Openness was related to the proportion of scan time (i.e., "dwell time") that participants spent in a brain state characterized by positive correlations among the default, executive, salience, and dorsal attention networks. Study 2 replicated and extended the effect of Openness on dwell time in a correlated brain state comparable to the state found in Study 1, and further demonstrated the robustness of this effect in latent variable models including fluid intelligence and other major personality factors. The findings suggest that Openness to Experience is associated with increased functional connectivity between default and cognitive control systems, a connectivity profile that may account for the enhanced imaginative and creative abilities of people high in Openness to Experience.

Schizophrenia Shows Disrupted Links between Brain Volume and Dynamic Functional Connectivity

Studies featuring multimodal neuroimaging data fusion for understanding brain function and structure, or disease characterization, leverage the partial information available in each of the modalities to reveal data variations not exhibited through the independent analyses. Similar to other complex syndromes, the characteristic brain abnormalities in schizophrenia may be better understood with the help of the additional information conveyed by leveraging an advanced modeling method involving multiple modalities. In this study, we propose a novel framework to fuse feature spaces corresponding to functional magnetic resonance imaging (functional) and gray matter (structural) data from 151 schizophrenia patients and 163 healthy controls. In particular, the features for the functional and structural modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) maps and the intensities of the gray matter (GM) maps, respectively. The dFNC maps are estimated from group independent component analysis (ICA) network time-courses by first computing windowed functional correlations using a sliding window approach, and then estimating subject specific states from this windowed data using temporal ICA followed by spatio-temporal regression. For each subject, the functional data features are horizontally concatenated with the corresponding GM features to form a combined feature space that is subsequently decomposed through a symmetric multimodal fusion approach involving a combination of multiset canonical correlation analysis (mCCA) and joint ICA (jICA). Our novel combined analyses successfully linked changes in the two modalities and revealed significantly disrupted links between GM volumes and time-varying functional connectivity in schizophrenia. Consistent with prior research, we found significant group differences in GM comprising regions in the superior parietal lobule, precuneus, postcentral gyrus, medial/superior frontal gyrus, superior/middle temporal gyrus, insula and fusiform gyrus, and several significant aberrations in the inter-regional functional connectivity strength as well. Importantly, structural and dFNC measures have independently shown changes associated with schizophrenia, and in this work we begin the process of evaluating the links between the two, which could shed light on the illness beyond what we can learn from a single imaging modality. In future work, we plan to evaluate replication of the inferred structure-function relationships in independent partitions of larger multi-modal schizophrenia datasets.

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and “task-free” technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients.

Identifying functional network changing patterns in individuals at clinical high-risk for psychosis and patients with early illness schizophrenia: A group ICA study

Although individuals at clinical high risk (CHR) for psychosis exhibit a psychosis-risk syndrome involving attenuated forms of the positive symptoms typical of schizophrenia (SZ), it remains unclear whether their resting-state brain intrinsic functional networks (INs) show attenuated or qualitatively distinct patterns of functional dysconnectivity relative to SZ patients. Based on resting-state functional magnetic imaging data from 70 healthy controls (HCs), 53 CHR individuals (among which 41 subjects were antipsychotic medication-naive), and 58 early illness SZ (ESZ) patients (among which 53 patients took antipsychotic medication) within five years of illness onset, we estimated subject-specific INs using a novel group information guided independent component analysis (GIG-ICA) and investigated group differences in INs. We found that when compared to HCs, both CHR and ESZ groups showed significant differences, primarily in default mode, salience, auditory-related, visuospatial, sensory-motor, and parietal INs. Our findings suggest that widespread INs were diversely impacted. More than 25% of voxels in the identified significant discriminative regions (obtained using all 19 possible changing patterns excepting the no-difference pattern) from six of the 15 interrogated INs exhibited monotonically decreasing Z-scores (in INs) from the HC to CHR to ESZ, and the related regions included the left lingual gyrus of two vision-related networks, the right postcentral cortex of the visuospatial network, the left thalamus region of the salience network, the left calcarine region of the fronto-occipital network and fronto-parieto-occipital network. Compared to HCs and CHR individuals, ESZ patients showed both increasing and decreasing connectivity, mainly hypo-connectivity involving 15% of the altered voxels from four INs. The left supplementary motor area from the sensory-motor network and the right inferior occipital gyrus in the vision-related network showed a common abnormality in CHR and ESZ groups. Some brain regions also showed a CHR-unique alteration (primarily the CHR-increasing connectivity). In summary, CHR individuals generally showed intermediate connectivity between HCs and ESZ patients across multiple INs, suggesting that some dysconnectivity patterns evident in ESZ predate psychosis in attenuated form during the psychosis risk stage. Hence, these connectivity measures may serve as possible biomarkers to predict schizophrenia progression.

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder

BACKGROUND

Continued, persistent gambling to recover accumulating losses, or 'loss-chasing', is a behavioral pattern linked particularly closely to gambling disorder (GD) but may reflect impaired decision-making processes relevant to drug addictions like cocaine-use disorder (CUD). However, little is known regarding the neurocognitive mechanisms of this complex, maladaptive behavior, particularly in individuals with addictive disorders.

METHODS

Seventy participants (25 GD, 18 CUD, and 27 healthy comparison (HC)) completed a loss-chase task during fMRI. Engagement of functional brain networks in response to losing outcomes and during decision-making periods preceding choices to loss-chase or to quit chasing losses were investigated using independent component analysis (ICA). An exploratory factor analysis was performed to examine patterns of coordinated engagement across identified networks.

RESULTS

In GD relative to HC and CUD participants, choices to quit chasing were associated with greater engagement of a medial frontal executive-processing network. By comparison, CUD participants exhibited altered engagement of a striato-amygdala motivational network in response to losing outcomes as compared to HC, and during decision-making as compared to GD. Several other networks were differentially engaged during loss-chase relative to quit-chasing choices, but did not differ across participant groups. Exploratory factor analysis identified a system of coordinated activity across prefrontal executive-control networks that was greater in GD and CUD relative to HC participants and was associated with increased chasing persistence across all participants.

CONCLUSIONS

Results provide evidence of shared and distinct neurobiological mechanisms in substance and behavioral addictions, and lend insight into potential cognitive interventions targeting loss-chasing behavior in GD.

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study

BACKGROUND

A key challenge in building a brain graph using fMRI data is how to define the nodes. Spatial brain components estimated by independent components analysis (ICA) and regions of interest (ROIs) determined by brain atlas are two popular methods to define nodes in brain graphs. It is difficult to evaluate which method is better in real fMRI data.

NEW METHOD

Here we perform a simulation study and evaluate the accuracies of a few graph metrics in graphs with nodes of ICA components, ROIs, or modified ROIs in four simulation scenarios.

RESULTS

Graph measures with ICA nodes are more accurate than graphs with ROI nodes in all cases. Graph measures with modified ROI nodes are modulated by artifacts. The correlations of graph metrics across subjects between graphs with ICA nodes and ground truth are higher than the correlations between graphs with ROI nodes and ground truth in scenarios with large overlapped spatial sources. Moreover, moving the location of ROIs would largely decrease the correlations in all scenarios.

COMPARISON WITH EXISTING METHOD (S)

Evaluating graphs with different nodes is promising in simulated data rather than real data because different scenarios can be simulated and measures of different graphs can be compared with a known ground truth.

CONCLUSION

Since ROIs defined using brain atlas may not correspond well to real functional boundaries, overall findings of this work suggest that it is more appropriate to define nodes using data-driven ICA than ROI approaches in real fMRI data.

Distinct resting-state functional connections associated with episodic and visuospatial memory in older adults

Episodic and spatial memory are commonly impaired in ageing and Alzheimer's disease. Volumetric and task-based functional magnetic resonance imaging (fMRI) studies suggest a preferential involvement of the medial temporal lobe (MTL), particularly the hippocampus, in episodic and spatial memory processing. The present study examined how these two memory types were related in terms of their associated resting-state functional architecture. 3T multiband resting state fMRI scans from 497 participants (60–82 years old) of the cross-sectional Whitehall II Imaging sub-study were analysed using an unbiased, data-driven network-modelling technique (FSLNets). Factor analysis was performed on the cognitive battery; the Hopkins Verbal Learning test and Rey-Osterreith Complex Figure test factors were used to assess verbal and visuospatial memory respectively. We present a map of the macroscopic functional connectome for the Whitehall II Imaging sub-study, comprising 58 functionally distinct nodes clustered into five major resting-state networks. Within this map we identified distinct functional connections associated with verbal and visuospatial memory. Functional anticorrelation between the hippocampal formation and the frontal pole was significantly associated with better verbal memory in an age-dependent manner. In contrast, hippocampus–motor and parietal–motor functional connections were associated with visuospatial memory independently of age. These relationships were not driven by grey matter volume and were unique to the respective memory domain. Our findings provide new insights into current models of brain-behaviour interactions, and suggest that while both episodic and visuospatial memory engage MTL nodes of the default mode network, the two memory domains differ in terms of the associated functional connections between the MTL and other resting-state brain networks.

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Episodic and visuospatial memory engaged a common medial temporal lobe substrate at rest.

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However, the resting-state functional connections of the MTL differed based on the memory demand.

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Visuospatial memory was associated with hippocampal-parietal and motorparietal interaction.

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Verbal memory was associated with hippocampus-frontal pole anticorrelation.

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Findings provide novel insights into resting-state brain-behaviour interactions in older adults.

Contextual connectivity: A framework for understanding the intrinsic dynamic architecture of large-scale functional brain networks

Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states. Furthermore, each of these localized, dynamic connectivity states is associated with global changes in the whole-brain functional connectome. By nesting localized dynamic connectivity states within their whole-brain contexts, we demonstrate the relative temporal independence of brain networks. Our assay for functional autonomy of coordinated neural systems is broadly applicable, and our findings provide evidence of structure in temporal state dynamics that complements the well-described static spatial organization of the brain.

Functional connectivity and cognitive impairment in migraine with and without aura

Background

Several fMRI studies in migraine assessed resting state functional connectivity in different networks suggesting that this neurological condition was associated with brain functional alteration. The aim of present study was to explore the association between cognitive functions and cerebral functional connectivity, in default mode network, in migraine patients without and with aura, during interictal episodic attack.

Methods

Twenty-eight migraine patients (14 without and 14 with aura) and 14 matched normal controls, were consecutively recruited. A battery of standardized neuropsychological test was administered to evaluate cognitive functions and all subjects underwent a resting state with high field fMRI examination.

Results

Migraine patients did not show abnormalities in neuropsychological evaluation, while, we found a specific alteration in cortical network, if we compared migraine with and without aura. We observed, in migraine with aura, an increased connectivity in left angular gyrus, left supramarginal gyrus, right precentral gyrus, right postcentral gyrus, right insular cortex.

Conclusion

Our findings showed in migraine patients an alteration in functional connectivity architecture. We think that our results could be useful to better understand migraine pathogenesis.

Brain networks associated with cognitive and hedonic responses to a meal

BACKGROUND

We recently reported interrelated digestive, cognitive, and hedonic responses to a meal. The aim of this study was to identify brain networks related to the hedonic response to eating.

METHODS

Thirty-eight healthy subjects (20-38 age range) were evaluated after a 5-hour fast and after ingestion of a test meal (juice and warm ham and cheese sandwich, 300 mL, 425 kcal). Perceptual and affective responses (satiety, abdominal fullness, digestive well-being, and positive mood), and resting scans of the brain using functional MRI (3T Trio, Siemens, Germany) were evaluated immediately before and after the test meal. A high-order group independent component analysis was performed to investigate ingestion-related changes in the intrinsic connectivity of brain networks, with a focus on thalamic and insular networks.

KEY RESULTS

Ingestion induced satiation (3.3±0.4 score increase; P<.001) and abdominal fullness (2.4±0.3 score increase; P<.001). These sensations included an affective dimension involving digestive well-being (2.8±0.3 score increase; P<.001) and positive mood (1.8±0.2 score increase; P<.001). In general, thalamo-cortical connectivity increased with meal ingestion while insular-cortical connectivity mainly decreased. Furthermore, larger meal-induced changes (increase/decrease) in specific thalamic connections were associated with smaller changes in satiety/fullness. In contrast, a larger meal-induced decrease in insular-anterior cingulate cortex connectivity was associated with increased satiety, fullness, and digestive well-being.

CONCLUSIONS AND INFERENCES

Perceptual and emotional responses to food intake are related to brain connectivity in defined functional networks. Brain imaging may provide objective biomarkers of subjective effects of meal ingestion.

Aberrant Intra- and Internetwork Functional Connectivity in Depressed Parkinson's Disease

Much is known concerning the underlying mechanisms of Parkinson’s disease (PD) with depression, but our understanding of this disease at the neural-system level remains incomplete. This study used resting-state functional MRI (rs-fMRI) and independent component analysis (ICA) to investigate intrinsic functional connectivity (FC) within and between large-scale neural networks in 20 depressed PD (dPD) patients, 35 non-depressed PD (ndPD) patients, and 34 healthy controls (HC). To alleviate the influence caused by ICA model order selection, this work reported results from analyses at 2 levels (low and high model order). Within these two analyses, similar results were obtained: 1) dPD and ndPD patients relative to HC had reduced FC in basal ganglia network (BGN); 2) dPD compared with ndPD patients exhibited increased FC in left frontoparietal network (LFPN) and salience network (SN), and decreased FC in default-mode network (DMN); 3) dPD patients compared to HC showed increased FC between DMN and LFPN. Additionally, connectivity anomalies in the DMN, LFPN and SN correlated with the depression severity in patients with PD. Our findings confirm the involvement of BGN, DMN, LFPN and SN in depression in PD, facilitating the development of more detailed and integrative neural models of PD with depression.

Coupling of Action-Perception Brain Networks during Musical Pulse Processing: Evidence from Region-of-Interest-Based Independent Component Analysis

Our sense of rhythm relies on orchestrated activity of several cerebral and cerebellar structures. Although functional connectivity studies have advanced our understanding of rhythm perception, this phenomenon has not been sufficiently studied as a function of musical training and beyond the General Linear Model (GLM) approach. Here, we studied pulse clarity processing during naturalistic music listening using a data-driven approach (independent component analysis; ICA). Participants' (18 musicians and 18 controls) functional magnetic resonance imaging (fMRI) responses were acquired while listening to music. A targeted region of interest (ROI) related to pulse clarity processing was defined, comprising auditory, somatomotor, basal ganglia, and cerebellar areas. The ICA decomposition was performed under different model orders, i.e., under a varying number of assumed independent sources, to avoid relying on prior model order assumptions. The components best predicted by a measure of the pulse clarity of the music, extracted computationally from the musical stimulus, were identified. Their corresponding spatial maps uncovered a network of auditory (perception) and motor (action) areas in an excitatory-inhibitory relationship at lower model orders, while mainly constrained to the auditory areas at higher model orders. Results revealed (a) a strengthened functional integration of action-perception networks associated with pulse clarity perception hidden from GLM analyses, and (b) group differences between musicians and non-musicians in pulse clarity processing, suggesting lifelong musical training as an important factor that may influence beat processing.

The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs.

Higher body mass index is associated with reduced posterior default mode connectivity in older adults

Obesity is a complex neurobehavioral disorder that has been linked to changes in brain structure and function. However, the impact of obesity on functional connectivity and cognition in aging humans is largely unknown. Therefore, the association of body mass index (BMI), resting-state network connectivity, and cognitive performance in 712 healthy, well-characterized older adults of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (60-80 years old, mean BMI 27.6 kg/m²  ± 4.2 SD, main sample: n = 521, replication sample: n = 191) was determined. Statistical analyses included a multivariate model selection approach followed by univariate analyses to adjust for possible confounders. Results showed that a higher BMI was significantly associated with lower default mode functional connectivity in the posterior cingulate cortex and precuneus. The effect remained stable after controlling for age, sex, head motion, registration quality, cardiovascular, and genetic factors as well as in replication analyses. Lower functional connectivity in BMI-associated areas correlated with worse executive function. In addition, higher BMI correlated with stronger head motion. Using 3T neuroimaging in a large cohort of healthy older adults, independent negative associations of obesity and functional connectivity in the posterior default mode network were observed. In addition, a subtle link between lower resting-state connectivity in BMI-associated regions and cognitive function was found. The findings might indicate that obesity is associated with patterns of decreased default mode connectivity similar to those seen in populations at risk for Alzheimer's disease. Hum Brain Mapp 38:3502-3515, 2017. © 2017 Wiley Periodicals, Inc.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.