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The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. However, little is known about the dynamic characteristics of resting-state functional connectivity (RSFC). In this study, we thoroughly examined this question within and between multiple functional brain networks. Twenty-two healthy subjects were scanned in a resting state. Several of the RSFC networks observed, including the default-mode, motor, attention, memory, auditory, visual, language and subcortical networks, were first identified using a conventional voxel-wise correlation analysis with predefined region of interests (ROIs). Then, a variable parameter regression model combined with the Kalman filtering method was employed to detect the dynamic interactions between each ROI and all other brain voxels within each of the RSFC maps extracted above. Experimental results revealed that the functional interactions within each RSFC map showed time-varying properties, and that approximately 10-20% of the voxels within each RSFC map showed significant functional connectivity to each ROI during the scanning session. This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.

Background: Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder that is characterized by recurrent intrusive thoughts, ideas or images and repetitive ritualistic behaviours. Although focal structural and functional abnormalities in specific brain regions have been widely studied in populations with OCD, changes in the functional relations among them remain poorly understood. This study examined OCD-related alterations in functional connectivity patterns in the brain's top-down control network. Methods: We applied resting-state functional magnetic resonance imaging to investigate the correlation patterns of intrinsic or spontaneous blood oxygen level-dependent signal fluctuations in 18 patients with OCD and 16 healthy controls. The brain control networks were first constructed by thresholding temporal correlation matrices of 39 brain regions associated with top-down control and then analyzed using graph theory-based approaches. Results: Compared with healthy controls, the patients with OCD showed decreased functional connectivity in the posterior temporal regions and increased connectivity in various control regions such as the cingulate, precuneus, thalamus and cerebellum. Furthermore, the brain's control networks in the healthy controls showed small-world architecture (high clustering coefficients and short path lengths), suggesting an optimal balance between modularized and distributed information processing. In contrast, the patients with OCD showed significantly higher local clustering, implying abnormal functional organization in the control network. Further analysis revealed that the changes in network properties occurred in regions of increased functional connectivity strength in patients with OCD. Limitations: The patient group in the present study was heterogeneous in terms of symptom clusters, and most of the patients with OCD were medicated. Conclusion: Our preliminary results suggest that the organizational patterns of intrinsic brain activity in the control networks are altered in patients with OCD and thus provide empirical evidence for aberrant functional connectivity in the large-scale brain systems in people with this disorder.

We used resting-state functional MRI to investigate spatial patterns of spontaneous brain activity in 22 healthy elderly subjects, as well as 16 mild cognitive impairment (MCI) and 16 Alzheimer's disease (AD) patients. The pattern of intrinsic brain activity was measured by examining the amplitude of low-frequency fluctuations (ALFF) of blood oxygen level dependent signal during rest. There were widespread ALFF differences among the three groups throughout the frontal, temporal, and parietal cortices. Both AD and MCI patients showed decreased activity mainly in the medial parietal lobe region and lentiform nucleus, while there was increased activity in the lateral temporal regions and superior frontal and parietal regions as compared with controls. Compared with MCI, the AD patients showed decreased activity in the medial prefrontal cortex and increased activity in the superior frontal gyrus and inferior and superior temporal gyri. Specifically, the most significant ALFF differences among the groups appeared in the posterior cingulate cortex, with a reduced pattern of activity when comparing healthy controls, MCI, and AD patients. Additionally, we also showed that the regions with ALFF changes had significant correlations with the cognitive performance of patients as measured by mini-mental state examination scores. Finally, while taking gray matter volume as covariates, the ALFF results were approximately consistent with those without gray matter correction, implying that the functional analysis could not be explained by regional atrophy. Together, our results demonstrate that there is a specific pattern of ALFF in AD and MCI, thus providing insights into biological mechanisms of the diseases.

The anatomical connectivity of the human cerebral cortex resembles a ‘‘small-world’’ architecture, which is characterized by the coexistence of structurally segregated and integrative connectivity patterns. However, organizational differences in networks among individuals remain largely unknown. Here, we utilize diffusion tensor imaging tractography and graph-theoretical approaches to investigate the effects of sex and brain size on the topological organization of human cortical anatomical network. Weighted cortical networks were constructed from 72 young healthy participants by measuring anatomical connection densities between 78 cortical regions. As expected, all participants showed a small-world topology (high local clustering and short paths between nodes), which suggests a highly efficient topological organization. Furthermore, we found that females had greater local efficiencies than males. Moreover, smaller brains showed higher local efficiency in females but not in males, suggesting an interaction between sex and brain size. Specifically, we show that several brain regions (e.g., the precuneus, precentral gyrus, and lingual gyrus) had significant associations between nodal centrality and sex or brain size. Our findings suggest that anatomical network organization in the human brain is associated with sex and brain size and provide insights into the understanding of the structural substrates that underlie individual differences in behavior and cognition.

Background Internet gaming disorder (IGD) has become an increasing mental health problem worldwide. Decreased resting-state functional connectivity (rsFC) between the ventral tegmental area (VTA) and the nucleus accumbens (NAcc) has been found in substance use and is thought to play an important role in the development of substance addiction. However, rsFC between the VTA and NAcc in a non-substance addiction, such as IGD, has not been assessed previously. The current study aimed to investigate: (1) if individuals with IGD exhibit alterations in VTA-NAcc functional connectivity; and (2) whether VTA-NAcc functional connectivity is associated with subjective Internet craving. Methods Thirty-five male participants with IGD and 24 healthy control (HC) individuals participated in resting-state functional magnetic resonance imaging. Regions of interest (left NAcc, right NAcc and VTA) were selected based on the literature and were defined by placing spheres centered on Talairach Daemon coordinates. Results In comparison with HCs, individuals with IGD had significantly decreased rsFC between the VTA and right NAcc. Resting-state functional connectivity strength between the VTA and right NAcc was negatively correlated with self-reported subjective craving for the Internet. Conclusions These results suggest possible neural functional similarities between individuals with IGD and individuals with substance addictions. Electronic supplementary material The online version of this article (doi:10.1186/s12993-015-0082-8) contains supplementary material, which is available to authorized users.

Highlights • Children with or without ADHD have moderate/high R-fMRI test–retest reliability. • Reliability is greater in controls than ADHD across most R-fMRI metrics. • Regional differences in ICC related to diagnostic groups reflect underlying pathophysiology for ADHD affecting both inter and intra subject variability., To date, only one study has examined test–retest reliability of resting state fMRI (R-fMRI) in children, none in clinical developing groups. Here, we assessed short-term test–retest reliability in a sample of 46 children (11–17.9 years) with attention-deficit/hyperactivity disorder (ADHD) and 57 typically developing children (TDC). Our primary test–retest reliability measure was the intraclass correlation coefficient (ICC), quantified for a range of R-fMRI metrics. We aimed to (1) survey reliability within and across diagnostic groups, and (2) compare voxel-wise ICC between groups. We found moderate-to-high ICC across all children and within groups, with higher-order functional networks showing greater ICC. Nearly all R-fMRI metrics exhibited significantly higher ICC in TDC than in children with ADHD for one or more regions. In particular, posterior cingulate and ventral precuneus exhibited group differences in ICC across multiple measures. In the context of overall moderate-to-high test–retest reliability in children, regional differences in ICC related to diagnostic groups likely reflect the underlying pathophysiology for ADHD. Our currently limited understanding of the factors contributing to inter- and intra-subject variability in ADHD underscores the need for large initiatives aimed at examining their impact on test–retest reliability in both clinical and developing populations.

Additional file 1. Correlations between VTA-NACC connectivity and IGD severity/subjective craving within each group, and the whole-brain between-group differences in resting state functional connectivity of VTA/NACC.

Alzheimer's disease (AD) is associated not only with regional gray matter damages, but also with abnormalities in functional integration between brain regions. Here, we employed resting-state functional magnetic resonance imaging data and voxel-based graph-theory analysis to systematically investigate intrinsic functional connectivity patterns of whole-brain networks in 32 AD patients and 38 healthy controls (HCs). We found that AD selectively targeted highly connected hub regions (in terms of nodal functional connectivity strength) of brain networks, involving the medial and lateral prefrontal and parietal cortices, insula, and thalamus. This impairment was connectivity distance-dependent (Euclidean), with the most prominent disruptions appearing in the long-range connections (e.g., 100-130 mm). Moreover, AD also disrupted functional connections within the default-mode, salience and executive-control modules, and connections between the salience and executive-control modules. These disruptions of hub connectivity and modular integrity significantly correlated with the patients' cognitive performance. Finally, the nodal connectivity strength in the posteromedial cortex exhibited a highly discriminative power in distinguishing individuals with AD from HCs. Taken together, our results emphasize AD-related degeneration of specific brain hubs, thus providing novel insights into the pathophysiological mechanisms of connectivity dysfunction in AD and suggesting the potential of using network hub connectivity as a diagnostic biomarker.

Perspectives of human brain functional connectivity continue to evolve. Static representations of functional interactions between brain regions are rapidly giving way to dynamic perspectives, which emphasize non-random temporal variations in intrinsic functional connectivity (iFC) patterns. Here, we bring this dynamic perspective to our understanding of iFC patterns for posteromedial cortex (PMC), a cortical hub known for its functional diversity. Previous work has consistently differentiated iFC patterns among PMC subregions, though assumed static iFC over time. Here, we assessed iFC as a function of time utilizing a sliding-window correlation approach, and applied hierarchical clustering to detect representative iFC states from the windowed iFC. Across subregions, five iFC states were detected over time. Although with differing frequencies, each subregion was associated with each of the states, suggesting that these iFC states are “common” to PMC subregions. Importantly, each subregion possessed a unique preferred state(s) and distinct transition patterns, explaining previously observed iFC differentiations. These results resonate with task-based fMRI studies suggesting that large-scale functional networks can be flexibly reconfigured in response to changing task-demands. Additionally, we used retest scans (~ 1 week later) to demonstrate the reproducibility of the iFC states identified, and establish moderate to high test–retest reliability for various metrics used to quantify switching behaviors. We also demonstrate the ability of dynamic properties in the visual PMC subregion to index inter-individual differences in a measure of concept formation and mental flexibility. These findings suggest functional relevance of dynamic iFC and its potential utility in biomarker identification over time, as d-iFC methodologies are refined and mature.

Caregiver maltreatment induces vulnerability to later-life psychopathology. Clinical and preclinical evidence suggest changes in prefrontal and limbic circuitry underlie this susceptibility. We examined this question using a rat model of maternal maltreatment and methods translated from humans, resting-state functional magnetic resonance imaging (R-fMRI). Rat pups were reared by mothers provided with insufficient or abundant bedding for nest building from postnatal (PN) days 8 to 12 and underwent behavioral assessments of affect-related behaviors (forced swim, sucrose preference and social interaction) in adolescence (PN45) and early adulthood (PN60). R-fMRI sessions were conducted under light anesthesia at both ages. Offspring reared with insufficient bedding (that is, maltreated) displayed enduring negative affective behaviors. Amygdala-prefrontal cortex (PFC) functional connectivity increased significantly from adolescence to adulthood in controls, but not in maltreated animals. We computed the fractional amplitude of low-frequency fluctuations (fALFF), an index of intrinsic brain activity, and found that fALFF in medial prefrontal cortex and anterior cingulate cortex (MPFC/ACC) increased significantly with age in controls but remained unchanged in maltreated animals during adolescence and adulthood. We used a seed-based analysis to explore changes in functional connectivity between this region and the whole brain. Compared with controls, maltreated animals demonstrated reduced functional connectivity between MPFC/ACC and left caudate/putamen across both ages. Functional connectivity between MPFC/ACC and right caudate/putamen showed a group by age interaction: decreased in controls but increased in maltreated animals. These data suggest that maltreatment induces vulnerability to psychopathology and is associated with differential developmental trajectories of prefrontal and subcortical circuits underlying affect regulation.

Graph theoretical explorations of functional interactions within the human connectome, are rapidly advancing our understanding of brain architecture. In particular, global and regional topological parameters are increasingly being employed to quantify and characterize inter-individual differences in human brain function. Head motion remains a significant concern in the accurate determination of resting-state fMRI based assessments of the connectome, including those based on graph theoretical analysis (e.g., motion can increase local efficiency, while decreasing global efficiency and small-worldness). This study provides a comprehensive examination of motion correction strategies on the relationship between motion and commonly used topological parameters. At the individual-level, we evaluated different models of head motion regression and scrubbing, as well as the potential benefits of using partial correlation (estimated via graphical lasso) instead of full correlation. At the group-level, we investigated the utility of regression of motion and mean intrinsic functional connectivity before topological parameters calculation and/or after. Consistent with prior findings, none of the explicit motion-correction approaches at individual-level were able to remove motion relationships for topological parameters. Global signal regression (GSR) emerged as an effective means of mitigating relationships between motion and topological parameters; though at the risk of altering the connectivity structure and topological hub distributions when higher densities graphs are employed (e.g., > 6%). Group-level analysis correction for motion was once again found to be a crucial step. Finally, similar to recent work, we found a constellation of findings suggestive of the possibility that some of the motion-relationships detected may reflect neural or trait signatures of motion, rather than simply motion-induced artifact.

The National Institute of Mental Health strategic plan for advancing psychiatric neuroscience calls for an acceleration of discovery and the delineation of developmental trajectories for risk and resilience across the lifespan. To attain these objectives, sufficiently powered datasets with broad and deep phenotypic characterization, state-of-the-art neuroimaging, and genetic samples must be generated and made openly available to the scientific community. The enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) is a response to this need. NKI-RS is an ongoing, institutionally centered endeavor aimed at creating a large-scale (N > 1000), deeply phenotyped, community-ascertained, lifespan sample (ages 6–85 years old) with advanced neuroimaging and genetics. These data will be publically shared, openly, and prospectively (i.e., on a weekly basis). Herein, we describe the conceptual basis of the NKI-RS, including study design, sampling considerations, and steps to synchronize phenotypic and neuroimaging assessment. Additionally, we describe our process for sharing the data with the scientific community while protecting participant confidentiality, maintaining an adequate database, and certifying data integrity. The pilot phase of the NKI-RS, including challenges in recruiting, characterizing, imaging, and sharing data, is discussed while also explaining how this experience informed the final design of the enhanced NKI-RS. It is our hope that familiarity with the conceptual underpinnings of the enhanced NKI-RS will facilitate harmonization with future data collection efforts aimed at advancing psychiatric neuroscience and nosology.

Behavioral studies have suggested a low-frequency (0.05 Hz) fluctuation of sustained attention on the basis of the intra-individual variability of reaction-time. Conventional task designs for functional magnetic resonance imaging (fMRI) studies are not appropriate for frequency analysis. The present study aimed to propose a new paradigm, real-time finger force feedback (RT-FFF), to study the brain mechanisms of sustained attention and neurofeedback.We compared the low-frequency fluctuations in both behavioral and fMRI data from 38 healthy adults (19 males; mean age, 22.3 years). Two fMRI sessions, in RT-FFF and sham finger force feedback (S-FFF) states, were acquired (TR 2 s, Siemens Trio 3-Tesla scanner, 8 min each, counter-balanced). Behavioral data of finger force were obtained simultaneously at a sampling rate of 250 Hz.Frequency analysis of the behavioral data showed lower amplitude in the low-frequency band (0.004-0.104 Hz) but higher amplitude in the high-frequency band (27.02-125 Hz) in the RT-FFF than the S-FFF states. The mean finger force was not significantly different between the two states. fMRI data analysis showed higher fractional amplitude of low-frequency fluctuation (fALFF) in the S-FFF than in the RT-FFF state in the visual cortex, but higher fALFF in RT-FFF than S-FFF in the middle frontal gyrus, the superior frontal gyrus, and the default mode network.The behavioral results suggest that the proposed paradigm may provide a new approach to studies of sustained attention. The fMRI results suggest that a distributed network including visual, motor, attentional, and default mode networks may be involved in sustained attention and/or real-time feedback. This paradigm may be helpful for future studies on deficits of attention, such as attention deficit hyperactivity disorder and mild traumatic brain injury.

Spontaneous brain activity or off-line activity after memory encoding is associated with memory consolidation. A few recent resting-state functional magnetic resonance imaging (RS-fMRI) studies indicate that the RS-fMRI could map off-line memory consolidation effects. However, the gene effects on memory consolidation process remain largely unknown. Here we collected two RS-fMRI sessions, one before and another after an episodic memory encoding task, from two groups of healthy young adults, one with apolipoprotein E (APOE) e2/e3 and the other with APOE e3/e4. The ratio of regional homogeneity (ReHo), a measure of local synchronization of spontaneous RS-fMRI signal, of the two sessions was used as an index of memory-consolidation. APOE e3/e4 group showed greater ReHo ratio within the medial temporal lobe (MTL). The ReHo ratio in MTL was significantly correlated with the recognition memory performance in the APOE e3/e4 group but not in e2/e3 group. Additionally, APOE e3/e4 group showed lower ReHo ratio in the occipital and parietal picture-encoding areas. Our results indicate that APOE e3/e4 group may have a different off-line memory consolidation process compared to e2/e3 group. These results may help generate future hypotheses that the off-line memory consolidation might be impaired in Alzheimer’s disease.

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri.net.

Increasing attention has recently been directed to the applications of pattern recognition and brain imaging techniques in the effective and accurate diagnosis of Alzheimer's disease (AD). However, most of the existing research focuses on the use of single-modal (e.g., structural or functional MRI) or single-level (e.g., brain local or connectivity metrics) biomarkers for the diagnosis of AD. In this study, we propose a methodological framework, called multi-modal imaging and multi-level characteristics with multi-classifier (M3), to discriminate patients with AD from healthy controls. This approach involved data analysis from two imaging modalities: structural MRI, which was used to measure regional gray matter volume, and resting-state functional MRI, which was used to measure three different levels of functional characteristics, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and regional functional connectivity strength (RFCS). For each metric, we computed the values of ninety regions of interest derived from a prior atlas, which were then further trained using a multi-classifier based on four maximum uncertainty linear discriminant analysis base classifiers. The performance of this method was evaluated using leave-one-out cross-validation. Applying the M3 approach to the dataset containing 16 AD patients and 22 healthy controls led to a classification accuracy of 89.47% with a sensitivity of 87.50% and a specificity of 90.91%. Further analysis revealed that the most discriminative features for classification are predominantly involved in several default-mode (medial frontal gyrus, posterior cingulate gyrus, hippocampus and parahippocampal gyrus), occipital (fusiform gyrus, inferior and middle occipital gyrus) and subcortical (amygdale and pallidum of lenticular nucleus) regions. Thus, the M3 method shows promising classification performance by incorporating information from different imaging modalities and different functional properties, and it has the potential to improve the clinical diagnosis and treatment evaluation of AD.

Spontaneous low-frequency fluctuations (LFF) in the blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal have been shown to reflect cerebral spontaneous neural activity. The objective of this study was to explore brain functional changes in patients with mild cognitive impairment (MCI) by measuring the amplitude of the BOLD signals. Eighteen amnestic MCI patients and 20 healthy elderly individuals underwent the fMRI scan. The amplitude of LFF (ALFF) was calculated using REST software. MCI patients showed decreased ALFF in the right hippocampus and parahippocampal cortex, left lateral temporal cortex and right ventral medial prefrontal cortex and increased ALFF in the left temporal-parietal joint (TPJ) and inferior parietal lobule. The ALFF value in the right hippocampus and parahippocampal cortex was positively correlated with the scores of Mini-Mental State Exam. Reduced medial temporal lobe activity may implicate the underlying memory impairment mechanisms in MCI. Increased TPJ and inferior parietal lobule activity may indicate the compensatory mechanism in MCI patients. These findings suggest that ALFF analysis could provide a useful tool in the fMRI study of MCI.

Intrinsic brain activity in a resting state incorporates components of the task negative network called default mode network (DMN) and task-positive networks called attentional networks. In the present study, the reciprocal neuronal networks in the elder group were compared with the young group to investigate the differences of the intrinsic brain activity using a method of temporal correlation analysis based on seed regions of posterior cingulate cortex (PCC) and ventromedial prefrontal cortex (vmPFC). We found significant decreased positive correlations and negative correlations with the seeds of PCC and vmPFC in the old group. The decreased coactivations in the DMN network components and their negative networks in the old group may reflect age-related alterations in various brain functions such as attention, motor control and inhibition modulation in cognitive processing. These alterations in the resting state anti-correlative networks could provide neuronal substrates for the aging brain.

We employed resting-state functional MRI (R-fMRI) to investigate hemisphere- and gender-related differences in the topological organization of human brain functional networks. Brain networks were first constructed by measuring inter-regional temporal correlations of R-fMRI data within each hemisphere in 86 young, healthy, right-handed adults (38 males and 48 females) followed by a graph-theory analysis. The hemispheric networks exhibit small-world attributes (high clustering and short paths) that are compatible with previous results in the whole-brain functional networks. Furthermore, we found that compared with females, males have a higher normalized clustering coefficient in the right hemispheric network but a lower clustering coefficient in the left hemispheric network, suggesting a gender-hemisphere interaction. Moreover, we observed significant hemisphere-related differences in the regional nodal characteristics in various brain regions, such as the frontal and occipital regions (leftward asymmetry) and the temporal regions (rightward asymmetry), findings that are consistent with previous studies of brain structural and functional asymmetries. Together, our results suggest that the topological organization of human brain functional networks is associated with gender and hemispheres, and they provide insights into the understanding of functional substrates underlying individual differences in behaviors and cognition.

In this study, we applied coherence to voxel-wise measurement of regional homogeneity of resting-state functional magnetic resonance imaging (RS-fMRI) signal. We compared the current method, regional homogeneity based on coherence (Cohe-ReHo), with previously proposed method, ReHo based on Kendall’s coefficient of concordance (KCC-ReHo), in terms of correlation and paired t-test in a large sample of healthy participants. We found the two measurements differed mainly in some brain regions where physiological noise is dominant. We also compared the sensitivity of these methods in detecting difference between resting-state conditions (eyes open (EO) vs. eyes closed (EC)) and in detecting abnormal local synchronization between two groups (attention deficit hyperactivity disorder (ADHD) patients vs. normal controls). Our results indicated that Cohe-ReHo is more sensitive than KCC-ReHo to the difference between two conditions (EO vs. EC) as well as that between ADHD and normal controls. These preliminary results suggest that Cohe-ReHo is superior to KCC-ReHo. A possible reason is that coherence is not susceptible to random noise induced by phase delay among the timecourses to be measured. However, further investigation is still needed to elucidate the sensitivity and specificity of these methods.

The characterization of topological architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (

Background Recent functional MRI (fMRI) studies have demonstrated that there is an intrinsically organized default mode network (DMN) in the resting brain, primarily made up of the posterior cingulate cortex (PCC) and the medial prefrontal cortex (MPFC). Several previous studies have found that the DMN is minimally disturbed during different resting-state conditions with limited cognitive demand. However, this conclusion was drawn from the visual inspection of the functional connectivity patterns within the DMN and no statistical comparison was performed. Methodology/Principal Findings Four resting-state fMRI sessions were acquired: 1) eyes-closed (EC) (used to generate the DMN mask); 2) EC; 3) eyes-open with no fixation (EO); and 4) eyes-open with a fixation (EO-F). The 2–4 sessions were counterbalanced across participants (n = 20, 10 males). We examined the statistical differences in both functional connectivity and regional amplitude of low frequency fluctuation (ALFF) within the DMN among the 2–4 resting-state conditions (i.e., EC, EO, and EO-F). Although the connectivity patterns of the DMN were visually similar across these three different conditions, we observed significantly higher functional connectivity and ALFF in both the EO and the EO-F conditions as compared to the EC condition. In addition, the first and second resting EC conditions showed significant differences within the DMN, suggesting an order effect on the DMN activity. Conclusions/Significance Our findings of the higher DMN connectivity and regional spontaneous activities in the resting state with the eyes open suggest that the participants might have more non-specific or non-goal-directed visual information gathering and evaluation, and mind wandering or daydreaming during the resting state with the eyes open as compared to that with the eyes closed, thus providing insights into the understanding of unconstrained mental activity within the DMN. Our results also suggest that it should be cautious when choosing the type of a resting condition and designating the order of the resting condition in multiple scanning sessions in experimental design.

The thalamus and visual cortex are two key components associated with the alpha power of electroencephalography. However, their functional relationship remains to be elucidated. Here, we employ resting-state functional MRI to investigate the temporal correlations of spontaneous fluctuations between the thalamus [the whole thalamus and its three largest nuclei (bilateral mediodorsal, ventrolateral and pulvinar nuclei)] and visual cortex under both eyes open and eyes closed conditions. The whole thalamus show negative correlations with the visual cortex and positive correlations with its contralateral counterpart in eyes closed condition, but which are significantly decreased in eyes open condition, consistent with previous findings of electroencephalography desynchronization during eyes open resting state. Furthermore, we find that bilateral thalamic mediodorsal nuclei and bilateral ventrolateral nuclei have remarkably similar connectivity maps, and resemble to those of the whole thalamus, suggesting their crucial contributions to the thalamus-visual correlations. The bilateral pulvinar nuclei are found to show distinct functional connectivity patterns, compatible with previous findings of the asymmetry of anatomical and functional organization in the nuclei. Our data provides evidence for the associations of intrinsic spontaneous neuronal activity between the thalamus and visual cortex under different resting conditions, which might have implications on the understanding of the generation and modulation of the alpha rhythm.

In this study, a resting-state fMRI based classifier, for the first time, was proposed and applied to discriminate children with attention-deficit/hyperactivity disorder (ADHD) from normal controls. On the basis of regional homogeneity (ReHo), a mapping of brain function at resting state, PCA-based Fisher discriminative analysis (PC-FDA) was trained to build a linear classifier. Permutation test was then conducted to identify the brain areas with the most significant contribution to the final discrimination. Experimental results showed a correct classification rate of 85% using a leave-one-out cross-validation. Moreover, some highly discriminative brain regions, like the prefrontal cortex and anterior cingulate cortex, well confirmed the previous findings on ADHD. Interestingly, some important but less reported regions such as the thalamus were also identified. We conclude that the classifier, using resting-state brain function as classification feature, has potential ability to improve current diagnosis and treatment evaluation of ADHD.

BACKGROUND: Prior research has shown that the amygdala of carriers of the short allele (s) of the serotonin transporter (5-HTT) gene (5-HTTLPR) have a larger response to negative emotional stimuli and higher spontaneous activity during the resting state than non-carriers. However, recent studies have suggested that the effects of 5-HTTLPR may be specific to different ethnic groups. Few studies have been conducted to address this issue. METHODOLOGY/PRINCIPAL FINDINGS: Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) was conducted on thirty-eight healthy Han Chinese subjects (l/l group, n = 19; s/s group, n = 19) during the resting state and during an emotional processing task. Compared with the s/s group, the l/l group showed significantly increased regional homogeneity or local synchronization in the right amygdala during the resting state (|t|>2.028, p

Graph theoretical analysis of brain networks based on resting-state functional MRI (R-fMRI) has attracted a great deal of attention in recent years. These analyses often involve the selection of correlation metrics and specific preprocessing steps. However, the influence of these factors on the topological properties of functional brain networks has not been systematically examined. Here, we investigated the influences of correlation metric choice (Pearson's correlation versus partial correlation), global signal presence (regressed or not) and frequency band selection [slow-5 (0.01-0.027 Hz) versus slow-4 (0.027-0.073 Hz)] on the topological properties of both binary and weighted brain networks derived from them, and we employed test-retest (TRT) analyses for further guidance on how to choose the "best" network modeling strategy from the reliability perspective. Our results show significant differences in global network metrics associated with both correlation metrics and global signals. Analysis of nodal degree revealed differing hub distributions for brain networks derived from Pearson's correlation versus partial correlation. TRT analysis revealed that the reliability of both global and local topological properties are modulated by correlation metrics and the global signal, with the highest reliability observed for Pearson's-correlation-based brain networks without global signal removal (WOGR-PEAR). The nodal reliability exhibited a spatially heterogeneous distribution wherein regions in association and limbic/paralimbic cortices showed moderate TRT reliability in Pearson's-correlation-based brain networks. Moreover, we found that there were significant frequency-related differences in topological properties of WOGR-PEAR networks, and brain networks derived in the 0.027-0.073 Hz band exhibited greater reliability than those in the 0.01-0.027 Hz band. Taken together, our results provide direct evidence regarding the influences of correlation metrics and specific preprocessing choices on both the global and nodal topological properties of functional brain networks. This study also has important implications for how to choose reliable analytical schemes in brain network studies.

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.