Cognitive and default‐mode resting state networks: Do male and female brains “rest” differently?

Variability in human behavior related to sex is supported by neuroimaging studies showing differences in brain activation patterns during cognitive task performance. An emerging field is examining the human connectome, including networks of brain regions that are not only temporally‐correlated during different task conditions, but also networks that show highly correlated spontaneous activity during a task‐free state. Both task‐related and task‐free network activity has been associated with individual task performance and behavior under certain conditions. Therefore, our aim was to determine whether sex differences exist during a task‐free resting state for two networks associated with cognitive task performance (executive control network (ECN), salience network (SN)) and the default mode network (DMN). Forty‐nine healthy subjects (26 females, 23 males) underwent a 5‐min task‐free fMRI scan in a 3T MRI. An independent components analysis (ICA) was performed to identify the best‐fit IC for each network based on specific spatial nodes defined in previous studies. To determine the consistency of these networks across subjects we performed self‐organizing group‐level ICA analyses. There were no significant differences between sexes in the functional connectivity of the brain areas within the ECN, SN, or the DMN. These important findings highlight the robustness of intrinsic connectivity of these resting state networks and their similarity between sexes. Furthermore, our findings suggest that resting state fMRI studies do not need to be controlled for sex. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Functional connectivity‐based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI

This study aimed to identify subunits of the basal ganglia and thalamus and to investigate the functional connectivity among these anatomically segregated subdivisions and the cerebral cortex in healthy subjects. For this purpose, we introduced multilevel independent component analysis (ICA) of the resting‐state functional magnetic resonance imaging (fMRI). After applying ICA to the whole brain gray matter, we applied second‐level ICA restrictively to the basal ganglia and the thalamus area to identify discrete functional subunits of those regions. As a result, the basal ganglia and the thalamus were parcelled into 31 functional subdivisions according to their temporal activity patterns. The extracted parcels showed functional network connectivity between hemispheres, between subdivisions of the basal ganglia and thalamus, and between the extracted subdivisions and cerebral functional components. Grossly, these findings correspond to cortico‐striato‐thalamo‐cortical circuits in the brain. This study also showed the utility of multilevel ICA of resting state fMRI in brain network research. Hum Brain Mapp, 2013. © 2012 Wiley Perodicals, Inc.     

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.     

Significance of longitudinal changes in the default‐mode network for cognitive recovery after stroke

Although a considerable number of patients suffer from cognitive impairments after stroke, the neural mechanism of cognitive recovery has not yet been clarified. Repeated resting‐state functional magnetic resonance imaging (fMRI) was used in this study to examine longitudinal changes in the default‐mode network (DMN) during the 6 months after stroke, and to investigate the relationship between DMN changes and cognitive recovery. Out of 24 initially recruited right‐hemispheric stroke patients, 11 (eight males, mean age 55.7 years) successfully completed the repeated fMRI protocol. Patients underwent three fMRI sessions at 1, 3 and 6 months after stroke. Their DMNs were analysed and compared with those of 11 age‐matched healthy subjects (nine males, mean age 56.2 years). Correlations between DMN connectivity and improvement of the cognitive performance scores were also assessed. The stroke patients were found to demonstrate markedly decreased DMN connectivity of the posterior cingulate cortex, precuneus, medial frontal gyrus and inferior parietal lobes at 1 month after stroke. At 3 months after stroke, the DMN connectivity of these brain areas was almost restored, suggesting that the period is critical for neural reorganization. The DMN connectivity of the dorsolateral prefrontal cortex in the contralesional hemisphere showed a significant correlation with cognitive function recovery in stroke patients, and should be considered a compensatory process for overcoming cognitive impairment due to brain lesion. This is the first longitudinal study to demonstrate the changes in DMN during recovery after stroke and the key regions influencing cognitive recovery.     

Dynamic functioning of transient resting‐state coactivation networks in the Human Connectome Project

Resting‐state analyses evaluating large‐scale brain networks have largely focused on static correlations in brain activity over extended time periods, however emerging approaches capture time‐varying or dynamic patterns of transient functional networks. In light of these new approaches, there is a need to classify common transient network states (TNS) in terms of their spatial and dynamic properties. To fill this gap, two independent resting state scans collected in 462 healthy adults from the Human Connectome Project were evaluated using coactivation pattern analysis to identify (eight) TNS that recurred across participants and over time. These TNS spatially overlapped with prototypical resting state networks, but also diverged in notable ways. In particular, analyses revealed three TNS that shared cortical midline overlap with the default mode network (DMN), but these “complex” DMN states also encompassed distinct regions that fall beyond the prototypical DMN, suggesting that the DMN defined using static methods may represent the average of distinct complex‐DMN states. Of note, dwell time was higher in “complex” DMN states, challenging the idea that the prototypical DMN, as a single unit, is the dominant resting‐state network as typically defined by static resting state methods. In comparing the two resting state scans, we also found high reliability in the spatial organization and dynamic activities of network states involving DMN or sensorimotor regions. Future work will determine whether these TNS defined by coactivation patterns are in other samples, and are linked to fundamental cognitive properties.     

The corticotopic organization of the human basal forebrain as revealed by regionally selective functional connectivity profiles

The cholinergic basal forebrain (CBF), comprising different groups of cortically projecting cholinergic neurons, plays a crucial role in higher cognitive processes and has been implicated in diverse neuropsychiatric disorders. A distinct corticotopic organization of CBF projections has been revealed in animal studies, but little is known about their organization in the human brain. We explored regional differences in functional connectivity (FC) profiles within the human CBF by applying a clustering approach to resting‐state functional magnetic resonance imaging (rs‐fMRI) data of healthy adult individuals (N = 85; 19–85 years). We further examined effects of age on FC of the identified CBF clusters and assessed the reproducibility of cluster‐specific FC profiles in independent data from healthy older individuals (N = 25; 65–89 years). Results showed that the human CBF is functionally organized into distinct anterior‐medial and posterior‐lateral subdivisions that largely follow anatomically defined boundaries of the medial septum/diagonal band and nucleus basalis Meynert. The anterior‐medial CBF subdivision was characterized by connectivity with the hippocampus and interconnected nodes of an extended medial cortical memory network, whereas the posterior‐lateral subdivision was specifically connected to anterior insula and dorsal anterior cingulate components of a salience/attention network. FC of both CBF subdivisions declined with increasing age, but the overall topography of subregion‐specific FC profiles was reproduced in independent rs‐fMRI data of healthy older individuals acquired in a typical clinical setting. Rs‐fMRI‐based assessments of subregion‐specific CBF function may complement established volumetric approaches for the in vivo study of CBF involvement in neuropsychiatric disorders.     

Recognition memory is associated with altered resting‐state functional connectivity in people at genetic risk for Alzheimer's disease

The apolipoprotein E ε4 (ApoE ε4) allele not only represents the strongest single genetic risk factor for sporadic Alzheimer's disease, but also imposes independent effects on brain function in healthy individuals where it has been shown to promote subtle memory deficits and altered intrinsic functional brain network connectivity. Based on previous work showing a potential relevance of the default mode network (DMN) functional connectivity for episodic memory function, we hypothesized that the ApoE ε4 genotype would affect memory performance via modulation of the DMN. We assessed 63 healthy individuals (50–80 years old), of which 20 carried the ε4 allele. All participants underwent resting‐state functional magnetic resonance imaging (fMRI), high‐resolution 3D anatomical MRI imaging and neuropsychological assessment. Functional connectivity analysis of resting‐state activity was performed with a predefined seed region located in the left posterior cingulate cortex (PCC), a core region of the DMN. ApoE ε4 carriers performed significantly poorer than non‐carriers in wordlist recognition and cued recall. Furthermore, ε4 carriers showed increased connectivity relative to ε4 non‐carriers between the PCC seed region and left‐hemispheric middle temporal gyrus (MTG). There was a positive correlation between recognition memory scores and resting‐state connectivity in the left MTG in ε4 carriers. These results can be interpreted as compensatory mechanisms strengthening the cross‐links between DMN core areas and cortical areas involved in memory processing.     

Working memory‐related changes in functional connectivity persist beyond task disengagement

We examined whether altered connectivity in functional networks during working memory performance persists following conclusion of that performance, into a subsequent resting state. We conducted functional magnetic resonance imaging (fMRI) in 50 young adults during an initial resting state, followed by an N‐back working memory task and a subsequent resting state, in order to examine changes in functional connectivity within and between the default‐mode network (DMN) and the task‐positive network (TPN) across the three states. We found that alterations in connectivity observed during the N‐back task persisted into the subsequent resting state within the TPN and between the DMN and TPN, but not within the DMN. Further, both speed of working memory performance and TPN connectivity strength during the N‐back task predicted connectivity strength in the subsequent resting state. Finally, DMN connectivity measured before and during the N‐back task predicted individual differences in self‐reported inattentiveness, but this association was not found during the post‐task resting state. Together, these findings have important implications for models of how the brain recovers following effortful cognition, as well as for experimental designs using resting and task scans. Hum Brain Mapp 35:1004–1017, 2014. © 2012 Wiley Periodicals, Inc.     

Neuronal oscillations and functional interactions between resting state networks

Functional magnetic imaging (fMRI) studies showed that resting state activity in the healthy brain is organized into multiple large‐scale networks encompassing distant regions. A key finding of resting state fMRI studies is the anti‐correlation typically observed between the dorsal attention network (DAN) and the default mode network (DMN), which—during task performance—are activated and deactivated, respectively. Previous studies have suggested that alcohol administration modulates the balance of activation/deactivation in brain networks, as well as it induces significant changes in oscillatory activity measured by electroencephalography (EEG). However, our knowledge of alcohol‐induced changes in band‐limited EEG power and their potential link with the functional interactions between DAN and DMN is still very limited. Here we address this issue, examining the neuronal effects of alcohol administration during resting state by using simultaneous EEG‐fMRI. Our findings show increased EEG power in the theta frequency band (4–8 Hz) after administration of alcohol compared to placebo, which was prominent over the frontal cortex. More interestingly, increased frontal tonic EEG activity in this band was associated with greater anti‐correlation between the DAN and the frontal component of the DMN. Furthermore, EEG theta power and DAN‐DMN anti‐correlation were relatively greater in subjects who reported a feeling of euphoria after alcohol administration, which may result from a diminished inhibition exerted by the prefrontal cortex. Overall, our findings suggest that slow brain rhythms are responsible for dynamic functional interactions between brain networks. They also confirm the applicability and potential usefulness of EEG‐fMRI for central nervous system drug research. Hum Brain Mapp 35:3517–3528, 2014. © 2013 Wiley Periodicals, Inc .     

Increased “default mode” activity in adolescents prenatally exposed to cocaine

Prenatal cocaine exposure (PCE) is associated with attention/arousal dysregulation and possible inefficiencies in some cognitive functions. However, the neurobiological bases of these teratogenic effects have not been well characterized. Because activities in the default mode network (DMN) reflect intrinsic brain functions that are closely associated with arousal regulation and cognition, alterations in the DMN could underlie cognitive effects related to PCE. With resting‐state and task activation functional magnetic resonance imaging (fMRI), this study investigated the possible PCE related changes in functional brain connectivity and brain activation in the DMN. In the resting state, the PCE group was found to have stronger functional connectivity in the DMN, as compared to the nonexposed controls. During a working memory task with emotional distracters, the PCE group exhibited less deactivation in the DMN and their fMRI signal was more increased by emotional arousal. These data revealed additional neural effects related to PCE, and consistent with previous findings, indicate that PCE may affect behavior and functioning by increasing baseline arousal and altering the excitatory/inhibitory balancing mechanisms involved in cognitive resource allocation. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.     

The effects of methylphenidate on whole brain intrinsic functional connectivity

Methylphenidate (MPH) is an indirect dopaminergic and noradrenergic agonist that is used to treat attention deficit hyperactivity disorder and that has shown therapeutic potential in neuropsychiatric diseases such as depression, dementia, and Parkinson's disease. While effects of MPH on task‐induced brain activation have been investigated, little is known about how MPH influences the resting brain. To investigate the effects of 40 mg of oral MPH on intrinsic functional connectivity, we used resting state fMRI in 54 healthy male subjects in a double‐blind, randomized, placebo‐controlled study. Functional connectivity analysis employing ICA revealed seven resting state networks (RSN) of interest. Connectivity strength between the dorsal attention network and the thalamus was increased after MPH intake. Other RSN located in association cortex areas, such as the left and right frontoparietal networks and the executive control network, showed MPH‐induced connectivity increase to sensory‐motor and visual cortex regions and connectivity decrease to cortical and subcortical components of cortico‐striato‐thalamo‐cortical circuits (CST). RSN located in sensory‐motor cortex areas showed the opposite pattern with MPH‐induced connectivity increase to CST components and connectivity decrease to sensory‐motor and visual cortex regions. Our results provide evidence that MPH does not only alter intrinsic connectivity between brain areas involved in sustained attention, but that it also induces significant changes in the cortico‐cortical and cortico‐subcortical connectivity of many other cognitive and sensory‐motor RSN. Hum Brain Mapp 35:5379–5388, 2014. © 2014 Wiley Periodicals, Inc.     

Partner-matching for the automated identification of reproducible ICA components from fMRI datasets: algorithm and validation

The analysis of functional magnetic resonance imaging (fMRI) data is complicated by the presence of a mixture of many sources of signal and noise. Independent component analysis (ICA) can separate these mixtures into independent components, each of which contains maximal information from a single, independent source of signal, whether from noise or from a discrete physiological or neural system. ICA typically generates a large number of components for each subject imaged, however, and therefore it generates a vast number of components across all of the subjects imaged in an fMRI dataset. The practical implementation of ICA has been limited by the difficulty in discerning which of these many components are spurious and which are reproducible, either within or across individuals of the dataset. We have developed a novel clustering algorithm, termed "Partner-Matching" (PM), which identifies automatically the independent components that are reproducible either within or between subjects. It identifies those components by clustering them according to robust measures of similarity in their spatial configurations either across different subjects of an fMRI dataset, within a single subject scanned across multiple scanning sessions, or within an individual subject scanned across multiple runs within a single scanning session. We demonstrate the face validity of our algorithm by applying it to the analysis of three fMRI datasets acquired in 13 healthy adults performing simple auditory, motor, and visual tasks. From among 50 independent components generated for each subject, our PM algorithm automatically identified, across all 13 subjects, components representing activity within auditory, motor, and visual cortices, respectively, as well as numerous other reproducible components outside of primary sensory and motor cortices, in functionally connected circuits that subserve higher-order cognitive functions, even in these simple tasks.     

Resting state default‐mode network connectivity in early depression using a seed region‐of‐interest analysis: Decreased connectivity with caudate nucleus

Aim: Reports on resting brain activity in healthy controls have described a default‐mode network (DMN) and important differences in DMN connectivity have emerged for several psychiatric conditions. No study to date, however, has investigated resting‐state DMN in relatively early depression before years of medication treatment. The objective of the present study was, therefore, to investigate the DMN in patients seeking help from specialized mental health services for the first time for symptoms of depression. Methods: Fourteen depressed subjects and 15 matched controls were scanned using 4‐T functional magnetic resonance imaging while resting with eyes closed. All but one subject was medication free. A precuneus/posterior cingulate cortex (P/PCC) seed‐region connectivity analysis was used to identify the DMN and compare study groups in regions of relevance to depression. Results: The P/PCC analysis identified the DMN well in both study groups, consistent with prior literature. Direct comparison showed significantly reduced correlation between the P/PCC and the bilateral caudate in depression compared with controls and no areas of increased connectivity in the depressed group. Conclusions: The present study is the first to investigate resting‐state DMN in the early stages of treatment‐seeking for depression. Depressed subjects had decreased connectivity between the P/PCC and the bilateral caudate, regions known to be involved in motivation and reward processing. Deficits in DMN connectivity with the caudate may be an early manifestation of major depressive disorder.     

Dysregulation of working memory and default‐mode networks in schizophrenia using independent component analysis,an fBIRN and MCIC study

Deficits in working memory (WM) are a consistent neurocognitive marker for schizophrenia. Previous studies have suggested that WM is the product of coordinated activity in distributed functionally connected brain regions. Independent component analysis (ICA) is a data‐driven approach that can identify temporally coherent networks that underlie fMRI activity. We applied ICA to an fMRI dataset for 115 patients with chronic schizophrenia and 130 healthy controls by performing the Sternberg Item Recognition Paradigm. Here, we describe the first results using ICA to identify differences in the function of WM networks in schizophrenia compared to controls. ICA revealed six networks that showed significant differences between patients with schizophrenia and healthy controls. Four of these networks were negatively task‐correlated and showed deactivation across the posterior cingulate, precuneus, medial prefrontal cortex, anterior cingulate, inferior parietal lobules, and parahippocampus. These networks comprise brain regions known as the default‐mode network (DMN), a well‐characterized set of regions shown to be active during internal modes of cognition and implicated in schizophrenia. Two networks were positively task‐correlated, with one network engaging WM regions such as bilateral DLPFC and inferior parietal lobules while the other network engaged primarily the cerebellum. Our results suggest that DLPFC dysfunction in schizophrenia might be lateralized to the left and intrinsically tied to other regions such as the inferior parietal lobule and cingulate gyrus. Furthermore, we found that DMN dysfunction in schizophrenia exists across multiple subnetworks of the DMN and that these subnetworks are individually relevant to the pathophysiology of schizophrenia. In summary, this large multsite study identified multiple temporally coherent networks, which are aberrant in schizophrenia versus healthy controls and suggests that both task‐correlated and task‐anticorrelated networks may serve as potential biomarkers. Hum Brain Mapp, 2009. © 2009 Wiley‐Liss, Inc.     

Establishing the resting state default mode network derived from functional magnetic resonance imaging tasks as an endophenotype: A twins study

The resting state default mode network (DMN) has been shown to characterize a number of neurological and psychiatric disorders. Evidence suggests an underlying genetic basis for this network and hence could serve as potential endophenotype for these disorders. Heritability is a defining criterion for endophenotypes. The DMN is measured either using a resting‐state functional magnetic resonance imaging (fMRI) scan or by extracting resting state activity from task‐based fMRI. The current study is the first to evaluate heritability of this task‐derived resting activity. 250 healthy adult twins (79 monozygotic and 46 dizygotic same sex twin pairs) completed five cognitive and emotion processing fMRI tasks. Resting state DMN functional connectivity was derived from these five fMRI tasks. We validated this approach by comparing connectivity estimates from task‐derived resting activity for all five fMRI tasks, with those obtained using a dedicated task‐free resting state scan in an independent cohort of 27 healthy individuals. Structural equation modeling using the classic twin design was used to estimate the genetic and environmental contributions to variance for the resting‐state DMN functional connectivity. About 9–41% of the variance in functional connectivity between the DMN nodes was attributed to genetic contribution with the greatest heritability found for functional connectivity between the posterior cingulate and right inferior parietal nodes (P < 0.001). Our data provide new evidence that functional connectivity measures from the intrinsic DMN derived from task‐based fMRI datasets are under genetic control and have the potential to serve as endophenotypes for genetically predisposed psychiatric and neurological disorders. Hum Brain Mapp 35:3893–3902, 2014. © 2014 Wiley Periodicals, Inc .     

Patterns of altered functional connectivity in mesial temporal lobe epilepsy

Purpose: In mesial temporal lobe epilepsy (MTLE) the epileptogenic area is confined to the mesial temporal lobe, but other cortical and subcortical areas are also affected and cognitive and psychiatric impairments are usually documented. Functional connectivity methods are based on the correlation of the blood oxygen level dependent (BOLD) signal between brain regions, which exhibit consistent and reproducible functional networks from resting state data. The aim of this study is to compare functional connectivity of patients with MTLE during the interictal period with healthy subjects. We hypothesize that patients show reduced functional connectivity compared to controls, the interest being to determine which regions show this reduction. Methods: We selected electroencephalography–functional magnetic resonance imaging (EEG‐fMRI) resting state data without EEG spikes from 16 patients with right and 7 patients with left MTLE. EEG‐fMRI resting state data of 23 healthy subjects matched for age, sex, and manual preference were selected as controls. Four volumes of interest in the left and right amygdalae and hippocampi (LA, RA, LH, and RH) were manually segmented in the anatomic MRI of each subject. The averaged BOLD time course within each volume of interest was used to detect brain regions with BOLD signal correlated with it. Group differences between patients and controls were estimated. Key Findings: In patients with right MTLE, group difference functional connectivity maps (RMTLE ? controls) showed for RA and RH decreased connectivity with the brain areas of the default mode network (DMN), the ventromesial limbic prefrontal regions, and contralateral mesial temporal structures; and for LA and LH, decreased connectivity with DMN and contralateral hippocampus. Additional decreased connectivity was found between LA and pons and between LH and ventromesial limbic prefrontal structures. In patients with left MTLE, functional connectivity maps (LMTLE ? controls) showed for LA and LH decreased connectivity with DMN, contralateral hippocampus, and bilateral ventromesial limbic prefrontal regions; no change in connectivity was detected for RA; and for RH, there was decreased connectivity with DMN, bilateral ventromesial limbic prefrontal regions, and contralateral amygdala and hippocampus. Significance: In unilateral MTLE, amygdala and hippocampus on the affected and to a lesser extent on the healthy side are less connected, and are also less connected with the dopaminergic mesolimbic and the DMNs. Changes in functional connectivity between mesial temporal lobe structures and these structures may explain cognitive and psychiatric impairments often found in patients with MTLE.     

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel‐reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs‐fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered‐SWC (T‐SWC) with different window lengths) based on both simulated and real rs‐fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T‐SWC (p‐value = .001) and SWC (p‐value <.001), respectively. Results of these different methods applied on real rs‐fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T‐SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC‐based method was unable to detect these dynamic connections.     

Test‐retest reliability of the default mode network in a multi‐centric fMRI study of healthy elderly: Effects of data‐driven physiological noise correction techniques

Understanding how to reduce the influence of physiological noise in resting state fMRI data is important for the interpretation of functional brain connectivity. Limited data is currently available to assess the performance of physiological noise correction techniques, in particular when evaluating longitudinal changes in the default mode network (DMN) of healthy elderly participants. In this 3T harmonized multisite fMRI study, we investigated how different retrospective physiological noise correction (rPNC) methods influence the within‐site test‐retest reliability and the across‐site reproducibility consistency of DMN‐derived measurements across 13 MRI sites. Elderly participants were scanned twice at least a week apart (five participants per site). The rPNC methods were: none (NPC), Tissue‐based regression, PESTICA and FSL‐FIX. The DMN at the single subject level was robustly identified using ICA methods in all rPNC conditions. The methods significantly affected the mean z‐scores and, albeit less markedly, the cluster‐size in the DMN; in particular, FSL‐FIX tended to increase the DMN z‐scores compared to others. Within‐site test‐retest reliability was consistent across sites, with no differences across rPNC methods. The absolute percent errors were in the range of 5–11% for DMN z‐scores and cluster‐size reliability. DMN pattern overlap was in the range 60–65%. In particular, no rPNC method showed a significant reliability improvement relative to NPC. However, FSL‐FIX and Tissue‐based physiological correction methods showed both similar and significant improvements of reproducibility consistency across the consortium (ICC = 0.67) for the DMN z‐scores relative to NPC. Overall these findings support the use of rPNC methods like tissue‐based or FSL‐FIX to characterize multisite longitudinal changes of intrinsic functional connectivity. Hum Brain Mapp 37:2114–2132, 2016. © 2016 Wiley Periodicals, Inc.     

Large‐scale dynamic causal modeling of major depressive disorder based on resting‐state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task‐based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within‐network or between‐network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large‐scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample‐size resting‐state fMRI consisting of 100 healthy subjects and 100 individuals with first‐episode drug‐naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high‐order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network‐averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self‐recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high‐order brain functional networks.