One-year test-retest reliability of intrinsic connectivity network fMRI in older adults

Brain network connectivity-behavioral relationships exhibit trait-like properties: Evidence from hippocampal connectivity and memory

Despite a growing number of studies showing relationships between behavior and resting-state functional MRI measures of large-scale brain network connectivity, no study to our knowledge has sought to investigate whether intrinsic connectivity-behavioral relationships are stable over time. In this study, we investigated the stability of such brain-behavior relationships at two timepoints, approximately 1 week apart. We focused on the relationship between the strength of hippocampal connectivity to posterior cingulate cortex and episodic memory performance. Our results showed that this relationship is stable across samples of a different age and reliable over two points in time. These findings provide the first evidence that the relationship between large-scale intrinsic network connectivity and episodic memory performance is a stable characteristic that varies between individuals.

Out-of-sync: disrupted neural activity in emotional circuitry during film viewing in melancholic depression

While a rich body of research in controlled experiments has established changes in the neural circuitry of emotion in major depressive disorders, little is known as to how such alterations might translate into complex, naturalistic settings--namely involving dynamic multimodal stimuli with rich contexts, such as those provided by films. Neuroimaging paradigms employing dynamic natural stimuli alleviate the anxiety often associated with complex tasks and eschew the need for laboratory-style abstractions, hence providing an ecologically valid means of elucidating neural underpinnings of neuropsychiatric disorders. To probe the neurobiological signature of refined depression subtypes, we acquired functional neuroimaging data in patients with the melancholic subtype of major depressive disorder during free viewing of emotionally salient films. We found a marked disengagement of ventromedial prefrontal cortex during natural viewing of a film with negative emotional valence in patients with melancholia. This effect significantly correlated with depression severity. Such changes occurred on the background of diminished consistency of neural activity in visual and auditory sensory networks, as well as higher-order networks involved in emotion and attention, including bilateral intraparietal sulcus and right anterior insula. These findings may reflect a failure to re-allocate resources and diminished reactivity to external emotional stimuli in melancholia.

Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review

The employment of graph theory to analyze spontaneous fluctuations in resting state BOLD fMRI data has become a dominant theme in brain imaging studies and neuroscience. Analysis of resting state functional brain networks based on graph theory has proven to be a powerful tool to quantitatively characterize functional architecture of the brain and it has provided a new platform to explore the overall structure of local and global functional connectivity in the brain. Due to its increased use and possible expansion to clinical use, it is essential that the reliability of such a technique is very strongly assessed. In this review, we explore the outcome of recent studies in network reliability which apply graph theory to analyze connectome resting state networks. Therefore, we investigate which preprocessing steps may affect reproducibility the most. In order to investigate network reliability, we compared the test-retest (TRT) reliability of functional data of published neuroimaging studies with different preprocessing steps. In particular we tested influence of global signal regression, correlation metric choice, binary versus weighted link definition, frequency band selection and length of time-series. Statistical analysis shows that only frequency band selection and length of time-series seem to affect TRT reliability. Our results highlight the importance of the choice of the preprocessing steps to achieve more reproducible measurements.

MRI characterization of temporal lobe epilepsy using rapidly measurable spatial indices with hemisphere asymmetries and gender features

INTRODUCTION

The paucity of morphometric markers for hemispheric asymmetries and gender variations in hippocampi and amygdalae in temporal lobe epilepsy (TLE) calls for better characterization of TLE by finding more useful prognostic MRI parameter(s).

METHODS

T1-weighted MRI (3 T) morphometry using multiple parameters of hippocampus-parahippocampus (angular and linear measures, volumetry) and amygdalae (volumetry) including their hemispheric asymmetry indices (AI) were evaluated in both genders. The cutoff values of parameters were statistically estimated from measurements of healthy subjects to characterize TLE (57 patients, 55% male) alterations.

RESULTS

TLE had differential categories with hippocampal atrophy, parahippocampal angle (PHA) acuteness, and several other parametric changes. Bilateral TLE categories were much more prevalent compared to unilateral TLE categories. Female patients were considerably more disposed to bilateral TLE categories than male patients. Male patients displayed diverse categories of unilateral abnormalities. Few patients (both genders) had combined bilateral appearances of hippocampal atrophy, amygdala atrophy, PHA acuteness, and increase in hippocampal angle (HA) where medial distance ratio (MDR) varied among genders. TLE had gender-specific and hemispheric dominant alterations in AI of parameters. Maximum magnitude of parametric changes in TLE includes (a) AI increase in HA of both genders, (b) HA increase (bilateral) in female patients, and (c) increase in ratio of amygdale/hippocampal volume (unilateral, right hemispheric), and AI decrease in MDR, in male patients.

CONCLUSION

Multiparametric MRI studies of hippocampus and amygdalae, including their hemispheric asymmetry, underscore better characterization of TLE. Rapidly measurable single-slice parameters (HA, PHA, MDR) can readily delineate TLE in a time-constrained clinical setting, which contrasts with customary three-dimensional hippocampal volumetry that requires many slice computation.

Away from home: the brain of the wandering mind as a model for schizophrenia

BACKGROUND

The notion that schizophrenia patients' (SZ) sense of being detached from external reality is a core feature of the disorder has existed since the early days of its recognition and is still largely emphasized in first person accounts of SZs; however, its etiology, neurophysiological mechanism, and significance for clinical symptoms are unclear. Mind-wandering is a ubiquitous experience of being detached from reality, the underlying neural mechanism of which closely resembles the brain in a resting-state.

METHODS

The resting-state functional magnetic resonance imaging data of 33 SZs and 33 matched healthy controls (CNT) were acquired. All subjects answered the mind-wandering subscale of the Imaginal Processing Inventory Questionnaire. Functional connectivity maps were constructed using 82 regions of interest comprising default-mode, salience, and frontoparietal networks.

RESULTS

SZs exhibit significantly higher mind-wandering frequency relative to CNT. The elevated mind-wandering frequency in SZs significantly correlated with positive and general symptom severity. The mind-wandering frequency was inversely correlated with connectivity degree in the right ventromedial prefrontal cortex, the brain region involved in self-experience in SZs.

CONCLUSIONS

Our results suggest that self-disturbances in SZs can explain SZs' disconnection to the external world, leading to the manifestation of positive psychotic symptoms. This study demonstrates strong preliminary evidence that contributes significantly to resolve the complex relationship between self, world, and the brain of SZs, which may lie at the "core" of psychotic experiences.

Reorganization of brain networks in aging: a review of functional connectivity studies

Healthy aging (HA) is associated with certain declines in cognitive functions, even in individuals that are free of any process of degenerative illness. Functional magnetic resonance imaging (fMRI) has been widely used in order to link this age-related cognitive decline with patterns of altered brain function. A consistent finding in the fMRI literature is that healthy old adults present higher activity levels in some brain regions during the performance of cognitive tasks. This finding is usually interpreted as a compensatory mechanism. More recent approaches have focused on the study of functional connectivity, mainly derived from resting state fMRI, and have concluded that the higher levels of activity coexist with disrupted connectivity. In this review, we aim to provide a state-of-the-art description of the usefulness and the interpretations of functional brain connectivity in the context of HA. We first give a background that includes some basic aspects and methodological issues regarding functional connectivity. We summarize the main findings and the cognitive models that have been derived from task-activity studies, and we then review the findings provided by resting-state functional connectivity in HA. Finally, we suggest some future directions in this field of research. A common finding of the studies included is that older subjects present reduced functional connectivity compared to young adults. This reduced connectivity affects the main brain networks and explains age-related cognitive alterations. Remarkably, the default mode network appears as a highly compromised system in HA. Overall, the scenario given by both activity and connectivity studies also suggests that the trajectory of changes during task may differ from those observed during resting-state. We propose that the use of complex modeling approaches studying effective connectivity may help to understand context-dependent functional reorganizations in the aging process.

The influence of physiological noise correction on test-retest reliability of resting-state functional connectivity

The utility and success of resting-state functional connectivity MRI (rs-fcMRI) depend critically on the reliability of this technique and the extent to which it accurately reflects neuronal function. One challenge is that rs-fcMRI is influenced by various sources of noise, particularly cardiac- and respiratory-related signal variations. The goal of the current study was to evaluate the impact of various physiological noise correction techniques, specifically those that use independent cardiac and respiration measures, on the test-retest reliability of rs-fcMRI. A group of 25 subjects were each scanned at three time points--two within the same imaging session and another 2-3 months later. Physiological noise corrections accounted for significant variance, particularly in blood vessels, sagittal sinus, cerebrospinal fluid, and gray matter. The fraction of variance explained by each of these corrections was highly similar within subjects between sessions, but variable between subjects. Physiological corrections generally reduced intrasubject (between-session) variability, but also significantly reduced intersubject variability, and thus reduced the test-retest reliability of estimating individual differences in functional connectivity. However, based on known nonneuronal mechanisms by which cardiac pulsation and respiration can lead to MRI signal changes, and the observation that the physiological noise itself is highly stable within individuals, removal of this noise will likely increase the validity of measured connectivity differences. Furthermore, removal of these fluctuations will lead to better estimates of average or group maps of connectivity. It is therefore recommended that studies apply physiological noise corrections but also be mindful of potential correlations with measures of interest.

Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks

The ability to attribute mental states to others, or "mentalizing," is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17-60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies.

Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy-based brain imaging

Test-retest reliability of neuroimaging measurements is an important concern in the investigation of cognitive functions in the human brain. To date, intraclass correlation coefficients (ICCs), originally used in interrater reliability studies in behavioral sciences, have become commonly used metrics in reliability studies on neuroimaging and functional near-infrared spectroscopy (fNIRS). However, as there are six popular forms of ICC, the adequateness of the comprehensive understanding of ICCs will affect how one may appropriately select, use, and interpret ICCs toward a reliability study. We first offer a brief review and tutorial on the statistical rationale of ICCs, including their underlying analysis of variance models and technical definitions, in the context of assessment on intertest reliability. Second, we provide general guidelines on the selection and interpretation of ICCs. Third, we illustrate the proposed approach by using an actual research study to assess interest reliability of fNIRS-based, volumetric diffuse optical tomography of brain activities stimulated by a risk decision-making protocol. Last, special issues that may arise in reliability assessment using ICCs are discussed and solutions are suggested.

Save the global: global signal connectivity as a tool for studying clinical populations with functional magnetic resonance imaging

The global signal is commonly removed from resting-state data, as it was presumed to reflect physiological noise. However, removal of the global signal is now under debate, as this signal may reflect important neuronal components, and its removal may introduce artifacts into the data. Here, we show that the functional connectivity (FC) of the global signal is of functional relevance, as it differentiates between schizophrenia patients and healthy controls during rest. We also demonstrate that other reported findings related to various clinical populations may actually reflect alternations in global signal FC. The evidence of the clinical relevance of the global signal propose its usage as a research tool, and extend previously reported perils of global signal removal in resting-state data of clinical populations.

Interindividual variability in functional connectivity as long-term correlate of temporal discounting

During intertemporal choice (IT) future outcomes are usually devaluated as a function of the delay, a phenomenon known as temporal discounting (TD). Based on task-evoked activity, previous neuroimaging studies have described several networks associated with TD. However, given its relevance for several disorders, a critical challenge is to define a specific neural marker able to predict TD independently of task execution. To this aim, we used resting-state functional connectivity MRI (fcMRI) and measured TD during economic choices several months apart in 25 human subjects. We further explored the relationship between TD, impulsivity and decision uncertainty by collecting standard questionnaires on individual trait/state differences. Our findings indicate that fcMRI within and between critical nodes of task-evoked neural networks associated with TD correlates with discounting behavior measured a long time afterwards, independently of impulsivity. Importantly, the nodes form an intrinsic circuit that might support all the mechanisms underlying TD, from the representation of subjective value to choice selection through modulatory effects of cognitive control and episodic prospection.

The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults

In functional magnetic resonance imaging (fMRI) research one is typically interested in neural activity. However, the blood‐oxygenation level‐dependent (BOLD) signal is a composite of both neural and vascular activity. As factors such as age or medication may alter vascular function, it is essential to account for changes in neurovascular coupling when investigating neurocognitive functioning with fMRI. The resting‐state fluctuation amplitude (RSFA) in the fMRI signal (rsfMRI) has been proposed as an index of vascular reactivity. The RSFA compares favourably with other techniques such as breath‐hold and hypercapnia, but the latter are more difficult to perform in some populations, such as older adults. The RSFA is therefore a candidate for use in adjusting for age‐related changes in vascular reactivity in fMRI studies. The use of RSFA is predicated on its sensitivity to vascular rather than neural factors; however, the extent to which each of these factors contributes to RSFA remains to be characterized. The present work addressed these issues by comparing RSFA (i.e., rsfMRI variability) to proxy measures of (i) cardiovascular function in terms of heart rate (HR) and heart rate variability (HRV) and (ii) neural activity in terms of resting state magnetoencephalography (rsMEG). We derived summary scores of RSFA, a sensorimotor task BOLD activation, cardiovascular function and rsMEG variability for 335 healthy older adults in the population‐based Cambridge Centre for Ageing and Neuroscience cohort (Cam‐CAN; http://www.cam-can.com). Mediation analysis revealed that the effects of ageing on RSFA were significantly mediated by vascular factors, but importantly not by the variability in neuronal activity. Furthermore, the converse effects of ageing on the rsMEG variability were not mediated by vascular factors. We then examined the effect of RSFA scaling of task‐based BOLD in the sensorimotor task. The scaling analysis revealed that much of the effects of age on task‐based activation studies with fMRI do not survive correction for changes in vascular reactivity, and are likely to have been overestimated in previous fMRI studies of ageing. The results from the mediation analysis demonstrate that RSFA is modulated by measures of vascular function and is not driven solely by changes in the variance of neural activity. Based on these findings we propose that the RSFA scaling method is articularly useful in large scale and longitudinal neuroimaging studies of ageing, or with frail participants, where alternative measures of vascular reactivity are impractical. Hum Brain Mapp 36:2248–2269, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

The anterior insula shows heightened interictal intrinsic connectivity in migraine without aura

OBJECTIVE

We sought to explore whether patients with migraine show heightened interictal intrinsic connectivity within primary sensory networks, the salience network, and a network anchored by the dorsal pons, a region known to be active during migraine attacks.

METHODS

Using task-free fMRI and a region-of-interest analysis, we compared intrinsic connectivity patterns in 15 migraineurs without aura to 15 age- and sex-matched healthy controls, focusing on networks anchored by the calcarine cortex, Heschl gyrus, right anterior insula, and dorsal pons, a region active during migraine attacks. We also examined the relationship between network connectivity, migraine frequency, and sensory sensitivity symptoms.

RESULTS

Migraineurs showed increased connectivity between primary visual and auditory cortices and the right dorsal anterior insula, between the dorsal pons and the bilateral anterior insulae, and between the right and left ventral anterior insulae. Increased connectivity showed no clinical correlation with migraine frequency or sensory sensitivity.

CONCLUSIONS

Patients with migraine display interictal changes in the topology of intrinsic connections, with greater connectivity between primary sensory cortices, the pons, and the anterior insula, a region involved in representing and coordinating responses to emotional salience.

Reproducibility of graph-theoretic brain network metrics: a systematic review

This systematic review aimed to assess the reproducibility of graph-theoretic brain network metrics. Primary research studies of test-retest reliability conducted on healthy human subjects were included that quantified test-retest reliability using either the intraclass correlation coefficient (ICC) or the coefficient of variance. The MEDLINE, Web of Knowledge, Google Scholar, and OpenGrey databases were searched up to February 2014. Risk of bias was assessed with 10 criteria weighted toward methodological quality. Twenty-three studies were included in the review (n=499 subjects) and evaluated for various characteristics, including sample size (5-45), retest interval (<1 h to >1 year), acquisition method, and test-retest reliability scores. For at least one metric, ICCs reached the fair range (ICC 0.40-0.59) in one study, the good range (ICC 0.60-0.74) in five studies, and the excellent range (ICC>0.74) in 16 studies. Heterogeneity of methods prevented further quantitative analysis. Reproducibility was good overall. For the metrics having three or more ICCs reported for both functional and structural networks, six of seven were higher in structural networks, indicating that structural networks may be more reliable over time. The authors were also able to highlight and discuss a number of methodological factors affecting reproducibility.

Resting-state networks predict individual differences in common and specific aspects of executive function

The goal of the present study was to examine relationships between individual differences in resting state functional connectivity as ascertained by fMRI (rs-fcMRI) and performance on tasks of executive function (EF), broadly defined as the ability to regulate thoughts and actions. Unlike most previous research that focused on the relationship between rs-fcMRI and a single behavioral measure of EF, in the current study we examined the relationship of rs-fcMRI with individual differences in subcomponents of EF. Ninety-one adults completed a resting state fMRI scan and three separate EF tasks outside the magnet: inhibition of prepotent responses, task set shifting, and working memory updating. From these three measures, we derived estimates of common aspects of EF, as well as abilities specific to working memory updating and task shifting. Using Independent Components Analysis (ICA), we identified across the group of participants several networks of regions (Resting State Networks, RSNs) with temporally correlated time courses. We then used dual regression to explore how these RSNs covaried with individual differences in EF. Dual regression revealed that increased higher common EF was associated with connectivity of a) frontal pole with an attentional RSN, and b) Crus I and II of the cerebellum with the right frontoparietal RSN. Moreover, higher shifting-specific abilities were associated with increased connectivity of angular gyrus with a ventral attention RSN. The results of the current study suggest that the organization of the brain at rest may have important implications for individual differences in EF, and that individuals higher in EF may have expanded resting state networks as compared to individuals with lower EF.

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

Resting state functional connectivity changes induced by prior brain state are not network specific

Resting state functional connectivity (rFC) is used to identify functionally related brain areas without requiring subjects to perform specific tasks. Previous work suggests that prior brain state, as determined by the activity engaged in immediately prior to collection of resting state data, can influence the networks recovered by rFC analyses. We determined the prevalence and network specificity of rFC changes induced by manipulations of prior state (including an unstructured (unconstrained) state, and language and motor tasks). Three blocks of rest data (one after each of the specified prior states) were acquired on each of 25 subjects. We hypothesised that prior state induced changes in rFC would be greatest within the networks most actively recruited by that prior state. Changes in rFC were greatest following the motor task and, contrary to our hypothesis, were not network specific. This was demonstrated by comparing (1) the timecourses within a set of ROIs selected on the basis of task-related de/activation, and (2) seed-based whole brain voxel-wise connectivity maps, seeded from local maxima in the task-related de/activation maps. Changes in connectivity strength tended to manifest as increases in rFC relative to that in the unstructured rest state, with change maps resembling partially complete maps of the primary sensory cortices and the cognitive control network. The majority of rFC changes occurred in areas moderately (but not weakly) connected to the seeds. Constrained prior states were associated with lower across-participant variance in rFC. This systematic investigation of the effect of prior brain state on rFC indicates that the rFC changes induced by prior brain state occur both in brain networks related to that brain activity and in networks nominally unrelated to that brain activity.

Characteristics of canonical intrinsic connectivity networks across tasks and monozygotic twin pairs

Intrinsic connectivity networks (ICNs) are becoming more prominent in the analyses of in vivo brain activity as the field of neurometrics has revealed their importance for augmenting traditional cognitive neuroscience approaches. Consequently, tools that assess the coherence, or connectivity, and morphology of ICNs are being developed to support inferences and assumptions about the dynamics of the brain. Recently, we reported trait-like profiles of ICNs showing reliability over time and reproducibility across different contexts. This study further examined the trait-like and familial nature of ICNs by utilizing two divergent task paradigms in twins. The study aimed to identify stable network phenotypes that exhibited sensitivity to individual differences and external perturbations in task demands. Analogous ICNs were detected in each task and these ICNs showed consistency in morphology and intranetwork coherence across tasks, whereas the ICN timecourse dynamics showed sensitivity to task demands. Specifically, the timecourse of an arm/hand sensorimotor network showed the strongest correlation with the timeline of a hand imitation task, and the timecourse of a language-processing network showed the strongest temporal association with a verb generation task. The area V1/simple visual stimuli network exhibited the most consistency in morphology, coherence, and timecourse dynamics within and across tasks. Similarly, this network exhibited familiality in all three domains as well. Hence, this experiment is a proof of principle that the morphology and coherence of ICNs can be consistent both within and across tasks, that ICN timecourses can be differentially and meaningfully modulated by a task, and that these domains can exhibit familiality.

Selective vulnerability related to aging in large-scale resting brain networks

Normal aging is associated with cognitive decline. Evidence indicates that large-scale brain networks are affected by aging; however, it has not been established whether aging has equivalent effects on specific large-scale networks. In the present study, 40 healthy subjects including 22 older (aged 60–80 years) and 18 younger (aged 22–33 years) adults underwent resting-state functional MRI scanning. Four canonical resting-state networks, including the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN) and salience network, were extracted, and the functional connectivities in these canonical networks were compared between the younger and older groups. We found distinct, disruptive alterations present in the large-scale aging-related resting brain networks: the ECN was affected the most, followed by the DAN. However, the DMN and salience networks showed limited functional connectivity disruption. The visual network served as a control and was similarly preserved in both groups. Our findings suggest that the aged brain is characterized by selective vulnerability in large-scale brain networks. These results could help improve our understanding of the mechanism of degeneration in the aging brain. Additional work is warranted to determine whether selective alterations in the intrinsic networks are related to impairments in behavioral performance.

Resting-state functional MR imaging: a new window to the brain

Resting-state (RS) functional magnetic resonance (MR) imaging constitutes a novel paradigm that examines spontaneous brain function by using blood oxygen level-dependent contrast in the absence of a task. Spatially distributed networks of temporal synchronization can be detected that can characterize RS networks (RSNs). With a short acquisition time of less than 10 minutes, RS functional MR imaging can be applied in special populations such as children and patients with dementia. Some RSNs are already present in utero, while others mature in childhood. Around 10 major RSNs are consistently found in adults, but their exact spatial extent and strength of coherence are affected by physiologic parameters and drugs. Though the acquisition and analysis methods are still evolving, new disease insights are emerging in a variety of neurologic and psychiatric disorders. The default mode network is affected in Alzheimer disease and various other diseases of cognitive impairment. Alterations in RSNs have been identified in many diseases, in the absence of evident structural modifications, indicating a high sensitivity of the method. Moreover, there is evidence of correlation between RSN alterations and disease progression and severity. However, different diseases often affect the same RSN, illustrating the limited specificity of the findings. This suggests that neurologic and psychiatric diseases are characterized by altered interactions between RSNs and therefore the whole brain should be examined as an integral network (with subnetworks), for example, using graph analysis. A challenge for clinical applications of RS functional MR imaging is the potentially confounding effect of aging, concomitant vascular diseases, or medication on the neurovascular coupling and consequently the functional MR imaging response. Current investigation combines RS functional MR imaging and other methods such as electroencephalography or magnetoencephalography to better understand the vascular and neuronal contributions to alterations in functional connectivity.

Template based rotation: a method for functional connectivity analysis with a priori templates

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.

Resting states are resting traits--an FMRI study of sex differences and menstrual cycle effects in resting state cognitive control networks

To what degree resting state fMRI is stable or susceptible to internal mind states of the individual is currently an issue of debate. To address this issue, the present study focuses on sex differences and investigates whether resting state fMRI is stable in men and women or changes within relative short-term periods (i.e., across the menstrual cycle). Due to the fact that we recently reported menstrual cycle effects on cognitive control based on data collected during the same sessions, the current study is particularly interested in fronto-parietal resting state networks. Resting state fMRI was measured in sixteen women during three different cycle phases (menstrual, follicular, and luteal). Fifteen men underwent three sessions in corresponding time intervals. We used independent component analysis to identify four fronto-parietal networks. The results showed sex differences in two of these networks with women exhibiting higher functional connectivity in general, including the prefrontal cortex. Menstrual cycle effects on resting states were non-existent. It is concluded that sex differences in resting state fMRI might reflect sexual dimorphisms in the brain rather than transitory activating effects of sex hormones on the functional connectivity in the resting brain.

Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective

Resting-state functional magnetic resonance imaging (RFMRI) enables researchers to monitor fluctuations in the spontaneous brain activities of thousands of regions in the human brain simultaneously, representing a popular tool for macro-scale functional connectomics to characterize normal brain function, mind-brain associations, and the various disorders. However, the test-retest reliability of RFMRI remains largely unknown. We review previously published papers on the test-retest reliability of voxel-wise metrics and conduct a meta-summary reliability analysis of seven common brain networks. This analysis revealed that the heteromodal associative (default, control, and attention) networks were mostly reliable across the seven networks. Regarding examined metrics, independent component analysis with dual regression, local functional homogeneity and functional homotopic connectivity were the three mostly reliable RFMRI metrics. These observations can guide the use of reliable metrics and further improvement of test-retest reliability for other metics in functional connectomics. We discuss the main issues with low reliability related to sub-optimal design and the choice of data processing options. Future research should use large-sample test-retest data to rectify both the within-subject and between-subject variability of RFMRI measurements and accelerate the application of functional connectomics.

Generation of the probabilistic template of default mode network derived from resting-state fMRI

Default-mode network (DMN) has become a prominent network among all large-scale brain networks which can be derived from the resting-state fMRI (rs-fMRI) data. Statistical template labeling the common location of hubs in DMN is favorable in the identification of DMN from tens of components resulted from the independent component analysis (ICA). This paper proposed a novel iterative framework to generate a probabilistic DMN template from a coherent group of 40 healthy subjects. An initial template was visually selected from the independent components derived from group ICA analysis of the concatenated rs-fMRI data of all subjects. An effective similarity measure was designed to choose the best-fit component from all independent components of each subject computed given different component numbers. The selected DMN components for all subjects were averaged to generate an updated DMN template and then used to select the DMN for each subject in the next iteration. This process iterated until the convergence was reached, i.e., the overlapping region between the DMN areas of the current template and the one generated from the previous stage is more than 95%. By validating the constructed DMN template on the rs-fMRI data from another 40 subjects, the generated probabilistic DMN template and the proposed similarity matching mechanism were demonstrated to be effective in automatic selection of independent components from the ICA analysis results.

A quantitative neural network approach to understanding aging phenotypes

Basic research on neurocognitive aging has traditionally adopted a reductionist approach in the search for the basis of cognitive preservation versus decline. However, increasing evidence suggests that a network level understanding of the brain can provide additional novel insight into the structural and functional organization from which complex behavior and dysfunction emerge. Using graph theory as a mathematical framework to characterize neural networks, recent data suggest that alterations in structural and functional networks may contribute to individual differences in cognitive phenotypes in advanced aging. This paper reviews literature that defines network changes in healthy and pathological aging phenotypes, while highlighting the substantial overlap in key features and patterns observed across aging phenotypes. Consistent with current efforts in this area, here we outline one analytic strategy that attempts to quantify graph theory metrics more precisely, with the goal of improving diagnostic sensitivity and predictive accuracy for differential trajectories in neurocognitive aging. Ultimately, such an approach may yield useful measures for gauging the efficacy of potential preventative interventions and disease modifying treatments early in the course of aging.

The effects of pharmacological treatment on functional brain connectome in obsessive-compulsive disorder

BACKGROUND

Previous neuroimaging studies of obsessive-compulsive disorder (OCD) have reported both baseline functional alterations and pharmacological changes in localized brain regions and connections; however, the effects of selective serotonin reuptake inhibitor (SSRI) treatment on the whole-brain functional network have not yet been elucidated.

METHODS

Twenty-five drug-free OCD patients underwent resting-state functional magnetic resonance imaging. After 16-weeks, seventeen patients who received SSRI treatment were rescanned. Twenty-three matched healthy control subjects were examined at baseline for comparison, and 21 of them were rescanned after 16 weeks. Topological properties of brain networks (including small-world, efficiency, modularity, and connectivity degree) were analyzed cross-sectionally and longitudinally with graph-theory approach.

RESULTS

At baseline, OCD patients relative to healthy control subjects showed decreased small-world efficiency (including local clustering coefficient, local efficiency, and small-worldness) and functional association between default-mode and frontoparietal modules as well as widespread altered connectivity degrees in many brain areas. We observed clinical improvement in OCD patients after 16 weeks of SSRI treatment, which was accompanied by significantly elevated small-world efficiency, modular organization, and connectivity degree. Improvement of obsessive-compulsive symptoms was significantly correlated with changes in connectivity degree in right ventral frontal cortex in OCD patients after treatment.

CONCLUSIONS

This is first study to use graph-theory approach for investigating valuable biomarkers for the effects of SSRI on neuronal circuitries of OCD patients. Our findings suggest that OCD phenomenology might be the outcome of disrupted optimal balance in the brain networks and that reinstating this balance after SSRI treatment accompanies significant symptom improvement.

Network dysfunction in Alzheimer's disease and frontotemporal dementia: implications for psychiatry

Structural and functional connectivity methods are changing how researchers conceptualize and explore neuropsychiatric disease. Here, we summarize emerging evidence of large-scale network dysfunction in Alzheimer's disease and behavioral variant frontotemporal dementia, focusing on the divergent impact these disorders have on the default mode network and the salience network. We update a working model for understanding the functions of these networks within a broader anatomical context and highlight the relevance of this model for understanding psychiatric illness. Finally, we look ahead to persistent challenges in the application of network-based imaging methods to patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and other neuropsychiatric conditions. Recent advances and persistent needs are discussed, with an eye toward anticipating the hurdles that must be overcome for a network-based framework to clarify the biology of psychiatric illness and aid in the drug discovery process.

Hippocampal functional connectivity patterns during spatial working memory differ in right versus left temporal lobe epilepsy

Temporal lobe epilepsy (TLE), affecting the medial temporal lobe, is a disorder that affects not just episodic memory but also working memory (WM). However, the exact nature of hippocampal-related network activity in visuospatial WM remains unclear. To clarify this, we utilized a functional connectivity (FC) methodology to investigate hippocampal network involvement during the encoding phase of a functional magnetic resonance imaging (fMRI) visuospatial WM task in right and left TLE patients. Specifically, we assessed the relation between FC within right and left hippocampus-seeded networks, and patient performance (rate of correct responses) during the encoding phase of a block span WM task. Results revealed that both TLE groups displayed a negative relation between WM performance and FC between the left hippocampus and ipsilateral parahippocampal gyrus. We also found a positive relationship between performance and FC between the left hippocampus seed and the precuneus, in the right TLE group. Lastly, the left TLE specifically demonstrated a negative relationship between performance and FC between both hippocampi and ipsilateral cerebellar clusters. Our findings indicate that right and left TLE groups may develop different patterns of FC to implement visuospatial WM. Indeed, the present result suggests that FC provides a unique means of identifying abnormalities in brain networks, which cannot be discerned at the level of behavioral output through neuropsychological testing. More broadly, our findings demonstrate that FC methods applied to task-based fMRI provide the opportunity to define specific task-related networks.

Windowed correlation: a suitable tool for providing dynamic fMRI-based functional connectivity neurofeedback on task difficulty

The goal of neurofeedback training is to provide participants with relevant information on their ongoing brain processes in order to enable them to change these processes in a meaningful way. Under the assumption of an intrinsic brain-behavior link, neurofeedback can be a tool to guide a participant towards a desired behavioral state, such as a healthier state in the case of patients. Current research in clinical neuroscience regarding the most robust indicators of pathological brain processes in psychiatric and neurological disorders indicates that fMRI-based functional connectivity measures may be among the most important biomarkers of disease. The present study therefore investigated the general potential of providing fMRI neurofeedback based on functional correlations, computed from short-window time course data at the level of single task periods. The ability to detect subtle changes in task performance with block-wise functional connectivity measures was evaluated based on imaging data from healthy participants performing a simple motor task, which was systematically varied along two task dimensions representing two different aspects of task difficulty. The results demonstrate that fMRI-based functional connectivity measures may provide a better indicator for an increase in overall (motor) task difficulty than activation level-based measures. Windowed functional correlations thus seem to provide relevant and unique information regarding ongoing brain processes, which is not captured equally well by standard activation level-based neurofeedback measures. Functional connectivity markers, therefore, may indeed provide a valuable tool to enhance and monitor learning within an fMRI neurofeedback setup.

Reliability measures of functional magnetic resonance imaging in a longitudinal evaluation of mild cognitive impairment

As the aging population grows, it has become increasingly important to carefully characterize amnestic mild cognitive impairment (aMCI), a preclinical stage of Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is a valuable tool for monitoring disease progression in selectively vulnerable brain regions associated with AD neuropathology. However, the reliability of fMRI data in longitudinal studies of older adults with aMCI is largely unexplored. To address this, aMCI participants completed two visual working tasks, a Delayed-Recognition task and a One-Back task, on three separate scanning sessions over a three-month period. Test-retest reliability of the fMRI blood oxygen level dependent (BOLD) activity was assessed using an intraclass correlation (ICC) analysis approach. Results indicated that brain regions engaged during the task displayed greater reliability across sessions compared to regions that were not utilized by the task. During task-engagement, differential reliability scores were observed across the brain such that the frontal lobe, medial temporal lobe, and subcortical structures exhibited fair to moderate reliability (ICC=0.3-0.6), while temporal, parietal, and occipital regions exhibited moderate to good reliability (ICC=0.4-0.7). Additionally, reliability across brain regions was more stable when three fMRI sessions were used in the ICC calculation relative to two fMRI sessions. In conclusion, the fMRI BOLD signal is reliable across scanning sessions in this population and thus a useful tool for tracking longitudinal change in observational and interventional studies in aMCI.

Anterior temporal lobe degeneration produces widespread network-driven dysfunction

The neural organization of semantic memory remains much debated. A 'distributed-only' view contends that semantic knowledge is represented within spatially distant, modality-selective primary and association cortices. Observations in semantic variant primary progressive aphasia have inspired an alternative model featuring the anterior temporal lobe as an amodal hub that supports semantic knowledge by linking distributed modality-selective regions. Direct evidence has been lacking, however, to support intrinsic functional interactions between an anterior temporal lobe hub and upstream sensory regions in humans. Here, we examined the neural networks supporting semantic knowledge by performing a multimodal brain imaging study in healthy subjects and patients with semantic variant primary progressive aphasia. In healthy subjects, the anterior temporal lobe showed intrinsic connectivity to an array of modality-selective primary and association cortices. Patients showed focal anterior temporal lobe degeneration but also reduced physiological integrity throughout distributed modality-selective regions connected with the anterior temporal lobe in healthy controls. Physiological deficits outside the anterior temporal lobe correlated with scores on semantic tasks and with anterior temporal subregion atrophy, following domain-specific and connectivity-based predictions. The findings provide a neurophysiological basis for the theory that semantic processing is orchestrated through interactions between a critical anterior temporal lobe hub and modality-selective processing nodes.

Clinical applications of the functional connectome

Central to the development of clinical applications of functional connectomics for neurology and psychiatry is the discovery and validation of biomarkers. Resting state fMRI (R-fMRI) is emerging as a mainstream approach for imaging-based biomarker identification, detecting variations in the functional connectome that can be attributed to clinical variables (e.g., diagnostic status). Despite growing enthusiasm, many challenges remain. Here, we assess evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability). We focus on current R-fMRI-based prediction efforts, and survey R-fMRI used for neurosurgical planning. We identify gaps and needs for R-fMRI-based biomarker identification, highlighting the potential of emerging conceptual, analytical and cultural innovations (e.g., the Research Domain Criteria Project (RDoC), open science initiatives, and Big Data) to address them. Additionally, we note the need to expand future efforts beyond identification of biomarkers for disease status alone to include clinical variables related to risk, expected treatment response and prognosis.

Short- and long-term effects of a novel on connectivity in the brain

We sought to determine whether reading a novel causes measurable changes in resting-state connectivity of the brain and how long these changes persist. Incorporating a within-subjects design, participants received resting-state functional magnetic resonance imaging scans on 19 consecutive days. First, baseline resting state data for a "washin" period were taken for each participant for 5 days. For the next 9 days, participants read 1/9th of a novel during the evening and resting-state data were taken the next morning. Finally, resting-state data for a "wash-out" period were taken for 5 days after the conclusion of the novel. On the days after the reading, significant increases in connectivity were centered on hubs in the left angular/supramarginal gyri and right posterior temporal gyri. These hubs corresponded to regions previously associated with perspective taking and story comprehension, and the changes exhibited a timecourse that decayed rapidly after the completion of the novel. Long-term changes in connectivity, which persisted for several days after the reading, were observed in bilateral somatosensory cortex, suggesting a potential mechanism for "embodied semantics."

Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study

Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study.

Impact of analysis methods on the reproducibility and reliability of resting-state networks

Though previous examinations of intrinsic resting-state networks (RSNs) in healthy populations have consistently identified several RSNs that represent connectivity patterns evoked by cognitive and sensory tasks, the effects of different analytic approaches on the reliability and reproducibility of these RSNs have yet to be fully explored. Thus, the primary aim of the current study was to investigate the effect of method (independent component analyses [ICA] vs. seed-based analyses) on RSN reproducibility (independent datasets) for ICA and reliability (independent time points) in both methods using functional magnetic resonance imaging. Good to excellent reproducibility was observed in 9 out of 10 commonly identified RSNs, indicating the robustness of these intrinsic fluctuations at the group level. Reliability analyses showed that results were dependent on three main methodological factors: (1) group versus subject-level analyses (group>subject); (2) whether data from different visits were analyzed separately or jointly with ICA (combined>separate ICA); and (3) whether ICA output was used to directly assess reliability or to inform seed-based analyses (seed-based>ICA). These results suggest that variations in the analytic technique have a significant impact on individual reliability measurements, but do not significantly affect the reproducibility or reliability of RSNs at the group level. Further investigation into the effect of the analytic technique on RSN quantification is warranted to increase the utility of RSN analyses in clinical studies.

A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that "micro" head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD>0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics - particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

Impaired default network functional connectivity in autosomal dominant Alzheimer disease

OBJECTIVE

To investigate default mode network (DMN) functional connectivity MRI (fcMRI) in a large cross-sectional cohort of subjects from families harboring pathogenic presenilin-1 (PSEN1), presenilin-2 (PSEN2), and amyloid precursor protein (APP) mutations participating in the Dominantly Inherited Alzheimer Network.

METHODS

Eighty-three mutation carriers and 37 asymptomatic noncarriers from the same families underwent fMRI during resting state at 8 centers in the United States, United Kingdom, and Australia. Using group-independent component analysis, fcMRI was compared using mutation status and Clinical Dementia Rating to stratify groups, and related to each participant's estimated years from expected symptom onset (eYO).

RESULTS

We observed significantly decreased DMN fcMRI in mutation carriers with increasing Clinical Dementia Rating, most evident in the precuneus/posterior cingulate and parietal cortices (p < 0.001). Comparison of asymptomatic mutation carriers with noncarriers demonstrated decreased fcMRI in the precuneus/posterior cingulate (p = 0.014) and right parietal cortex (p = 0.0016). We observed a significant interaction between mutation carrier status and eYO, with decreases in DMN fcMRI observed as mutation carriers approached and surpassed their eYO.

CONCLUSION

Functional disruption of the DMN occurs early in the course of autosomal dominant Alzheimer disease, beginning before clinically evident symptoms, and worsening with increased impairment. These findings suggest that DMN fcMRI may prove useful as a biomarker across a wide spectrum of disease, and support the feasibility of DMN fcMRI as a secondary endpoint in upcoming multicenter clinical trials in Alzheimer disease.

Intrinsic connectivity network disruption in progressive supranuclear palsy

OBJECTIVE

Progressive supranuclear palsy (PSP) has been conceptualized as a large-scale network disruption, but the specific network targeted has not been fully characterized. We sought to delineate the affected network in patients with clinical PSP.

METHODS

Using task-free functional magnetic resonance imaging, we mapped intrinsic connectivity to the dorsal midbrain tegmentum (dMT), a region that shows focal atrophy in PSP. Two healthy control groups (1 young, 1 older) were used to define and replicate the normal connectivity pattern, and patients with PSP were compared to an independent matched healthy control group on measures of network connectivity.

RESULTS

Healthy young and older subjects showed a convergent pattern of connectivity to the dMT, including brainstem, cerebellar, diencephalic, basal ganglia, and cortical regions involved in skeletomotor, oculomotor, and executive control. Patients with PSP showed significant connectivity disruptions within this network, particularly within corticosubcortical and cortico-brainstem interactions. Patients with more severe functional impairment showed lower mean dMT network connectivity scores.

INTERPRETATION

This study defines a PSP-related intrinsic connectivity network in the healthy brain and demonstrates the sensitivity of network-based imaging methods to PSP-related physiological and clinical changes.

Resting state magnetoencephalography functional connectivity in traumatic brain injury

OBJECT

Traumatic brain injury (TBI) is one of the leading causes of morbidity worldwide. One mechanism by which blunt head trauma may disrupt normal cognition and behavior is through alteration of functional connectivity between brain regions. In this pilot study, the authors applied a rapid automated resting state magnetoencephalography (MEG) imaging technique suitable for routine clinical use to test the hypothesis that there is decreased functional connectivity in patients with TBI compared with matched controls, even in cases of mild TBI. Furthermore, they posit that these abnormal reductions in MEG functional connectivity can be detected even in TBI patients without specific evidence of traumatic lesions on 3-T MR images. Finally, they hypothesize that the reductions of functional connectivity can improve over time across serial MEG scans during recovery from TBI.

METHODS

Magnetoencephalography maps of functional connectivity in the alpha (8- to 12-Hz) band from 21 patients who sustained a TBI were compared with those from 18 age- and sex-matched controls. Regions of altered functional connectivity in each patient were detected in automated fashion through atlas-based registration to the control database. The extent of reduced functional connectivity in the patient group was tested for correlations with clinical characteristics of the injury as well as with findings on 3-T MRI. Finally, the authors compared initial connectivity maps with 2-year follow-up functional connectivity in a subgroup of 5 patients with TBI.

RESULTS

Fourteen male and 7 female patients (17-53 years old, median 29 years) were enrolled. By Glasgow Coma Scale (GCS) criteria, 11 patients had mild, 1 had moderate, and 3 had severe TBI, and 6 had no GCS score recorded. On 3-T MRI, 16 patients had abnormal findings attributable to the trauma and 5 had findings in the normal range. As a group, the patients with TBI had significantly lower functional connectivity than controls (p < 0.01). Three of the 5 patients with normal findings on 3-T MRI showed regions of abnormally reduced MEG functional connectivity. No significant correlations were seen between extent of functional disconnection and injury severity or posttraumatic symptoms (p > 0.05). In the subgroup undergoing 2-year follow-up, the second MEG scan demonstrated a significantly lower percentage of voxels with decreased connectivity (p < 0.05) than the initial MEG scan.

CONCLUSIONS

A rapid automated resting-state MEG imaging technique demonstrates abnormally decreased functional connectivity that may persist for years after TBI, including cases classified as "mild" by GCS criteria. Disrupted MEG connectivity can be detected even in some patients with normal findings on 3-T MRI. Analysis of follow-up MEG scans in a subgroup of patients shows that, over time, the abnormally reduced connectivity can improve, suggesting neuroplasticity during the recovery from TBI. Resting state MEG deserves further investigation as a prognostic and predictive biomarker for TBI.

Neurometrics of intrinsic connectivity networks at rest using fMRI: retest reliability and cross-validation using a meta-level method

Functional images of the resting brain can be empirically parsed into intrinsic connectivity networks (ICNs) which closely resemble patterns of evoked task-based brain activity and which have a biological and genetic basis. Recently, ICNs have become popular for investigating brain functioning and brain-behavior relationships. However, the replicability and neurometrics of these networks are only beginning to be reported. Using a meta-level independent component analysis (ICA), we produced ICNs from three data sets collected from two samples of healthy adults. The ICNs from our data sets demonstrated robust and independent replication of 12 intrinsic networks that reflected 17 canonical, task-based, brain networks. We found within-subject reliability of ICNs was modest overall, but ranged from poor to good, and that voxels with the highest measured connectivity rarely had the highest reliability. Networks associated with executive functions, visuospatial reasoning, motor coordination, speech and audition, default mode, vision, and interoception showed moderate to high group-level reproducibility and replicability. However, only the first four of these networks also showed fair or better within-subject reliability over time. Our findings highlight the replicability of ICNs across data sets, the range of within-subject neurometrics across different networks, and the shared characteristics between resting and task-based networks.

Resting-state functional connectivity in normal brain aging

The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rs-fMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field.

Electroconvulsive therapy response in major depressive disorder: a pilot functional network connectivity resting state FMRI investigation

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treatment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case-control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and functional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treatment. The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode.

The connectivity of functional cores reveals different degrees of segregation and integration in the brain at rest

The principles of functional specialization and integration in the resting brain are implemented in a complex system of specialized networks that share some degree of interaction. Recent studies have identified wider functional modules compared to previously defined networks and reported a small-world architecture of brain activity in which central nodes balance the pressure to evolve segregated pathways with the integration of local systems. The accurate identification of such central nodes is crucial but might be challenging for several reasons, e.g. inter-subject variability and physiological/pathological network plasticity, and recent works reported partially inconsistent results concerning the properties of these cortical hubs. Here, we applied a whole-brain data-driven approach to extract cortical functional cores and examined their connectivity from a resting state fMRI experiment on healthy subjects. Two main statistically significant cores, centered on the posterior cingulate cortex and the supplementary motor area, were extracted and their functional connectivity maps, thresholded at three statistical levels, revealed the presence of two complex systems. One system is consistent with the default mode network (DMN) and gradually connects to visual regions, the other centered on motor regions and gradually connects to more sensory-specific portions of cortex. These two large scale networks eventually converged to regions belonging to the medial aspect of the DMN, potentially allowing inter-network interactions.

An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed.