Quantification of the impact of a confounding variable on functional connectivity confirms anti-correlated networks in the resting-state

Functional topography of pulvinar-visual cortex networks in macaques revealed by INS-fMRI

The pulvinar in the macaque monkey contains three divisions: the medial pulvinar (PM), the lateral pulvinar (PL), and the inferior pulvinar (PI). Anatomical studies have shown that connections of PM are preferentially distributed to higher association areas, those of PL are biased toward the ventral visual pathway, and those of PI are biased with the dorsal visual pathway. To study functional connections of the pulvinar at mesoscale, we used a novel method called INS-fMRI (infrared neural stimulation and functional magnetic resonance imaging). This method permits studies and comparisons of multiple pulvinar networks within single animals. As previously revealed, stimulations of different sites in PL and PI produced topographically organized focal activations in visual areas V1, V2, and V3. In contrast, PM stimulation elicited little or diffuse response. The relative activations of areas V1, V2, V3A, V3d, V3v, V4, MT, and MST revealed that connections of PL are biased to ventral pathway areas, and those of PI are biased to dorsal areas. Different statistical values of activated blood-oxygen-level-dependent responses produced the same center of activation, indicating stability of connectivity; it also suggests possible dynamics of broad to focal responses from single stimulation sites. These results demonstrate that infrared neural stimulation-induced connectivity is largely consistent with previous anatomical connectivity studies, thereby demonstrating validity of our novel method. In addition, it suggests additional interpretations of functional connectivity to complement anatomical studies.

Disturbed functional connectivity and topological properties of the frontal lobe in minimally conscious state based on resting-state fNIRS

Background

Patients in minimally conscious state (MCS) exist measurable evidence of consciousness. The frontal lobe is a crucial part of the brain that encodes abstract information and is closely related to the conscious state. We hypothesized that the disturbance of the frontal functional network exists in MCS patients.

Methods

We collected the resting-state functional near-infrared spectroscopy (fNIRS) data of fifteen MCS patients and sixteen age- and gender-matched healthy controls (HC). The Coma Recovery Scale-Revised (CRS-R) scale of MCS patients was also composed. The topology of the frontal functional network was analyzed in two groups.

Results

Compared with HC, the MCS patients showed widely disrupted functional connectivity in the frontal lobe, especially in the frontopolar area and right dorsolateral prefrontal cortex. Moreover, the MCS patients displayed lower clustering coefficient, global efficiency, local efficiency, and higher characteristic path length. In addition, the nodal clustering coefficient and nodal local efficiency in the left frontopolar area and right dorsolateral prefrontal cortex were significantly reduced in MCS patients. Furthermore, the nodal clustering coefficient and nodal local efficiency in the right dorsolateral prefrontal cortex were positively correlated to auditory subscale scores.

Conclusion

This study reveals that MCS patients' frontal functional network is synergistically dysfunctional. And the balance between information separation and integration in the frontal lobe is broken, especially the local information transmission in the prefrontal cortex. These findings help us to understand the pathological mechanism of MCS patients better.

Abnormalities of hemispheric specialization in drug-naïve and drug-receiving self-limited epilepsy with centrotemporal spikes

PURPOSE

Self-limited epilepsy with centrotemporal spikes (SLECTS) is a pediatric benign epilepsy but often accompanied by subsequent (in adulthood) functional changes such as language, which are thought to have distinct areas of hemispheric lateralization and functional differentiation. This study aimed to explore hemispheric specialization measured by resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity in drug-naïve and drug-receiving SLECTS.

METHODS

Hemispheric specialization was quantified in three groups of children, including 21 drug-naïve patients (DNP) with SLECTS, 34 drug-receiving patients (DRP) with SLECTS and 36 demographically matched typical development (TD).

RESULTS

Compared with the TD group, both the DNP and DRP groups exhibited significantly higher specialization in the left superior temporal gyrus, right parahippocampus, left putamen, and right caudate. The DNP group exhibited significantly higher hemispheric specialization in the right precentral gyrus and right inferior temporal gyrus, while the DRP group demonstrated significantly higher hemispheric specialization in the left postcentral gyrus and right hippocampus than the TD group. Furthermore, bilateral cerebellum_6 showed opposing hemispheric specialization trends in the two patient groups. Further meta-analytical mapping demonstrated that hemispheric specialization-related differential brain regions are primarily involved in language processing.

CONCLUSION

Our findings showed that children with SLECTS had altered hemispheric specialization, mainly in language processing regions, suggesting both abnormal intrahemispheric segregation and interhemispheric integration in these children.

Dynamic and Static Amplitude of Low-Frequency Fluctuation Is a Potential Biomarker for Predicting Prognosis of Degenerative Cervical Myelopathy Patients: A Preliminary Resting-State fMRI Study

Objective

The aim of this study was to explore the clinical value of the static amplitude of low-frequency fluctuation (sALFF) and dynamic amplitude of low-frequency fluctuation (dALFF) in the identification of brain functional alterations in degenerative cervical myelopathy (DCM) patients.

Methods

Voxel-wise sALFF and dALFF of 47 DCM patients and 44 healthy controls were calculated using resting-state fMRI data, and an intergroup comparison was performed. The mean of sALFF or dALFF data were extracted within the resultant clusters and the correlation analysis of these data with the clinical measures was performed. Furthermore, whole-brain-wise and region-wise multivariate pattern analyses (MVPAs) were performed to classify DCM patients and healthy controls. sALFF and dALFF were used to predict the prognosis of DCM patients.

Results

The findings showed that (1) DCM patients exhibited higher sALFF within the left thalamus and putamen compared with that of the healthy controls. DCM patients also exhibited lower dALFF within bilateral postcentral gyrus compared with the healthy controls; (2) No significant correlations were observed between brain alterations and clinical measures through univariate correlation analysis; (3) sALFF (91%) and dALFF (95%) exhibited high accuracy in classifying the DCM patients and healthy controls; (4) Region-wise MVPA further revealed brain regions in which functional patterns were associated with prognosis in DCM patients. These regions were mainly located at the frontal lobe and temporal lobe.

Conclusion

In summary, sALFF and dALFF can be used to accurately reveal brain functional alterations in DCM patients. Furthermore, the multivariate approach is a more sensitive method in exploring neuropathology and establishing a prognostic biomarker for DCM compared with the conventional univariate method.

Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder

Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.

A multi-measure approach for assessing the performance of fMRI preprocessing strategies in resting-state functional connectivity

It is well established that head motion and physiological processes (e.g. cardiac and breathing activity) should be taken into consideration when analyzing and interpreting results in fMRI studies. However, even though recent studies aimed to evaluate the performance of different preprocessing pipelines there is still no consensus on the optimal strategy. This is partly due to the fact that the quality control (QC) metrics used to evaluate differences in performance across pipelines have often yielded contradictory results. Furthermore, preprocessing techniques based on physiological recordings or data decomposition techniques (e.g. aCompCor) have not been comprehensively examined. Here, to address the aforementioned issues, we propose a framework that summarizes the scores from eight previously proposed and novel QC metrics to a reduced set of two QC metrics that reflect the signal-to-noise ratio and the reduction in motion artifacts and biases in the preprocessed fMRI data. Using this framework, we evaluate the performance of three commonly used practices on the quality of data: 1) Removal of nuisance regressors from fMRI data, 2) discarding motion-contaminated volumes (i.e., scrubbing) before regression, and 3) low-pass filtering the data and the nuisance regressors before their removal. Using resting-state fMRI data from the Human Connectome Project, we show that the scores of the examined QC metrics improve the most when the global signal (GS) and about 17% of principal components from white matter (WM) are removed from the data. Finally, we observe a small further improvement with low-pass filtering at 0.20 Hz and milder variants of WM denoising, but not with scrubbing.

Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph

The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.

Mapping grip force to motor networks

AIM

There is ongoing debate about the role of cortical and subcortical brain areas in force modulation. In a whole-brain approach, we sought to investigate the anatomical basis of grip force whilst acknowledging interindividual differences in connectivity patterns. We tested if brain lesion mapping in patients with unilateral motor deficits can inform whole-brain structural connectivity analysis in healthy controls to uncover the networks underlying grip force.

METHODS

Using magnetic resonance imaging (MRI) and whole-brain voxel-based morphometry in chronic stroke patients (n=55) and healthy controls (n=67), we identified the brain regions in both grey and white matter significantly associated with grip force strength. The resulting statistical parametric maps (SPMs) provided seed areas for whole-brain structural covariance analysis in a large-scale community dwelling cohort (n=977) that included beyond volume estimates, parameter maps sensitive to myelin, iron and tissue water content.

RESULTS

The SPMs showed symmetrical bilateral clusters of correlation between upper limb motor performance, basal ganglia, posterior insula and cortico-spinal tract. The covariance analysis with the seed areas derived from the SPMs demonstrated a widespread anatomical pattern of brain volume and tissue properties, including both cortical, subcortical nodes of motor networks and sensorimotor areas projections.

CONCLUSION

We interpret our covariance findings as a biological signature of brain networks implicated in grip force. The data-driven definition of seed areas obtained from chronic stroke patients showed overlapping structural covariance patterns within cortico-subcortical motor networks across different tissue property estimates. This cumulative evidence lends face validity of our findings and their biological plausibility.

Hallucinations Under Psychedelics and in the Schizophrenia Spectrum: An Interdisciplinary and Multiscale Comparison

The recent renaissance of psychedelic science has reignited interest in the similarity of drug-induced experiences to those more commonly observed in psychiatric contexts such as the schizophrenia-spectrum. This report from a multidisciplinary working group of the International Consortium on Hallucinations Research (ICHR) addresses this issue, putting special emphasis on hallucinatory experiences. We review evidence collected at different scales of understanding, from pharmacology to brain-imaging, phenomenology and anthropology, highlighting similarities and differences between hallucinations under psychedelics and in the schizophrenia-spectrum disorders. Finally, we attempt to integrate these findings using computational approaches and conclude with recommendations for future research.

Elevated caudate connectivity in cognitively normal Parkinson's disease patients

Mild cognitive impairment (MCI) is common in Parkinson’s disease patients. However, its underlying mechanism is not well understood, which has hindered new treatment discoveries specific to MCI. The aim of this study was to investigate functional connectivity changes of the caudate nucleus in cognitively impaired Parkinson’s patients. We recruited 18 Parkinson’s disease patients—10 PDNC [normal cognition Parkinson’s disease; Montreal Cognitive Assessment (MoCA) ≥ 26], 8 PDLC (low cognition Parkinson’s disease; MoCA < 26) —and 10 age-matched healthy controls. All subjects were scanned with resting-state functional magnetic resonance imaging (MRI) and perfusion MRI. We analyzed these data for graph theory metrics and Alzheimer’s disease-like pattern score, respectively. A strong positive correlation was found between the functional connectivity of the right caudate nucleus and MoCA scores in Parkinson’s patient groups, but not in healthy control subjects. Interestingly, PDNC’s functional connectivity of the right caudate was significantly higher than both PDLC and healthy controls, while PDLC and healthy controls were not significantly different from each other. We found that Alzheimer’s disease-like metabolic/perfusion pattern score correlated with MoCA scores in healthy controls, but not in Parkinson’s disease. Increased caudate connectivity may be related to a compensatory mechanism found in cognitively normal patients with Parkinson’s disease. Our findings support and complement the dual syndrome hypothesis.

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.

Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors

Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.

Psychedelics, Meditation, and Self-Consciousness

In recent years, the scientific study of meditation and psychedelic drugs has seen remarkable developments. The increased focus on meditation in cognitive neuroscience has led to a cross-cultural classification of standard meditation styles validated by functional and structural neuroanatomical data. Meanwhile, the renaissance of psychedelic research has shed light on the neurophysiology of altered states of consciousness induced by classical psychedelics, such as psilocybin and LSD, whose effects are mainly mediated by agonism of serotonin receptors. Few attempts have been made at bridging these two domains of inquiry, despite intriguing evidence of overlap between the phenomenology and neurophysiology of meditation practice and psychedelic states. In particular, many contemplative traditions explicitly aim at dissolving the sense of self by eliciting altered states of consciousness through meditation, while classical psychedelics are known to produce significant disruptions of self-consciousness, a phenomenon known as drug-induced ego dissolution. In this article, we discuss available evidence regarding convergences and differences between phenomenological and neurophysiological data on meditation practice and psychedelic drug-induced states, with a particular emphasis on alterations of self-experience. While both meditation and psychedelics may disrupt self-consciousness and underlying neural processes, we emphasize that neither meditation nor psychedelic states can be conceived as simple, uniform categories. Moreover, we suggest that there are important phenomenological differences even between conscious states described as experiences of self-loss. As a result, we propose that self-consciousness may be best construed as a multidimensional construct, and that "self-loss," far from being an unequivocal phenomenon, can take several forms. Indeed, various aspects of self-consciousness, including narrative aspects linked to autobiographical memory, self-related thoughts and mental time travel, and embodied aspects rooted in multisensory processes, may be differently affected by psychedelics and meditation practices. Finally, we consider long-term outcomes of experiences of self-loss induced by meditation and psychedelics on individual traits and prosocial behavior. We call for caution regarding the problematic conflation of temporary states of self-loss with "selflessness" as a behavioral or social trait, although there is preliminary evidence that correlations between short-term experiences of self-loss and long-term trait alterations may exist.

The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults

An important characteristic of spontaneous brain activity is the anticorrelation between the core default network (cDN) and the dorsal attention network (DAN) and the salience network (SN). This anticorrelation may constitute a key aspect of functional anatomy and is implicated in several brain disorders. We used dynamic causal modeling to assess the hypothesis that a causal hierarchy underlies this anticorrelation structure, using resting-state fMRI of healthy adolescent and young adults (N = 404). Our analysis revealed an asymmetric effective connectivity, such that the regions in the SN and DAN exerted an inhibitory influence on the cDN regions; whereas the cDN exerted an excitatory influence on the SN and DAN regions. The relative strength of efferent versus afferent connections places the SN at the apex of the hierarchy, suggesting that the SN modulates anticorrelated networks with descending hierarchical connections. In short, this study of directed neuronal coupling reveals a causal hierarchical architecture that generates or orchestrates anticorrelation of brain activity. These new findings shed light on functional integration of intrinsic brain networks at rest and speak to future dynamic causal modeling studies of large-scale networks.

Partial correlation analysis reveals abnormal retinotopically organized functional connectivity of visual areas in amblyopia

Amblyopia is a prevalent developmental visual disorder of childhood that typically persists in adults. Due to altered visual experience during critical periods of youth, the structure and function of adult visual cortex is abnormal. In addition to substantial deficits shown with task-based fMRI, previous studies have used resting state measures to demonstrate altered long-range connectivity in amblyopia. This is the first study in amblyopia to analyze connectivity between regions of interest that are smaller than a single cortical area and to apply partial correlation analysis to reduce network effects. We specifically assess short-range connectivity between retinotopically defined regions of interest within the occipital lobe of 8 subjects with amblyopia and 7 subjects with normal vision (aged 19–45). The representations of visual areas V1, V2, and V3 within each of the four quadrants of visual space were further subdivided into three regions based on maps of visual field eccentricity. Connectivity between pairs of all nine regions of interest in each quadrant was tested via correlation and partial correlation for both groups. Only the tests of partial correlation, i.e., correlation between time courses of two regions following the regression of time courses from all other regions, yielded significant differences between resting state functional connectivity in amblyopic and normal subjects. Subjects with amblyopia showed significantly higher partial correlation between para-foveal and more eccentric representations within V1, and this effect associated with poor acuity of the worse eye. In addition, we observed reduced correlation in amblyopic subjects between isoeccentricity regions in V1 and V2, and separately, between such regions in V2 and V3. We conclude that partial correlation-based connectivity is altered in an eccentricity-dependent pattern in visual field maps of amblyopic patients. Moreover, results are consistent with known clinical and psychophysical vision loss. More broadly, this provides evidence that abnormal cortical adaptations to disease may be better isolated with tests of partial correlation connectivity than with the regular correlation techniques that are currently widely used.

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Cortical functional connectivity abnormalities exist in amblyopia at a scale finer than previously reported.

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Connectivity changes within primary visual cortex are consistent with known loss of function.

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Connectivity changes between visual areas are consistent with concept of deafferentation.

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Partial correlation differentiates patients from controls, whereas correlation does not.

Subtle alterations in cerebrovascular reactivity in mild cognitive impairment detected by graph theoretical analysis and not by the standard approach

There is growing support that cerebrovascular reactivity (CVR) in response to a vasodilatory challenge, also defined as the cerebrovascular reserve, is reduced in Alzheimer's disease dementia. However, this is less clear in patients with mild cognitive impairment (MCI). The current standard analysis may not reflect subtle abnormalities in CVR. In this study, we aimed to investigate vasodilatory-induced changes in the topology of the cerebral blood flow correlation (CBF_corr) network to study possible network-related CVR abnormalities in MCI. For this purpose, four CBF_corr networks were constructed: two using CBF SPECT data at baseline and under the vasodilatory challenge of acetazolamide (ACZ), obtained from a group of 26 MCI patients; and two equivalent networks from a group of 26 matched cognitively normal controls. The mean strength of association (SA) and clustering coefficient (C) were used to evaluate ACZ-induced changes on the topology of CBF_corr networks. We found that cognitively normal adults and MCI patients show different patterns of C and SA changes. The observed differences included the medial prefrontal cortices and inferior parietal lobe, which represent areas involved in MCI's cognitive dysfunction. In contrast, no substantial differences were detected by standard CVR analysis. These results suggest that graph theoretical analysis of ACZ-induced changes in the topology of the CBF_corr networks allows the identification of subtle network-related CVR alterations in MCI, which couldn't be detected by the standard approach.

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Subtle alterations in cerebrovascular reactivity in MCI by graph theoretical analysis.

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Graph theoretical analysis seems to be sensitive to subtle abnormalities.

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The standard approach could be insufficient for capturing subtle abnormal changes.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

Dissociated patterns of anti-correlations with dorsal and ventral default-mode networks at rest

Previous studies of resting state functional connectivity have demonstrated that the default-mode network (DMN) is negatively correlated with a set of brain regions commonly activated during goal-directed tasks. However, the location and extent of anti-correlations are inconsistent across different studies, which has been posited to result largely from differences in whether or not global signal regression (GSR) was applied as a pre-processing step. Notably, coordinates of seed regions-of-interest defined within the posterior cingulate cortex (PCC)/precuneus, an area often employed to study functional connectivity of the DMN, have been inconsistent across studies. Taken together with recent observations that the DMN contains functionally heterogeneous subdivisions, it is presently unclear whether these seeds map to different DMN subnetworks, whose patterns of anti-correlation may differ. If so, then seed location may be a non-negligible factor that, in addition to differences in preprocessing steps, contributes to the inconsistencies reported among published studies regarding DMN correlations/anti-correlations. In this study, they examined anti-correlations of different subnetworks within the DMN during rest using both seed-based and point process analyses, and discovered that: (1) the ventral branch of the DMN (vDMN) yielded significantly weaker anti-correlations than that associated with the dorsal branch of the DMN (dDMN); (2) vDMN anti-correlations introduced by GSR were distinct from dDMN anti-correlations; (3) PCC/precuneus seeds employed by earlier studies mapped to different DMN subnetworks, which may explain some of the inconsistency (in addition to preprocessing steps) in the reported DMN anti-correlations. Hum Brain Mapp 38:2454-2465, 2017. © 2017 Wiley Periodicals, Inc.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Global signal regression acts as a temporal downweighting process in resting-state fMRI

In resting-state functional MRI (rsfMRI), the correlation between blood oxygenation level dependent (BOLD) signals across different brain regions is used to estimate the functional connectivity of the brain. This approach has led to the identification of a number of resting-state networks, including the default mode network (DMN) and the task positive network (TPN). Global signal regression (GSR) is a widely used pre-processing step in rsfMRI that has been shown to improve the spatial specificity of the estimated resting-state networks. In GSR, a whole brain average time series, known as the global signal (GS), is regressed out of each voxel time series prior to the computation of the correlations. However, the use of GSR is controversial because it can introduce artifactual negative correlations. For example, it has been argued that anticorrelations observed between the DMN and TPN are primarily an artifact of GSR. Despite the concerns about GSR, there is currently no consensus regarding its use. In this paper, we introduce a new framework for understanding the effects of GSR. In particular, we show that the main effects of GSR can be well approximated as a temporal downweighting process in which the data from time points with relatively large GS magnitudes are greatly attenuated while data from time points with relatively small GS magnitudes are largely unaffected. Furthermore, we show that a limiting case of this downweighting process in which data from time points with large GS magnitudes are censored can also approximate the effects of GSR. In other words, the correlation maps obtained after GSR show a high degree of spatial similarity (including the presence of anticorrelations between the DMN and TPN) with maps obtained using only the uncensored (i.e. retained) time points. Since the data from these retained time points are unaffected by the censoring process, this finding suggests that the observed anticorrelations inherently exist in the data from time points with small GS magnitudes and are not simply an artifact of GSR.

Cognitive reserve moderates the association between functional network anti-correlations and memory in MCI

Cognitive reserve (CR) shows protective effects on cognitive function in older adults. Here, we focused on the effects of CR at the functional network level. We assessed in patients with amnestic mild cognitive impairment (aMCI) whether higher CR moderates the association between low internetwork cross-talk on memory performance. In 2 independent aMCI samples (n = 76 and 93) and healthy controls (HC, n = 36), CR was assessed via years of education and intelligence (IQ). We focused on the anti-correlation between the dorsal attention network (DAN) and an anterior and posterior default mode network (DMN), assessed via sliding time window analysis of resting-state functional magnetic resonance imaging (fMRI). The DMN-DAN anti-correlation was numerically but not significantly lower in aMCI compared to HC. However, in aMCI, lower anterior DMN-DAN anti-correlation was associated with lower memory performance. This association was moderated by CR proxies, where the association between the internetwork anti-correlation and memory performance was alleviated at higher levels of education or IQ. In conclusion, lower DAN-DMN cross-talk is associated with lower memory in aMCI, where such effects are buffered by higher CR.

Towards a consensus regarding global signal regression for resting state functional connectivity MRI

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single "right" way to process resting state data that reveals the "true" nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation.

Partial Correlation-Based Retinotopically Organized Resting-State Functional Connectivity Within and Between Areas of the Visual Cortex Reflects More Than Cortical Distance

Numerous studies have demonstrated functional magnetic resonance imaging (fMRI)-based resting-state functional connectivity (RSFC) between cortical areas. Recent evidence suggests that synchronous fluctuations in blood oxygenation level-dependent fMRI reflect functional organization at a scale finer than that of visual areas. In this study, we investigated whether RSFCs within and between lower visual areas are retinotopically organized and whether retinotopically organized RSFC merely reflects cortical distance. Subjects underwent retinotopic mapping and separately resting-state fMRI. Visual areas V1, V2, and V3, were subdivided into regions of interest (ROIs) according to quadrants and visual field eccentricity. Functional connectivity (FC) was computed based on Pearson's linear correlation (correlation), and Pearson's linear partial correlation (correlation between two time courses after the time courses from all other regions in the network are regressed out). Within a quadrant, within visual areas, all correlation and nearly all partial correlation FC measures showed statistical significance. Consistently in V1, V2, and to a lesser extent in V3, correlation decreased with increasing eccentricity separation. Consistent with previously reported monkey anatomical connectivity, correlation/partial correlation values between regions from adjacent areas (V1-V2 and V2-V3) were higher than those between nonadjacent areas (V1-V3). Within a quadrant, partial correlation showed consistent significance between regions from two different areas with the same or adjacent eccentricities. Pairs of ROIs with similar eccentricity showed higher correlation/partial correlation than pairs distant in eccentricity. Between dorsal and ventral quadrants, partial correlation between common and adjacent eccentricity regions within a visual area showed statistical significance; this extended to more distant eccentricity regions in V1. Within and between quadrants, correlation decreased approximately linearly with increasing distances separating the tested ROIs. Partial correlation showed a more complex dependence on cortical distance: it decreased exponentially with increasing distance within a quadrant, but was best fit by a quadratic function between quadrants. We conclude that RSFCs within and between lower visual areas are retinotopically organized. Correlation-based FC is nonselectively high across lower visual areas, even between regions that do not share direct anatomical connections. The mechanisms likely involve network effects caused by the dense anatomical connectivity within this network and projections from higher visual areas. FC based on partial correlation, which minimizes network effects, follows expectations based on direct anatomical connections in the monkey visual cortex better than correlation. Last, partial correlation-based retinotopically organized RSFC reflects more than cortical distance effects.

Patterns of Atypical Functional Connectivity and Behavioral Links in Autism Differ Between Default, Salience, and Executive Networks

Autism spectrum disorder (ASD) is characterized by atypical brain network organization, but findings have been inconsistent. While methodological and maturational factors have been considered, the network specificity of connectivity abnormalities remains incompletely understood. We investigated intrinsic functional connectivity (iFC) for four "core" functional networks-default-mode (DMN), salience (SN), and left (lECN) and right executive control (rECN). Resting-state functional MRI data from 75 children and adolescents (37 ASD, 38 typically developing [TD]) were included. Functional connectivity within and between networks was analyzed for regions of interest (ROIs) and whole brain, compared between groups, and correlated with behavioral scores. ROI analyses showed overconnectivity (ASD > TD), especially between DMN and ECN. Whole-brain results were mixed. While predominant overconnectivity was found for DMN (posterior cingulate seed) and rECN (right inferior parietal seed), predominant underconnectivity was found for SN (right anterior insula seed) and lECN (left inferior parietal seed). In the ASD group, reduced SN integrity was associated with sensory and sociocommunicative symptoms. In conclusion, atypical connectivity in ASD is network-specific, ranging from extensive overconnectivity (DMN, rECN) to extensive underconnectivity (SN, lECN). Links between iFC and behavior differed between groups. Core symptomatology in the ASD group was predominantly related to connectivity within the salience network.

Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity

STUDY OBJECTIVES

We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN).

METHODS

Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age).

RESULTS

cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST.

CONCLUSIONS

This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive performance.

On the origins of the 'global signal' determined by functional magnetic resonance imaging in the resting state

OBJECTIVE

Functional magnetic resonance imaging blood oxygen level dependent (BOLD) determinations of correlations between 'resting-state' neuronal activity in different regions of cortex have generated much interest. Determination of these correlations requires regressing out signals that are correlated in all parts of the cortex and are taken to be artefactual, such as those due to movement, respiration and cardiovascular activity. However when these are removed there still remains a 'global signal' (GS), which is taken to be of unknown physiological origin, and is regressed out by some researchers but not by others.

APPROACH

We have investigated the origin of this GS using cortical models consisting of coupled networks of modules representing regions of interest.

MAIN RESULTS

We show that the GS has an amplitude that is linearly related to the average correlation between the modules/voxels in the network over a large range of such correlations. The GS arises as a consequence of feedback between the modules/voxels leading to correlations in their BOLD signals. Given the relationship between the GS and the average correlations it might be anticipated that regressing out the GS during preprocessing will significantly modify the correlations subsequently determined. This is shown to be the case when comparing the connections of individual modules with that predicted by the correlations.

SIGNIFICANCE

The present model shows that such correlations can arise as a consequence of the intermodular feedback connectivity without recourse to imposing a GS independent of the connectivity. Our model indicates that the GS reflects the extent of feedback pathways provided by the intermodular/inter-regional connections and hence the average correlation between modules or regions of cortex. However the model has not been used to elucidate the possible contributions of a GS independent of the connectivity, which might indeed contribute to the GS of the cortex.

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates

Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI). This coupled activity is conveniently expressed using covariance, but this measure fails to distinguish between direct and indirect effects. A popular alternative that addresses this issue is partial correlation, which regresses out the signal of potentially confounding variables, resulting in a measure that reveals only direct connections. Importantly, provided the data are normally distributed, if two variables are conditionally independent given all other variables, their respective partial correlation is zero. In this paper, we propose a probabilistic generative model that allows us to estimate functional connectivity in terms of both partial correlations and a graph representing conditional independencies. Simulation results show that this methodology is able to outperform the graphical LASSO, which is the de facto standard for estimating partial correlations. Furthermore, we apply the model to estimate functional connectivity for twenty subjects using resting-state fMRI data. Results show that our model provides a richer representation of functional connectivity as compared to considering partial correlations alone. Finally, we demonstrate how our approach can be extended in several ways, for instance to achieve data fusion by informing the conditional independence graph with data from probabilistic tractography. As our Bayesian formulation of functional connectivity provides access to the posterior distribution instead of only to point estimates, we are able to quantify the uncertainty associated with our results. This reveals that while we are able to infer a clear backbone of connectivity in our empirical results, the data are not accurately described by simply looking at the mode of the distribution over connectivity. The implication of this is that deterministic alternatives may misjudge connectivity results by drawing conclusions from noisy and limited data.

Significant neuroscientific effort is devoted to elucidating functional connectivity between spatially segregated brain regions. This requires that we are able to quantify the degree of dependence between the signals of different areas. Yet how this must be accomplished—using which measures, each with their own limitations and interpretations—is far from a trivial task. One frequently advocated metric for functional connectivity is partial correlation, which is related to conditional independence: if two regions are independent, conditioned on all other regions, then their partial correlation is zero, assuming Gaussian data. Here, we use a probabilistic generative model to describe the relationship between functional connectivity and conditional independence. We apply this Bayesian approach to reveal functional connectivity between subcortical areas, and in addition we propose different variants of the generative model for connectivity. In the first, we address how a Bayesian formulation of connectivity allows for integration with other imaging modalities, resulting in data fusion. Secondly, we show how prior constraints can be incorporated in our estimates of connectivity.

Temporal dynamics of stress-induced alternations of intrinsic amygdala connectivity and neuroendocrine levels

Stress-induced changes in functional brain connectivity have been linked to the etiology of stress-related disorders. Resting state functional connectivity (rsFC) is especially informative in characterizing the temporal trajectory of glucocorticoids during stress adaptation. Using the imaging Maastricht Acute Stress Test (iMAST), we induced acute stress in 39 healthy volunteers and monitored the neuroendocrine stress levels during three runs of resting state functional magnetic resonance imaging (rs-fMRI): before (run 1), immediately following (run 2), and 30 min after acute stress (run 3). The iMAST resulted in strong increases in cortisol levels. Whole-brain analysis revealed that acute stress (run 2 - 1) was characterized by changes in connectivity of the amygdala with the ventrolateral prefrontal cortex (vlPFC), ventral posterior cingulate cortex (PCC), cuneus, parahippocampal gyrus, and culmen. Additionally, cortisol responders were characterized by enhanced amygdala - medial prefrontal cortex (mPFC) connectivity. Stress recovery (run 3 - 2) was characterized by altered amygdala connectivity with the dorsolateral prefrontal cortex (dlPFC), ventral and dorsal anterior cingulate cortex (ACC), anterior hippocampal complex, cuneus, and presupplementary motor area (preSMA). Opposite to non-responders, cortisol responders were characterized by enhanced amygdala connectivity with the anterior hippocampal complex and parahippocampal gyrus, and reduced connectivity with left dlPFC, dACC, and culmen during early recovery. Acute stress responding and recovery are thus associated with changes in the functional connectivity of the amygdala network. Our findings show that these changes may be regulated via stress-induced neuroendocrine levels. Defining stress-induced neuronal network changes is pertinent to developing treatments that target abnormal neuronal activity.

Reduced functional connectivity between salience and visual networks in migraine with aura

BACKGROUND

Migraine with visual aura (MA) is associated with distinct visual disturbances preceding migraine attacks, but shares other visual deficits in between attacks with migraine without aura (MO). Here, we seek to determine if abnormalities specific to interictal MA patients exist in functional brain connectivity of intrinsic cognitive networks. In particular, these networks are involved in top-down modulation of visual processing.

METHODS

Using resting-state functional magnetic resonance imaging, whole-brain functional connectivity maps were derived from seeds placed in the anterior insula and the middle frontal gyrus, key nodes of the salience and dorsal attention networks, respectively. Twenty-six interictal MA patients were compared with 26 matched MO patients and 26 healthy matched controls.

RESULTS

The major findings were: connectivity between the anterior insula and occipital areas, including area V3A, was reduced in MA but not in MO. Connectivity changes between the anterior insula and occipital areas further correlated with the headache severity in MA only.

CONCLUSIONS

The unique pattern of connectivity changes found in interictal MA patients involved area V3A, an area previously implicated in aura generation. Hypoconnectivity to this and other occipital regions may either represent a compensatory response to occipital dysfunctions or predispose MA patients to the development of aura.

Dreaming and the default network: A review, synthesis, and counterintuitive research proposal

This article argues that the default network, augmented by secondary visual and sensorimotor cortices, is the likely neural correlate of dreaming. This hypothesis is based on a synthesis of work on dream content, the findings on the contents and neural correlates of mind-wandering, and the results from EEG and neuroimaging studies of REM sleep. Relying on studies in the 1970s that serendipitously discovered episodes of dreaming during waking mind-wandering, this article presents the seemingly counterintuitive hypothesis that the neural correlates for dreaming could be further specified in the process of carrying out EEG/fMRI studies of mind-wandering and default network activity. This hypothesis could be tested by asking participants for experiential reports during moments of differentially high levels of default network activation, as indicated by mixed EEG/fMRI criteria. Evidence from earlier EEG/fMRI studies of mind-wandering and from laboratory studies of dreaming during the sleep-onset process is used to support the argument.

The pediatric template of brain perfusion

Magnetic resonance imaging (MRI) captures the dynamics of brain development with multiple modalities that quantify both structure and function. These measurements may yield valuable insights into the neural patterns that mark healthy maturation or that identify early risk for psychiatric disorder. The Pediatric Template of Brain Perfusion (PTBP) is a free and public neuroimaging resource that will help accelerate the understanding of childhood brain development as seen through the lens of multiple modality neuroimaging and in relation to cognitive and environmental factors. The PTBP uses cross-sectional and longitudinal MRI to quantify cortex, white matter, resting state functional connectivity and brain perfusion, as measured by Arterial Spin Labeling (ASL), in 120 children 7-18 years of age. We describe the PTBP and show, as a demonstration of validity, that global summary measurements capture the trajectories that demarcate critical turning points in brain maturation. This novel resource will allow a more detailed understanding of the network-level, structural and functional landmarks that are obtained during normal adolescent brain development.

Studying brain organization via spontaneous fMRI signal

In recent years, some substantial advances in understanding human (and nonhuman) brain organization have emerged from a relatively unusual approach: the observation of spontaneous activity, and correlated patterns in spontaneous activity, in the "resting" brain. Most commonly, spontaneous neural activity is measured indirectly via fMRI signal in subjects who are lying quietly in the scanner, the so-called "resting state." This Primer introduces the fMRI-based study of spontaneous brain activity, some of the methodological issues active in the field, and some ways in which resting-state fMRI has been used to delineate aspects of area-level and supra-areal brain organization.

Functional ultrasound imaging of intrinsic connectivity in the living rat brain with high spatiotemporal resolution

Long-range coherences in spontaneous brain activity reflect functional connectivity. Here we propose a novel, highly resolved connectivity mapping approach, using ultrafast functional ultrasound (fUS), which enables imaging of cerebral microvascular haemodynamics deep in the anaesthetized rodent brain, through a large thinned-skull cranial window, with pixel dimensions of 100 μm × 100 μm in-plane. The millisecond-range temporal resolution allows unambiguous cancellation of low-frequency cardio-respiratory noise. Both seed-based and singular value decomposition analysis of spatial coherences in the low-frequency (<0.1 Hz) spontaneous fUS signal fluctuations reproducibly report, at different coronal planes, overlapping high-contrast, intrinsic functional connectivity patterns. These patterns are similar to major functional networks described in humans by resting-state fMRI, such as the lateral task-dependent network putatively anticorrelated with the midline default-mode network. These results introduce fUS as a powerful novel neuroimaging method, which could be extended to portable systems for three-dimensional functional connectivity imaging in awake and freely moving rodents.

Unravelling the intrinsic functional organization of the human striatum: a parcellation and connectivity study based on resting-state FMRI

As the main input hub of the basal ganglia, the striatum receives projections from the cerebral cortex. Many studies have provided evidence for multiple parallel corticostriatal loops based on the structural and functional connectivity profiles of the human striatum. A recent resting-state fMRI study revealed the topography of striatum by assigning each voxel in the striatum to its most strongly correlated cortical network among the cognitive, affective, and motor networks. However, it remains unclear what patterns of striatal parcellation would result from performing the clustering without subsequent assignment to cortical networks. Thus, we applied unsupervised clustering algorithms to parcellate the human striatum based on its functional connectivity patterns to other brain regions without any anatomically or functionally defined cortical targets. Functional connectivity maps of striatal subdivisions, identified through clustering analyses, were also computed. Our findings were consistent with recent accounts of the functional distinctions of the striatum as well as with recent studies about its functional and anatomical connectivity. For example, we found functional connections between dorsal and ventral striatal clusters and the areas involved in cognitive and affective processes, respectively, and between rostral and caudal putamen clusters and the areas involved in cognitive and motor processes, respectively. This study confirms prior findings, showing similar striatal parcellation patterns between the present and prior studies. Given such striking similarity, it is suggested that striatal subregions are functionally linked to cortical networks involving specific functions rather than discrete portions of cortical regions. Our findings also demonstrate that the clustering of functional connectivity patterns is a reliable feature in parcellating the striatum into anatomically and functionally meaningful subdivisions. The striatal subdivisions identified here may have important implications for understanding the relationship between corticostriatal dysfunction and various neurodegenerative and psychiatric disorders.

The impact of MIR137 on dorsolateral prefrontal-hippocampal functional connectivity in healthy subjects

A recent mega-analysis combining genome-wide association study data revealed that a variant of microRNA 137 (MIR137) exhibits the most significant association with schizophrenia. Other biological evidence also consistently suggests that MIR137 may have a pivotal role in the pathogenesis of schizophrenia. However, the underlying neural mechanism remains unclear. As the disrupted dorsolateral prefrontal cortex (DLPFC) coupling with the hippocampal formation (HF) has been widely observed in schizophrenia patients, DLPFC-HF dysconnectivity can therefore be thought of as a pivotal intermediate phenotype that links genetic variants of psychiatric risk genes to schizophrenia. This study used resting-state functional magnetic resonance imaging to test whether the MIR137 variant (rs1625579) impacts DLPFC-HF functional connectivity and cognitive performance in 290 young, healthy Han Chinese individuals. To identify functional connectivity between DLPFC and HF, a seed-based functional connectivity analysis was used. The association between DLPFC-HF connectivity and working memory performance was further examined in individuals with different MIR137 genotypes. The individuals who are homozygous for the MIR137 risk allele (TT), which confers a high risk for schizophrenia, exhibited significantly different DLPFC-HF functional connectivity compared with TG individuals. Moreover, the DLPFC-HF connectivity could predict the working memory performance in MIR137 TG individuals, but not in TT individuals. The current findings obtained in a large sample of healthy participants identified potential neural mechanisms linking MIR137 with the risk of developing schizophrenia via the intermediate phenotype of DLPFC-HF connectivity.

Brain functional connectivity changes in children that differ in impulsivity temperamental trait

Impulsivity is a core personality trait forming part of normal behavior and contributing to adaptive functioning. However, in typically developing children, altered patterns of impulsivity constitute a risk factor for the development of behavioral problems. Since both pathological and non-pathological states are commonly characterized by continuous transitions, we used a correlative approach to investigate the potential link between personality and brain dynamics. We related brain functional connectivity of typically developing children, measured with magnetic resonance imaging at rest, with their impulsivity scores obtained from a questionnaire completed by their parents. We first looked for areas within the default mode network (DMN) whose functional connectivity might be modulated by trait impulsivity. Then, we calculated the functional connectivity among these regions and the rest of the brain in order to assess if impulsivity trait altered their relationships. We found two DMN clusters located at the posterior cingulate cortex and the right angular gyrus which were negatively correlated with impulsivity scores. The whole-brain correlation analysis revealed the classic network of correlating and anti-correlating areas with respect to the DMN. The impulsivity trait modulated such pattern showing that the canonical anti-phasic relation between DMN and action-related network was reduced in high impulsive children. These results represent the first evidence that the impulsivity, measured as personality trait assessed through parents' report, exerts a modulatory influence over the functional connectivity of resting state brain networks in typically developing children. The present study goes further to connect developmental approaches, mainly based on data collected through the use of questionnaires, and behavioral neuroscience, interested in how differences in brain structure and functions reflect in differences in behavior.