Global functional connectivity deficits in schizophrenia depend on behavioral state

Neurobiological Underpinnings of Hyperarousal in Depression: A Comprehensive Review

Patients with major depressive disorder (MDD) exhibit an abnormal physiological arousal pattern known as hyperarousal, which may contribute to their depressive symptoms. However, the neurobiological mechanisms linking this abnormal arousal to depressive symptoms are not yet fully understood. In this review, we summarize the physiological and neural features of arousal, and review the literature indicating abnormal arousal in depressed patients. Evidence suggests that a hyperarousal state in depression is characterized by abnormalities in sleep behavior, physiological (e.g., heart rate, skin conductance, pupil diameter) and electroencephalography (EEG) features, and altered activity in subcortical (e.g., hypothalamus and locus coeruleus) and cortical regions. While recent studies highlight the importance of subcortical-cortical interactions in arousal, few have explored the relationship between subcortical-cortical interactions and hyperarousal in depressed patients. This gap limits our understanding of the neural mechanism through which hyperarousal affects depressive symptoms, which involves various cognitive processes and the cerebral cortex. Based on the current literature, we propose that the hyperconnectivity in the thalamocortical circuit may contribute to both the hyperarousal pattern and depressive symptoms. Future research should investigate the relationship between thalamocortical connections and abnormal arousal in depression, and explore its implications for non-invasive treatments for depression.

Beyond noise to function: reframing the global brain activity and its dynamic topography

How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.

Analyses of n-back high-load working memory in early Parkinson's disease: An fMRI study

BACKGROUND

Cognitive impairment is common in Parkinson's disease (PD). Working memory is a group of basic processes that is part of many cognitive operations (from complex decision-making to selective attention). To date, there is not sufficient research on working memory impairment in early PD patients, especially patients under high load.

AIM

The aim of this study was to observe the activation of brain regions in early PD patients during verbal high-load cognitive tasks and to explore the correlation between cognitive behavior indexes and gray matter areas of brain structure.

METHODS

A total of 45 participants were included: 15 healthy controls and 30 PD patients without dementia. They underwent BOLD imaging during a digital N-back working memory task in a 3 T scanner, and their high-resolution T1-weighted three-dimensional brain anatomical images were collected. The SPM12 standard method was used to generate a comparison map with 2 plus 3-back greater than 0-back, and with age, sex, levodopa equivalent daily dose (LEDD) and education as covariables, and multiple regression analysis was conducted to analyze the correlation between MOCA, MES and brain gray matter volume.

RESULTS

Compared with the control group, the frontoparietal region was significantly activated in patients with PD under the background of high working memory burden. In addition, there was a widespread area correlation between the brain structure and basic cognitive function.

CONCLUSIONS

The frontoparietal cortex is the core hub of working memory, and there may be a compensation mechanism in PD patients without dementia with a high-load of working memory.

Youth with Down syndrome display widespread increased functional connectivity during rest

Studies of resting-state functional connectivity in young people with Down syndrome (DS) have yielded conflicting results. Some studies have found increased connectivity while others have found a mix of increased and decreased connectivity. No studies have examined whole-brain connectivity at the voxel level in youth with DS during an eyes-open resting-state design. Additionally, no studies have examined the relationship between connectivity and network selectivity in youth with DS. Thus, the current study sought to fill this gap in the literature. Nineteen youth with DS (Mage = 16.5; range 7-23; 13 F) and 33 typically developing (TD) youth (Mage = 17.5; range 6-24; 18 F), matched on age and sex, completed a 5.25-min eyes-open resting-state fMRI scan. Whole-brain functional connectivity (average Pearson correlation of each voxel with every other voxel) was calculated for each individual and compared between groups. Network selectivity was then calculated and correlated with functional connectivity for the DS group. Results revealed that whole-brain functional connectivity was significantly higher in youth with DS compared to TD controls in widespread regions throughout the brain. Additionally, participants with DS had significantly reduced network selectivity compared to TD peers, and selectivity was significantly related to connectivity in all participants. Exploratory behavioral analyses revealed that regions showing increased connectivity in DS predicted Verbal IQ, suggesting differences in connectivity may be related to verbal abilities. These results indicate that network organization is disrupted in youth with DS such that disparate networks are overly connected and less selective, suggesting a potential target for clinical interventions.

fMRI-based detection of alertness predicts behavioral response variability

Levels of alertness are closely linked with human behavior and cognition. However, while functional magnetic resonance imaging (fMRI) allows for investigating whole-brain dynamics during behavior and task engagement, concurrent measures of alertness (such as EEG or pupillometry) are often unavailable. Here, we extract a continuous, time-resolved marker of alertness from fMRI data alone. We demonstrate that this fMRI alertness marker, calculated in a short pre-stimulus interval, captures trial-to-trial behavioral responses to incoming sensory stimuli. In addition, we find that the prediction of both EEG and behavioral responses during the task may be accomplished using only a small fraction of fMRI voxels. Furthermore, we observe that accounting for alertness appears to increase the statistical detection of task-activated brain areas. These findings have broad implications for augmenting a large body of existing datasets with information about ongoing arousal states, enriching fMRI studies of neural variability in health and disease.

Enhanced inter-regional coupling of neural responses and repetition suppression provide separate contributions to long-term behavioral priming

Stimulus identification commonly improves with repetition over long delays ("repetition priming"), whereas neural activity commonly decreases ("repetition suppression"). Multiple models have been proposed to explain this brain-behavior relationship, predicting alterations in functional and/or effective connectivity (Synchrony and Predictive Coding models), in the latency of neural responses (Facilitation model), and in the relative similarity of neural representations (Sharpening model). Here, we test these predictions with fMRI during overt and covert naming of repeated and novel objects. While we find partial support for predictions of the Facilitation and Sharpening models in the left fusiform gyrus and left frontal cortex, the data were most consistent with the Synchrony model, with increased coupling between right temporoparietal and anterior cingulate cortex for repeated objects that correlated with priming magnitude across participants. Increased coupling and repetition suppression varied independently, each explaining unique variance in priming and requiring modifications of all current models.

Time to re-engage psychiatric drug discovery by strengthening confidence in preclinical psychopharmacology

BACKGROUND

There is urgent need for new medications for psychiatric disorders. Mental illness is expected to become the leading cause of disability worldwide by 2030. Yet, the last two decades have seen the pharmaceutical industry withdraw from psychiatric drug discovery after costly late-stage trial failures in which clinical efficacy predicted pre-clinically has not materialised, leading to a crisis in confidence in preclinical psychopharmacology.

METHODS

Based on a review of the relevant literature, we formulated some principles for improving investment in translational neuroscience aimed at psychiatric drug discovery.

RESULTS

We propose the following 8 principles that could be used, in various combinations, to enhance CNS drug discovery: (1) consider incorporating the NIMH Research Domain Criteria (RDoC) approach; (2) engage the power of translational and systems neuroscience approaches; (3) use disease-relevant experimental perturbations; (4) identify molecular targets via genomic analysis and patient-derived pluripotent stem cells; (5) embrace holistic neuroscience: a partnership with psychoneuroimmunology; (6) use translational measures of neuronal activation; (7) validate the reproducibility of findings by independent collaboration; and (8) learn and reflect. We provide recent examples of promising animal-to-human translation of drug discovery projects and highlight some that present re-purposing opportunities.

CONCLUSIONS

We hope that this review will re-awaken the pharma industry and mental health advocates to the opportunities for improving psychiatric pharmacotherapy and so restore confidence and justify re-investment in the field.

Multivariate patterns of gray matter volume in thalamic nuclei are associated with positive schizotypy in healthy individuals

BACKGROUND

Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case-control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.

METHODS

Following a previous report, we used Random Forests to predict diagnosis in a case-control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.

RESULTS

Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.

CONCLUSIONS

These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.

Disordered directional brain network interactions during learning dynamics in schizophrenia revealed by multivariate autoregressive models

Directional network interactions underpin normative brain function in key domains including associative learning. Schizophrenia (SCZ) is characterized by altered learning dynamics, yet dysfunctional directional functional connectivity (dFC) evoked during learning is rarely assessed. Here, nonlinear learning dynamics were induced using a paradigm alternating between conditions (Encoding and Retrieval). Evoked fMRI time series data were modeled using multivariate autoregressive (MVAR) models, to discover dysfunctional direction interactions between brain network constituents during learning stages (Early vs. Late), and conditions. A functionally derived subnetwork of coactivated (healthy controls [HC] ∩ SCZ] nodes was identified. MVAR models quantified directional interactions between pairs of nodes, and coefficients were evaluated for intergroup differences (HC ≠ SCZ). In exploratory analyses, we quantified statistical effects of neuroleptic dosage on performance and MVAR measures. During Early Encoding, SCZ showed reduced dFC within a frontal–hippocampal–fusiform network, though during Late Encoding reduced dFC was associated with pathways toward the dorsolateral prefrontal cortex (dlPFC). During Early Retrieval, SCZ showed increased dFC in pathways to and from the dorsal anterior cingulate cortex, though during Late Retrieval, patients showed increased dFC in pathways toward the dlPFC, but decreased dFC in pathways from the dlPFC. These discoveries constitute novel extensions of our understanding of task‐evoked dysconnection in schizophrenia and motivate understanding of the directional aspect of the dysconnection in schizophrenia. Disordered directionality should be investigated using computational psychiatric approaches that complement the MVAR method used in our work.

The boundaries of the self: The sense of agency across different sensorimotor aspects

The sense of agency (SoA) is the sensation of control over our actions. SoA is thought to rely mainly upon the comparison of predictions regarding the sensory outcomes of one's actions and the actual sensory outcomes. Previous studies have shown that when a discrepancy is introduced between one's actions and the sensory feedback, the reported SoA is reduced. Experimental manipulations of SoA are typically induced by introducing a discrepancy between a motor action and visual feedback of a specific sensorimotor aspect. For example, introducing a delay or a spatial deviation between the action and its sensory feedback reduces SoA. However, it is yet unclear whether the sensorimotor prediction processes underlying SoA are related between different aspects. Here in one exploratory and one preregistered experiment we tested the sense of agency across temporal, spatial, and anatomical aspects in a within-subject design. Using a novel virtual-reality task allowing the manipulation of the visual feedback of a motor action across different aspects, we show that the sensitivity of agency is different across aspects, agency judgments are correlated across aspects within subjects and bias toward attributing the viewed action to the self or to an external source is correlated as well. Our results suggest that sensorimotor prediction mechanisms underlying SoA are related between different aspects and that people have a predisposition for the directionality of agency judgments. These findings reveal the psychophysical attributes of SoA across sensorimotor aspects. Data and preregistration are available at https://goo.gl/SkbGrb.

A Dynamic Multi-Reduction Algorithm for Brain Functional Connection Pathways Analysis

Revealing brain functional connection pathways is of great significance in understanding the cognitive mechanism of the brain. In this paper, we present a novel rough set based dynamic multi-reduction algorithm (DMRA) to analyze brain functional connection pathways. First, a binary discernibility matrix is introduced to obtain a reduction, and a reduction equivalence theorem is proposed and proved to verify the feasibility of reduction algorithm. Based on this idea, we propose a dynamic single-reduction algorithm (DSRA) to obtain a seed reduction, in which two dynamical acceleration mechanisms are presented to reduce the size of the binary discernibility matrix dynamically. Then, the dynamic multi-reduction algorithm is proposed, and multi-reductions can be obtained by replacing the non-core attributes in seed reduction. Comparative performance experiments were carried out on the UCI datasets to illustrate the superiority of DMRA in execution time and classification accuracy. A memory cognitive experiment was designed and three brain functional connection pathways were successfully obtained from brain functional Magnetic Resonance Imaging (fMRI) by employing the proposed DMRA. The theoretical and empirical results both illustrate the potentials of DMRA for brain functional connection pathways analysis.

Sex Differences in Resting-State Functional Connectivity of the Cerebellum in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is more prevalent in males than females, but the underlying neurobiology of this sex bias remains unclear. Given its involvement in ASD, its role in sensorimotor, cognitive, and socio-affective processes, and its developmental sensitivity to sex hormones, the cerebellum is a candidate for understanding this sex difference. The current study used resting-state functional magnetic resonance imaging (fMRI) to investigate sex-dependent differences in cortico-cerebellar organization in ASD. We collected resting-state fMRI scans from 47 females (23 ASD, 24 controls) and 120 males (56 ASD, 65 controls). Using a measure of global functional connectivity (FC), we ran a linear mixed effects analysis to determine whether there was a sex-by-diagnosis interaction in resting-state FC. Subsequent seed-based analyses from the resulting clusters were run to clarify the global connectivity effects. Two clusters in the bilateral cerebellum exhibited a diagnosis-by-sex interaction in global connectivity. These cerebellar clusters further showed a pattern of interaction with regions in the cortex, including bilateral fusiform, middle occipital, middle frontal, and precentral gyri, cingulate cortex, and precuneus. Post hoc tests revealed a pattern of cortico-cerebellar hyperconnectivity in ASD females and a pattern of hypoconnectivity in ASD males. Furthermore, cortico-cerebellar FC in females more closely resembled that of control males than that of control females. These results shed light on the sex-specific pathophysiology of ASD and are indicative of potentially divergent neurodevelopmental trajectories for each sex. This sex-dependent, aberrant cerebellar connectivity in ASD might also underlie some of the motor and/or socio-affective difficulties experienced by members of this population, but the symptomatic correlate(s) of these brain findings remain unknown.

Clinical Trial Registration: www.ClinicalTrials.gov, NIH Clinical Study Protocol 10-M-0027 (ZIA MH002920-09) identifier #NCT01031407

A framework for offline evaluation and optimization of real-time algorithms for use in neurofeedback, demonstrated on an instantaneous proxy for correlations

Interest in real-time fMRI neurofeedback has grown exponentially over the past few years, both for use as a basic science research tool, and as part of the search for novel clinical interventions for neurological and psychiatric illnesses. In order to expand the range of questions which can be addressed with this tool however, new neurofeedback methods must be developed, going beyond feedback of activations in a single region. These new methods, several of which have already been proposed, are by their nature complex, involving many possible parameters. Here we suggest a framework for evaluating and optimizing algorithms for use in a real-time setting, before beginning the neurofeedback experiment, by offline simulations of algorithm output using a previously collected dataset. We demonstrate the application of this framework on the instantaneous proxy for correlations which we developed for training connectivity between different network nodes, identify the optimal parameters for use with this algorithm, and compare it to more traditional correlation methods. We also examine the effects of advanced imaging techniques, such as multi-echo acquisition, and the integration of these into the real-time processing stream.

Overt social interaction and resting state in young adult males with autism: core and contextual neural features

Conversation is an important and ubiquitous social behaviour. Individuals with autism spectrum disorder (autism) without intellectual disability often have normal structural language abilities but deficits in social aspects of communication like pragmatics, prosody, and eye contact. Previous studies of resting state activity suggest that intrinsic connections among neural circuits involved with social processing are disrupted in autism, but to date no neuroimaging study has examined neural activity during the most commonplace yet challenging social task: spontaneous conversation. Here we used functional MRI to scan autistic males (n = 19) without intellectual disability and age- and IQ-matched typically developing control subjects (n = 20) while they engaged in a total of 193 face-to-face interactions. Participants completed two kinds of tasks: conversation, which had high social demand, and repetition, which had low social demand. Autistic individuals showed abnormally increased task-driven interregional temporal correlation relative to controls, especially among social processing regions and during high social demand. Furthermore, these increased correlations were associated with parent ratings of participants' social impairments. These results were then compared with previously-acquired resting state data (56 autism, 62 control subjects). While some interregional correlation levels varied by task or rest context, others were strikingly similar across both task and rest, namely increased correlation among the thalamus, dorsal and ventral striatum, somatomotor, temporal and prefrontal cortex in the autistic individuals, relative to the control groups. These results suggest a basic distinction. Autistic cortico-cortical interactions vary by context, tending to increase relative to controls during task and decrease during test. In contrast, striato- and thalamocortical relationships with socially engaged brain regions are increased in both task and rest, and may be core to the condition of autism.

Bilateral functional connectivity at rest predicts apraxic symptoms after left hemisphere stroke

Increasing evidence indicates that focal lesions following stroke cause alterations in connectivity among functional brain networks. Functional connectivity between hemispheres has been shown to be particularly critical for predicting stroke-related behavioral deficits and recovery of motor function and attention. Much less is known, however, about the relevance of interhemispheric functional connectivity for cognitive abilities like praxis that rely on strongly lateralized brain networks. In the current study, we examine correlations between symptoms of apraxia-a disorder of skilled action that cannot be attributed to lower-level sensory or motor impairments-and spontaneous, resting brain activity in functional MRI in chronic left hemisphere stroke patients and neurologically-intact control participants. Using a data-driven approach, we identified 32 regions-of-interest in which pairwise functional connectivity correlated with two distinct measures of apraxia, even when controlling for age, head motion, lesion volume, and other artifacts: overall ability to pantomime the typical use of a tool, and disproportionate difficulty pantomiming the use of tools associated with different, competing use and grasp-to-move actions (e.g., setting a kitchen timer versus picking it up). Better performance on both measures correlated with stronger interhemispheric functional connectivity. Relevant regions in the right hemisphere were often homologous to left hemisphere areas associated with tool use and action. Additionally, relative to overall pantomime accuracy, disproportionate difficulty pantomiming the use of tools associated with competing use and grasp actions was associated with weakened functional connectivity among a more strongly left-lateralized and peri-Sylvian set of brain regions. Finally, patient performance on both measures of apraxia was best predicted by a model that incorporated information about lesion location and functional connectivity, and functional connectivity continued to explain unique variance in behavior even after accounting for lesion loci. These results indicate that interhemispheric functional connectivity is relevant even for a strongly lateralized cognitive ability like praxis and emphasize the importance of the right hemisphere in skilled action.

Effects of perinatal blood pressure on maternal brain functional connectivity

Perinatal hypertensive disorder including pre-eclampsia is a systemic syndrome that occurs in 3–5% of pregnant women. It can result in various degrees of brain damage. A recent study suggested that even gestational hypertension without proteinuria can cause cardiovascular or cognitive impairments later in life. We hypothesized that perinatal hypertension affects the brain functional connectivity (FC) regardless of the clinical manifestation of brain functional impairment. In the present study, we analyzed regional global connectivity (rGC) strength (mean cross-correlation coefficient between a brain region and all other regions) using resting-state functional magnetic resonance imaging to clarify brain FC changes associated with perinatal blood pressure using data from 16 women with a normal pregnancy and 21 pregnant women with pre-eclampsia. The rGC values in the bilateral orbitofrontal gyri were negatively correlated with diastolic blood pressure (dBP), which could not be explained by other pre-eclampsia symptoms. The strength of FC seeding at the left orbitofrontal gyrus was negatively correlated with dBP in the anterior cingulate gyri and right middle frontal gyrus. These results suggest that dBP elevation during pregnancy can affect the brain FC. Since FC is known to be associated with various brain functions and diseases, our findings are important for elucidating the neural correlate of cognitive impairments related to hypertension in pregnancy.

Attenuated resting-state functional connectivity in patients with childhood- and adult-onset schizophrenia

BACKGROUND

Childhood-onset schizophrenia (COS) is a rare, severe form of the adult-onset disorder (AOS). Our previous resting-state fMRI study identified attenuated functional connectivity in COS compared with controls. Here, we ask whether COS and AOS patients and their siblings exhibit similar abnormalities of functional connectivity.

METHODS

A whole-brain, data-driven approach was used to assess resting-state functional connectivity differences in COS (patients/siblings/controls, n: 26/28/33) and AOS (n: 19/28/30). There were no significant differences in age, sex, or head motion across groups in each dataset and as designed, the COS dataset has a significantly lower age than the AOS.

RESULTS

Both COS and AOS patients showed decreased functional connectivity relative to controls among a wide set of brain regions (P<0.05, corrected), but their siblings did not. Decreased connectivity in COS and AOS patients showed no amplitude differences and was not modulated by age-at-onset or medication doses. Cluster analysis revealed that these regions fell into two large-scale networks: one sensorimotor network and one centered on default-mode network regions, but including higher-order cognitive areas only in COS. Decreased connectivity between these two networks was notable (P<0.05, corrected) for both patient groups.

CONCLUSIONS

A shared pattern of attenuated functional connectivity was found in COS and AOS, supporting the continuity of childhood-onset and adult-onset schizophrenia. Connections were altered between sensorimotor areas and default-mode areas in both COS and AOS, suggesting potential abnormalities in processes of self-monitoring and sensory prediction. The absence of substantial dysconnectivity in siblings indicates that attenuation is state-related.

Rethinking Body Ownership in Schizophrenia: Experimental and Meta-analytical Approaches Show no Evidence for Deficits

Schizophrenia is a severe psychiatric disorder, in which patients experience an abnormal sense of self. While deficits in sensorimotor self-representation (agency) are well documented in schizophrenia, less is known about other aspects of bodily self-representation (body ownership). Here, we tested a large cohort (N = 59) of chronic schizophrenia patients and matched controls (N = 30) on a well-established body illusion paradigm, the Full Body Illusion (FBI). In this paradigm, changes in body ownership are induced through prolonged multisensory stimulation, in which participants are stroked on their back while seeing the stroking on the back of a virtual body. When the felt and seen stroking are synchronous, participants typically feel higher identification with the seen body as well as a drift in self-location towards it. However, when the stroking is asynchronous, no such changes occur. Our results show no evidence for abnormal body ownership in schizophrenia patients. A meta-analysis of previous work corroborates this result. Thus, while schizophrenia patients may be impaired in the sense of agency, their multisensory bodily self-representation, as tested here, seems to be unaffected by the illness.

Spontaneously Emerging Patterns in Human Visual Cortex Reflect Responses to Naturalistic Sensory Stimuli

In the absence of stimulus or task, the cortex spontaneously generates rich and consistent functional connectivity patterns (termed resting state networks) which are evident even within individual cortical areas. We and others have previously hypothesized that habitual cortical network activations during daily life contribute to the shaping of these connectivity patterns. Here we tested this hypothesis by comparing, using blood oxygen level-dependent-functional magnetic resonance imaging, the connectivity patterns that spontaneously emerge during rest in retinotopic visual areas to the patterns generated by naturalistic visual stimuli (repeated movie segments). These were then compared with connectivity patterns produced by more standard retinotopic mapping stimuli (polar and eccentricity mapping). Our results reveal that the movie-driven patterns were significantly more similar to the spontaneously emerging patterns, compared with the connectivity patterns of either eccentricity or polar mapping stimuli. Intentional visual imagery of naturalistic stimuli was unlikely to underlie these results, since they were duplicated when participants were engaged in an auditory task. Our results suggest that the connectivity patterns that appear during rest better reflect naturalistic activations rather than controlled, artificially designed stimuli. The results are compatible with the hypothesis that the spontaneous connectivity patterns in human retinotopic areas reflect the statistics of cortical coactivations during natural vision.

Altered intrinsic and extrinsic connectivity in schizophrenia

Schizophrenia is a disorder characterized by functional dysconnectivity among distributed brain regions. However, it is unclear how causal influences among large-scale brain networks are disrupted in schizophrenia. In this study, we used dynamic causal modeling (DCM) to assess the hypothesis that there is aberrant directed (effective) connectivity within and between three key large-scale brain networks (the dorsal attention network, the salience network and the default mode network) in schizophrenia during a working memory task. Functional MRI data during an n-back task from 40 patients with schizophrenia and 62 healthy controls were analyzed. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes, we found that intrinsic (within-region) and extrinsic (between-region) effective connectivity involving prefrontal regions were abnormal in schizophrenia. Specifically, in patients (i) inhibitory self-connections in prefrontal regions of the dorsal attention network were decreased across task conditions; (ii) extrinsic connectivity between regions of the default mode network was increased; specifically, from posterior cingulate cortex to the medial prefrontal cortex; (iii) between-network extrinsic connections involving the prefrontal cortex were altered; (iv) connections within networks and between networks were correlated with the severity of clinical symptoms and impaired cognition beyond working memory. In short, this study revealed the predominance of reduced synaptic efficacy of prefrontal efferents and afferents in the pathophysiology of schizophrenia.

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A first use of hierarchical modeling of effective connectivity to characterize large-scale networks in schizophrenia.

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Intrinsic and extrinsic effective connectivity involving prefrontal regions were abnormal in schizophrenia.

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Diagnostic connections could predict the severity of clinical symptoms and cognition in schizophrenia.

Evidence for Functional Networks within the Human Brain's White Matter

Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry.SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human brain. However, most fMRI studies ignored a major part of the brain, the white-matter, discarding signals from it as arising from noise. Here we use resting-state fMRI data from 176 subjects to show that signals from the human white-matter contain meaningful information. We identify 12 functional networks composed of interacting long-distance white-matter tracts. Moreover, we show that these networks are highly correlated to resting-state gray-matter networks, highlighting their functional role. Our findings enable reinterpretation of many existing fMRI datasets, and suggest a new way to explore the white-matter role in cognition and its disturbances in neuropsychiatric disorders.

Task Performance Modulates Functional Connectivity Involving the Dorsolateral Prefrontal Cortex in Patients with Schizophrenia

Previous studies have suggested that patients with schizophrenia and healthy controls exhibit differential activation of and connectivity involving the dorsolateral prefrontal cortex (DLPFC) during working memory tasks, though their findings remain inconsistent. The functional integration perspective further suggests that working memory performance also modulates differences in functional interactions of the DLPFC between patients and controls. To explore this possibility, 45 healthy controls and 45 patients with schizophrenia were recruited to perform a 2-back task during functional magnetic resonance imaging (fMRI). Each group was further divided into two subgroups based on task performance to examine the modulatory effect of performance on functional interactions of the DLPFC, as measured via psychophysiological interaction (PPI) analyses. We observed that, in patients with schizophrenia who exhibited impaired working memory capacity and decreased brain activation/deactivation, functional interactions between the right/left DLPFC and angular cortex were decreased relative to those of healthy controls. Furthermore, we observed an interaction effect of working memory performance and diagnosis on functional connectivity between the right/left DLPFC seed region and posterior regions such as the angular cortex, fusiform gyrus, and middle occipital gyrus. This interaction effect was mainly driven by the negative correlation between functional connectivity and performance in healthy controls, and by the positive correlation in patients with schizophrenia. These results demonstrate the effects of inter-individual differences in working memory performance on functional interactions between the DLPFC and posterior regions in patients with schizophrenia as well as healthy controls, which may shed new light on the neural basis of working memory.

Altered brain connectivity in patients with schizophrenia is consistent across cognitive contexts

BACKGROUND

Schizophrenia has been defined as a dysconnection syndrome characterized by aberrant functional brain connectivity. Using task-based fMRI, we assessed to what extent the nature of the cognitive context may further modulate abnormal functional brain connectivity.

METHODS

We analyzed data matched for motion in patients with schizophrenia and healthy controls who performed 3 different tasks. Tasks 1 and 2 both involved emotional processing and only slighlty differed (incidental encoding v. memory recognition), whereas task 3 was a much different mental rotation task. We conducted a connectome-wide general linear model analysis aimed at identifying context-dependent and independent functional brain connectivity alterations in patients with schizophrenia.

RESULTS

After matching for motion, we included 30 patients with schizophrenia and 30 healthy controls in our study. Abnormal connectivity in patients with schizophrenia followed similar patterns regardless of the degree of similarity between cognitive tasks. Decreased connectivity was most notable in the medial prefrontal cortex, the anterior and posterior cingulate, the temporal lobe, the lobule IX of the cerebellum and the premotor cortex.

LIMITATIONS

A more circumscribed yet significant context-dependent effect might be detected with larger sample sizes or cognitive domains other than emotional and visuomotor processing.

CONCLUSION

The context-independence of functional brain dysconnectivity in patients with schizophrenia provides a good justification for pooling data from multiple experiments in order to identify connectivity biomarkers of this mental illness.

Dysregulations of Synaptic Vesicle Trafficking in Schizophrenia

Schizophrenia is a serious psychiatric illness which is experienced by about 1 % of individuals worldwide and has a debilitating impact on perception, cognition, and social function. Over the years, several models/hypotheses have been developed which link schizophrenia to dysregulations of the dopamine, glutamate, and serotonin receptor pathways. An important segment of these pathways that have been extensively studied for the pathophysiology of schizophrenia is the presynaptic neurotransmitter release mechanism. This set of molecular events is an evolutionarily well-conserved process that involves vesicle recruitment, docking, membrane fusion, and recycling, leading to efficient neurotransmitter delivery at the synapse. Accumulated evidence indicate dysregulation of this mechanism impacting postsynaptic signal transduction via different neurotransmitters in key brain regions implicated in schizophrenia. In recent years, after ground-breaking work that elucidated the operations of this mechanism, research efforts have focused on the alterations in the messenger RNA (mRNA) and protein expression of presynaptic neurotransmitter release molecules in schizophrenia and other neuropsychiatric conditions. In this review article, we present recent evidence from schizophrenia human postmortem studies that key proteins involved in the presynaptic release mechanism are dysregulated in the disorder. We also discuss the potential impact of dysfunctional presynaptic neurotransmitter release on the various neurotransmitter systems implicated in schizophrenia.

Implicit Timing as the Missing Link between Neurobiological and Self Disorders in Schizophrenia?

Disorders of consciousness and the self are at the forefront of schizophrenia symptomatology. Patients are impaired in feeling themselves as the authors of their thoughts and actions. In addition, their flow of consciousness is disrupted, and thought fragmentation has been suggested to be involved in the patients' difficulties in feeling as being one unique, unchanging self across time. Both impairments are related to self disorders, and both have been investigated at the experimental level. Here we review evidence that both mechanisms of motor control and the temporal structure of signal processing are impaired in schizophrenia patients. Based on this review, we propose that the sequencing of action and perception plays a key role in the patients' impairments. Furthermore, the millisecond time scale of the disorders, as well as the impaired sequencing, highlights the cooperation between brain networks including the cerebellum, as proposed by Andreasen (1999). We examine this possibility in the light of recent knowledge on the anatomical and physiological properties of the cerebellum, its role in timing, and its involvement in known physiological impairments in patients with schizophrenia, e.g., resting states and brain dynamics. A disruption in communication between networks involving the cerebellum, related to known impairments in dopamine, glutamate and GABA transmission, may help to better explain why patients experience reduced attunement with the external world and possibly with themselves.

Disrupted sensorimotor and social-cognitive networks underlie symptoms in childhood-onset schizophrenia

Schizophrenia is increasingly recognized as a neurodevelopmental disorder with altered connectivity among brain networks. In the current study we examined large-scale network interactions in childhood-onset schizophrenia, a severe form of the disease with salient genetic and neurobiological abnormalities. Using a data-driven analysis of resting-state functional magnetic resonance imaging fluctuations, we characterized data from 19 patients with schizophrenia and 26 typically developing controls, group matched for age, sex, handedness, and magnitude of head motion during scanning. This approach identified 26 regions with decreased functional correlations in schizophrenia compared to controls. These regions were found to organize into two function-related networks, the first with regions associated with social and higher-level cognitive processing, and the second with regions involved in somatosensory and motor processing. Analyses of across- and within-network regional interactions revealed pronounced across-network decreases in functional connectivity in the schizophrenia group, as well as a set of across-network relationships with overall negative coupling indicating competitive or opponent network dynamics. Critically, across-network decreases in functional connectivity in schizophrenia predicted the severity of positive symptoms in the disorder, such as hallucinations and delusions. By contrast, decreases in functional connectivity within the social-cognitive network of regions predicted the severity of negative symptoms, such as impoverished speech and flattened affect. These results point toward the role that abnormal integration of sensorimotor and social-cognitive processing may play in the pathophysiology and symptomatology of schizophrenia.

Connectome and schizophrenia

PURPOSE OF REVIEW

The neural connections, interconnections and organized networks of the central nervous system (CNS), which represent the human connectome, are critical for intact brain function. Consequently, disturbances at any level or juncture of these networks may alter behaviour and/or lead to brain dysfunction. In this review, we focus on highlighting recent work using advanced imaging methods to address alterations in the structural and functional connectome in patients with schizophrenia.

RECENT FINDINGS

Using structural, diffusion, resting-state and task-related functional imaging and advanced computational analysis methods such as graph theory, more than 200 publications have addressed different aspects of structural and/or functional connectivity in schizophrenia over the last year. These studies have focused on determining how brain networks differ from those in controls, interact with symptom profiles within and across diagnoses, interface with disease-related cognitive impairments and confer genetic risk for the disorder.

SUMMARY

Much existing evidence supports the view that schizophrenia is a disorder of altered brain connectivity. Recent and continued characterization of the structural and functional connectome in schizophrenia patients have advanced our understanding of the neurobiology underlying clinical symptoms and cognitive impairments in a particular patient, their overlaps with other neuropsychiatric disorders sharing common features as well as the contributions of genetic risk factors. Although the clinical utility of the schizophrenia connectome remains to be realized, recent findings provide further promise that research in this area may lead to improved diagnosis, treatments and clinical outcomes.

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.

Mediodorsal and visual thalamic connectivity differ in schizophrenia and bipolar disorder with and without psychosis history

Empirical and theoretical studies implicate thalamocortical circuits in schizophrenia, supported by emerging resting-state functional connectivity studies (rs-fcMRI). Similar but attenuated alterations were found in bipolar disorder (BD). However, it remains unknown if segregated loops within thalamocortical systems show distinct rs-fcMRI alterations in schizophrenia. For instance, the mediodorsal (MD) nucleus, known to project to prefrontal networks, may be differently altered than the lateral geniculate nucleus (LGN), known to project to the occipital cortex. Also, it remains unknown if these circuits show different patterns of alterations in BD as a function of psychosis history, which may be associated with a more severe clinical course. We addressed these questions in 90 patients with chronic schizophrenia and 73 remitted BD patients (33 with psychosis history) matched to 146 healthy comparison subjects. We hypothesized that the MD vs LGN would show dissociations across diagnostic groups. We found that MD and LGN show more qualitative similarities than differences in their patterns of dysconnectivity in schizophrenia. In BD, patterns qualitatively diverged between thalamic nuclei although these effects were modest statistically. BD with psychosis history was associated with more severe dysconnectivity, particularly for the MD nucleus. Also, the MD nucleus showed connectivity reductions with the cerebellum in schizophrenia but not in BD. Results suggest dissociations for thalamic nuclei across diagnoses, albeit carefully controlling for medication is warranted in future studies. Collectively, these findings have implications for designing more precise neuroimaging-driven biomarkers that can identify common and divergent large-scale network perturbations across psychiatric diagnoses with shared symptoms.

Hyper-coupling between working memory task-evoked activations and amplitude of spontaneous fluctuations in first-episode schizophrenia

Working memory (WM) deficit is an important component of impaired cognition in schizophrenia. However, between-studies inconsistencies as to the specific functional substrate imply that inter-individual variability (IIV) in the WM performance is associated with IIV in brain activity in schizophrenia. To examine the neural substrate of this WM IIV, we studied whether the neural mechanisms that underlie individual differences in WM capacity are the same in schizophrenia patients and healthy people. We correlated the IIV of the task-evoked brain activity and task performance during an n-back WM task with the IIV of the moment-to-moment variability in intrinsic resting-state activity, as measured by the amplitude of low-frequency fluctuations (ALFFs) and further compared this relationship between 17 patients with first-episode schizophrenia (FES) and 18 healthy controls. Between-group comparisons of the correlation patterns indicated aberrant ALFF-WM activation correlations and ALFF-WM performance correlations in the FES patients, but no significant changes were detected in any single measurement of these three characteristics. Specifically, we found increased positive ALFF-WM activation correlations in the bilateral lateral prefrontal cortices, posterior parietal cortices and fusiform gyri in the FES patients. We also observed significant increases in positive ALFF-WM performance correlations in the bilateral ventromedial prefrontal cortices in the FES patients. This hyper-coupling between the ALFF and fMRI measures during a WM task may indicate that it was difficult for the patients to detach themselves from one state to transition to another and suggests that the inefficient cortical function in schizophrenia stems from the intrinsic functional architecture of the brain.

Contributions of Feature Binding During Encoding and Functional Connectivity of the Medial Temporal Lobe Structures to Episodic Memory Deficits Across the Prodromal and First-Episode Phases of Schizophrenia

Patients with and at risk for psychosis may have difficulty using associative strategies to facilitate episodic memory encoding and recall. In parallel studies, patients with first-episode schizophrenia (n = 27) and high psychosis risk (n = 28) compared with control participants (n = 22 and n = 20, respectively) underwent functional MRI during a remember-know memory task. Psychophysiological interaction analyses, using medial temporal lobe (MTL) structures as regions of interest, were conducted to measure functional connectivity patterns supporting successful episodic memory. During encoding, patients with first-episode schizophrenia demonstrated reduced functional coupling between MTL regions and regions involved in stimulus representations, stimulus selection, and cognitive control. Relative to control participants and patients with high psychosis risk who did not convert to psychosis, patients with high psychosis risk who later converted to psychosis also demonstrated reduced connectivity between MTL regions and auditory-verbal and visual-association regions. These results suggest that episodic memory deficits in schizophrenia are related to inefficient recruitment of cortical connections involved in associative memory formation; such deficits precede the onset of psychosis among those individuals at high clinical risk.

Neuroimaging for psychotherapy research: current trends

OBJECTIVE

This article reviews neuroimaging studies that inform psychotherapy research. An introduction to neuroimaging methods is provided as background for the increasingly sophisticated breadth of methods and findings appearing in psychotherapy research.

METHOD

We compiled and assessed a comprehensive list of neuroimaging studies of psychotherapy outcome, along with selected examples of other types of studies that also are relevant to psychotherapy research. We emphasized magnetic resonance imaging (MRI) since it is the dominant neuroimaging modality in psychological research.

RESULTS

We summarize findings from neuroimaging studies of psychotherapy outcome, including treatment for depression, obsessive compulsive disorder (OCD), and schizophrenia.

CONCLUSIONS

The increasing use of neuroimaging methods in the study of psychotherapy continues to refine our understanding of both outcome and process. We suggest possible directions for future neuroimaging studies in psychotherapy research.

The brain network reflecting bodily self-consciousness: a functional connectivity study

Several brain regions are important for processing self-location and first-person perspective, two important aspects of bodily self-consciousness. However, the interplay between these regions has not been clarified. In addition, while self-location and first-person perspective in healthy subjects are associated with bilateral activity in temporoparietal junction (TPJ), disturbed self-location and first-person perspective result from damage of only the right TPJ. Identifying the involved brain network and understanding the role of hemispheric specializations in encoding self-location and first-person perspective, will provide important information on system-level interactions neurally mediating bodily self-consciousness. Here, we used functional connectivity and showed that right and left TPJ are bilaterally connected to supplementary motor area, ventral premotor cortex, insula, intraparietal sulcus and occipitotemporal cortex. Furthermore, the functional connectivity between right TPJ and right insula had the highest selectivity for changes in self-location and first-person perspective. Finally, functional connectivity revealed hemispheric differences showing that self-location and first-person perspective modulated the connectivity between right TPJ, right posterior insula, and right supplementary motor area, and between left TPJ and right anterior insula. The present data extend previous evidence on healthy populations and clinical observations in neurological deficits, supporting a bilateral, but right-hemispheric dominant, network for bodily self-consciousness.

Connectivity, pharmacology, and computation: toward a mechanistic understanding of neural system dysfunction in schizophrenia

Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks. Functional neuroimaging tools have evolved sufficiently to reliably detect system-level disturbances in neural networks. This review focuses on recent findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. These benefits notwithstanding, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle in translating findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. We specifically discuss two complementary approaches. The first is pharmacological manipulations in healthy volunteers, which transiently mimic some cardinal features of psychiatric conditions. We specifically focus on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, we discuss the combination of human pharmacological imaging with biophysically informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. To illustrate this approach, we review clinical investigations in addition to recent findings of how computational modeling has guided inferences drawn from our studies involving ketamine administration to healthy subjects. Thus, this review asserts that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.

The effects of psychotherapy on brain function: a systematic and critical review

Over the past two decades, the development of neuroimaging techniques has allowed the non-invasive investigation of neuroplastic changes associated with psychotherapeutic treatment. The aim of the present article is to present a systematic and critical review of longitudinal studies addressing the impact of psychotherapy on the brain published to date. After summarizing the results reported in the literature for each psychiatric disorder separately (i.e. obsessive-compulsive disorder, panic disorder, unipolar major depressive disorder, posttraumatic stress disorder, specific phobia, schizophrenia), we discuss the results focusing on three questions of interest: (i) whether neurobiological changes which follow psychotherapy occur in regions that showed significant neurofunctional alteration pre-treatment; (ii) whether these neurobiological changes are similar, or different, to those observed following pharmacological treatment; and (iii) whether neurobiological changes could be used as an objective means of monitoring the progress and outcome of psychotherapy. The evidence reviewed indicates that (i) depending on the disorder under investigation, psychotherapy results in either a normalisation of abnormal patterns of activity, the recruitment of additional areas which did not show altered activation prior to treatment, or a combination of the two; (ii) the effects of psychotherapy on brain function are comparable to those of medication for some but not all disorders; and (iii) there is preliminary evidence that neurobiological changes are associated with the progress and outcome of psychotherapy. It is hoped that a better understanding of the impact of psychotherapy on brain function will eventually inform the development of new biologically informed treatments and allow clinicians to make more effective treatment decisions.

Neurocognitive biases and the patterns of spontaneous correlations in the human cortex

When the brain is 'at rest', spatiotemporal activity patterns emerge spontaneously, that is, in the absence of an overt task. However, what these patterns reveal about cortical function remains elusive. In this article, we put forward the hypothesis that the correlation patterns among these spontaneous fluctuations (SPs) reflect the profile of individual a priori cognitive biases, coded as synaptic efficacies in cortical networks. Thus, SPs offer a new means for mapping personal traits in both neurotypical and atypical cases. Three sets of observations and related empirical evidence provide support for this hypothesis. First, SPs correspond to activation patterns that occur during typical task performance. Second, individual differences in SPs reflect individual biases and abnormalities. Finally, SPs can be actively remodeled in a long-term manner by focused and intense cortical training.

Shifted coupling of EEG driving frequencies and fMRI resting state networks in schizophrenia spectrum disorders

Introduction

The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) resting state networks (RSNs). Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates) and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN) and left-working-memory network (LWMN).

Methods

Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11) and matched healthy controls (n = 11) using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps.

Results

The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls.

Conclusions

By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling.

Significance

The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations), arise.

The day-after effect: long term, Hebbian-like restructuring of resting-state fMRI patterns induced by a single epoch of cortical activation

During rest, the cerebral cortex displays rich, coordinated patterns of spontaneous activity. The mechanism that shapes these patterns is largely unknown. Here we demonstrate that a Hebbian-like, sustained process plays a role in focusing these coherent patterns. Human subjects used an fMRI-based neurofeedback (NF) paradigm to intensely activate the dorsal anterior cingulate cortex for a single epoch (30 min). Resting-state correlations between all of the cortical voxels' BOLD time courses (functional connectivity) were mapped before, immediately after, and one day after the NF session. We found that the single epoch of cortical activation induced a lasting restructuring of the functional connections according to a Hebbian-like rule. Therefore, the change (increase and decrease) in functional connectivity strength of cortical voxels during rest reflected the level of their prior coactivation during the NF epoch. Interestingly, the effect was significantly enhanced 1 d after the NF activation epoch. The effect was evident in each subject individually, indicating its potential as a diagnostic window into the personal history of prior brain activations of both healthy and abnormal individuals.

The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders

We have previously argued from a theoretical basis that the standard practice of regression of the Global Signal from the fMRI time series in functional connectivity studies is ill advised, particularly when comparing groups of participants. Here, we demonstrate in resting-state data from participants with an Autism Spectrum Disorder and matched controls that these concerns are also well founded in real data. Using the prior theoretical work to formulate predictions, we show: (1) rather than simply altering the mean or range of correlation values amongst pairs of brain regions, Global Signal Regression systematically alters the rank ordering of values in addition to introducing negative values, (2) it leads to a reversal in the direction of group correlation differences relative to other preprocessing approaches, with a higher incidence of both long-range and local correlation differences that favor the Autism Spectrum Disorder group, (3) the strongest group differences under other preprocessing approaches are the ones most altered by Global Signal Regression, and (4) locations showing group differences no longer agree with those showing correlations with behavioral symptoms within the Autism Spectrum Disorder group. The correlation matrices of both participant groups under Global Signal Regression were well predicted by our previous mathematical analyses, demonstrating that there is nothing mysterious about these results. Finally, when independent physiological nuisance measures are lacking, we provide a simple alternative approach for assessing and lessening the influence of global correlations on group comparisons that replicates our previous findings. While this alternative performs less well for symptom correlations than our favored preprocessing approach that includes removal of independent physiological measures, it is preferable to the use of Global Signal Regression, which prevents unequivocal conclusions about the direction or location of group differences.

Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness

Schizophrenia is a devastating neuropsychiatric syndrome associated with distributed brain dysconnectivity that may involve large-scale thalamo-cortical systems. Incomplete characterization of thalamic connectivity in schizophrenia limits our understanding of its relationship to symptoms and to diagnoses with shared clinical presentation, such as bipolar illness, which may exist on a spectrum. Using resting-state functional magnetic resonance imaging, we characterized thalamic connectivity in 90 schizophrenia patients versus 90 matched controls via: (1) Subject-specific anatomically defined thalamic seeds; (2) anatomical and data-driven clustering to assay within-thalamus dysconnectivity; and (3) machine learning to classify diagnostic membership via thalamic connectivity for schizophrenia and for 47 bipolar patients and 47 matched controls. Schizophrenia analyses revealed functionally related disturbances: Thalamic over-connectivity with bilateral sensory-motor cortices, which predicted symptoms, but thalamic under-connectivity with prefrontal-striatal-cerebellar regions relative to controls, possibly reflective of sensory gating and top-down control disturbances. Clustering revealed that this dysconnectivity was prominent for thalamic nuclei densely connected with the prefrontal cortex. Classification and cross-diagnostic results suggest that thalamic dysconnectivity may be a neural marker for disturbances across diagnoses. Present findings, using one of the largest schizophrenia and bipolar neuroimaging samples to date, inform basic understanding of large-scale thalamo-cortical systems and provide vital clues about the complex nature of its disturbances in severe mental illness.

Brain regions responsible for tinnitus distress and loudness: a resting-state FMRI study

Subjective tinnitus is characterized by the perception of phantom sound without an external auditory stimulus. We hypothesized that abnormal functionally connected regions in the central nervous system might underlie the pathophysiology of chronic subjective tinnitus. Statistical significance of functional connectivity (FC) strength is affected by the regional autocorrelation coefficient (AC). In this study, we used resting-state functional MRI (fMRI) and measured regional mean FC strength (mean cross-correlation coefficient between a region and all other regions without taking into account the effect of AC (rGC) and with taking into account the effect of AC (rGCa) to elucidate brain regions related to tinnitus symptoms such as distress, depression and loudness. Consistent with previous studies, tinnitus loudness was not related to tinnitus-related distress and depressive state. Although both rGC and rGCa revealed similar brain regions where the values showed a statistically significant relationship with tinnitus-related symptoms, the regions for rGCa were more localized and more clearly delineated the regions related specifically to each symptom. The rGCa values in the bilateral rectus gyri were positively correlated and those in the bilateral anterior and middle cingulate gyri were negatively correlated with distress and depressive state. The rGCa values in the bilateral thalamus, the bilateral hippocampus, and the left caudate were positively correlated and those in the left medial superior frontal gyrus and the left posterior cingulate gyrus were negatively correlated with tinnitus loudness. These results suggest that distinct brain regions are responsible for tinnitus symptoms. The regions for distress and depressive state are known to be related to depression, while the regions for tinnitus loudness are known to be related to the default mode network and integration of multi-sensory information.

Combining classification with fMRI-derived complex network measures for potential neurodiagnostics

Complex network analysis (CNA), a subset of graph theory, is an emerging approach to the analysis of functional connectivity in the brain, allowing quantitative assessment of network properties such as functional segregation, integration, resilience, and centrality. Here, we show how a classification framework complements complex network analysis by providing an efficient and objective means of selecting the best network model characterizing given functional connectivity data. We describe a novel kernel-sum learning approach, block diagonal optimization (BDopt), which can be applied to CNA features to single out graph-theoretic characteristics and/or anatomical regions of interest underlying discrimination, while mitigating problems of multiple comparisons. As a proof of concept for the method’s applicability to future neurodiagnostics, we apply BDopt classification to two resting state fMRI data sets: a trait (between-subjects) classification of patients with schizophrenia vs. controls, and a state (within-subjects) classification of wake vs. sleep, demonstrating powerful discriminant accuracy for the proposed framework.

Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain

Human brain functional networks contain a few densely connected hubs that play a vital role in transferring information across regions during resting and task states. However, the relationship of these functional hubs to measures of brain physiology, such as regional cerebral blood flow (rCBF), remains incompletely understood. Here, we used functional MRI data of blood-oxygenation-level-dependent and arterial-spin-labeling perfusion contrasts to investigate the relationship between functional connectivity strength (FCS) and rCBF during resting and an N-back working-memory task. During resting state, functional brain hubs with higher FCS were identified, primarily in the default-mode, insula, and visual regions. The FCS showed a striking spatial correlation with rCBF, and the correlation was stronger in the default-mode network (DMN; including medial frontal-parietal cortices) and executive control network (ECN; including lateral frontal-parietal cortices) compared with visual and sensorimotor networks. Moreover, the relationship was connection-distance dependent; i.e., rCBF correlated stronger with long-range hubs than short-range ones. It is notable that several DMN and ECN regions exhibited higher rCBF per unit connectivity strength (rCBF/FCS ratio); whereas, this index was lower in posterior visual areas. During the working-memory experiment, both FCS-rCBF coupling and rCBF/FCS ratio were modulated by task load in the ECN and/or DMN regions. Finally, task-induced changes of FCS and rCBF in the lateral-parietal lobe positively correlated with behavioral performance. Together, our results indicate a tight coupling between blood supply and brain functional topology during rest and its modulation in response to task demands, which may shed light on the physiological basis of human brain functional connectome.

Resting state functional connectivity reflects abnormal task-activated patterns in a developmental object agnosic

Even in the absence of stimulation or task, the cerebral cortex shows an incessant pattern of ultra slow fluctuations which are coherent across brain regions. In the healthy brain these coherent patterns (also termed resting state functional connectivity) often exhibit spatial similarity to the large scale organization of task-induced functional networks. However, it is not clear to what extent the resting state patterns can also reflect task-induced abnormalities in cortical activations which are often detected in various brain pathologies. Here we examined whether an abnormal visual activation pattern is recapitulated in the resting state functional connectivity. We examined LG, a sighted young adult with developmental object agnosia and no apparent cortical structural abnormality. We have previously reported that upon visual stimulation, LG's intermediate visual areas (V2, V3) are paradoxically deactivated. Here, examining LG's resting state functional connectivity revealed the same pattern of functional abnormality - including a strong atypical decorrelation between areas V2-V3 and the rest of the visual system. Thus, our results suggest that resting-state functional connectivity could provide a powerful tool which could complement task-specific paradigms in detecting task-related abnormalities in cortical activity without resorting to task performance.

Never resting region--mPFC in schizophrenia

Reduced functional connectivity (FC) in schizophrenia has been demonstrated either in task related or 'default network' areas, but not between these networks, which interact meaningfully. We examined the role of FC between the inferior frontal gyrus (IFG) and medial prefrontal cortex (mPFC) in determining language-lateralization during a language task, and its association with structural integrity of the corpus-callosum. Only schizophrenia patients presented increased mPFC-IFG FC during task, which additionally corresponded to decreased white-matter organization of the corpus-callosum. These findings suggest that inability to suppress irrelevant internally-generated information while processing external stimuli might be the basis of functional psychopathology in schizophrenia.

Reduced connectivity in the self-processing network of schizophrenia patients with poor insight

Lack of insight (unawareness of illness) is a common and clinically relevant feature of schizophrenia. Reduced levels of self-referential processing have been proposed as a mechanism underlying poor insight. The default mode network (DMN) has been implicated as a key node in the circuit for self-referential processing. We hypothesized that during resting state the DMN network would show decreased connectivity in schizophrenia patients with poor insight compared to patients with good insight. Patients with schizophrenia were recruited from mental health care centers in the north of the Netherlands and categorized in groups having good insight (n= 25) or poor insight (n = 19). All subjects underwent a resting state fMRI scan. A healthy control group (n = 30) was used as a reference. Functional connectivity of the anterior and posterior part of the DMN, identified using Independent Component Analysis, was compared between groups. Patients with poor insight showed lower connectivity of the ACC within the anterior DMN component and precuneus within the posterior DMN component compared to patients with good insight. Connectivity between the anterior and posterior part of the DMN was lower in patients than controls, and qualitatively different between the good and poor insight patient groups. As predicted, subjects with poor insight in psychosis showed decreased connectivity in DMN regions implicated in self-referential processing, although this concerned only part of the network. This finding is compatible with theories implying a role of reduced self-referential processing as a mechanism contributing to poor insight.