Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model

Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.

Long-term olfactory enrichment promotes non-olfactory cognition, noradrenergic plasticity and remodeling of brain functional connectivity in older mice

Brain functional and structural changes lead to cognitive decline during aging, but a high level of cognitive stimulation during life can improve cognitive performances in the older adults, forming the cognitive reserve. Noradrenaline has been proposed as a molecular link between environmental stimulation and constitution of the cognitive reserve. Taking advantage of the ability of olfactory stimulation to activate noradrenergic neurons of the locus coeruleus, we used repeated olfactory enrichment sessions over the mouse lifespan to enable the cognitive reserve buildup. Mice submitted to olfactory enrichment, whether started in early or late adulthood, displayed improved olfactory discrimination at late ages and interestingly, improved spatial memory and cognitive flexibility. Moreover, olfactory and non-olfactory cognitive performances correlated with increased noradrenergic innervation in the olfactory bulb and dorsal hippocampus. Finally, c-Fos mapping and connectivity analysis revealed task-specific remodeling of functional neural networks in enriched older mice. Long-term olfactory enrichment thus triggers structural noradrenergic plasticity and network remodeling associated with better cognitive aging and thereby forms a promising mouse model of the cognitive reserve buildup.

Distinct MRI-based functional and structural connectivity for antidepressant response prediction in major depressive disorder

OBJECTIVE

Emerging studies have identified treatment-related connectome predictors in major depressive disorder (MDD). However, quantifying treatment-responsive patterns in structural connectivity (SC) and functional connectivity (FC) simultaneously remains underexplored. We aimed to evaluate whether spatial distributions of FC and SC associated treatment responses are shared or unique.

METHODS

Diffusion tensor imaging and resting-state functional magnetic resonance imaging were collected from 210 patients with MDD at baseline. We separately developed connectome-based prediction models (CPM) to predict reduction of depressive severity after 6-week monotherapy based on structural, functional, and combined connectomes, then validated them on the external dataset. We identified the predictive SC and FC from CPM with high occurrence frequencies during the cross-validation.

RESULTS

Structural connectomes (r = 0.2857, p < 0.0001), functional connectomes (r = 0.2057, p = 0.0025), and their combined CPM (r = 0.4, p < 0.0001) can significantly predict a reduction of depressive severity. We didn't find shared connectivity between predictive FC and SC. Specifically, the most predictive FC stemmed from the default mode network, while predictive SC was mainly characterized by within-network SC of fronto-limbic networks.

CONCLUSIONS

These distinct patterns suggest that SC and FC capture unique connectivity concerning the antidepressant response.

SIGNIFICANCE

Our findings provide comprehensive insights into the neurophysiology of antidepressants response.

Impaired Cortico-Thalamo-Cerebellar Integration Across Schizophrenia, Bipolar II, and Attention Deficit Hyperactivity Disorder Patients Suggests Potential Neural Signatures for Psychiatric Illness

Understanding aberrant functional changes between brain regions has shown promise for characterizing and differentiating the symptoms associated with progressive psychiatric disorders. The functional integration between the thalamus and cerebellum significantly influences learning and memory in cognition. Observed in schizophrenic patients, dysfunction within the corticalthalamocerebellar (CTC) circuitry is linked to challenges in prioritizing, processing, coordinating, and responding to information. This study explored whether abnormal CTC functional network connectivity patterns are present across schizophrenia (SCHZ) patients, bipolar II disorder (BIPOL) patients, and ADHD patients by examining both task- and task-free conditions compared to healthy volunteers (HC). Leveraging fMRI data from 135 participants (39 HC, 27 SCHZ patients, 38 BIPOL patients, and 31 ADHD patients), we analyzed functional network connectivity (FNC) patterns across 115 cortical, thalamic, subcortical, and cerebellar regions of interest (ROIs). Guiding our investigation: First, do the brain regions of the CTC circuit exhibit distinct abnormal patterns at rest in SCHZ, ADHD, and BIPOL? Second, do working memory tasks in these patients engage common regions of the circuit in similar or unique patterns? Consistent with previous findings, our observations revealed FNC patterns constrained in the cerebellar, thalamic, striatal, hippocampal, medial prefrontal and insular cortices across all three psychiatric cohorts when compared to controls in both task and task-free conditions. Post hoc analysis suggested a predominance in schizophrenia and ADHD patients during rest, while the task condition demonstrated effects across all three disorders. Factor-by-covariance GLM MANOVA further specified regions associated with clinical symptoms and trait assessments. Our study provides evidence suggesting that dysfunctional CTC circuitry in both task-free and task-free conditions may be an important broader neural signature of psychiatric illness.

Functional Connectivity of the Nucleus Accumbens across Variants of Callous-Unemotional Traits: A Resting-State fMRI Study in Children and Adolescents

A large body of literature suggests that the primary (high callousness-unemotional traits [CU] and low anxiety) and secondary (high CU traits and anxiety) variants of psychopathy significantly differ in terms of their clinical profiles. However, little is known about their neurobiological differences. While few studies showed that variants differ in brain activity during fear processing, it remains unknown whether they also show atypical functioning in motivational and reward system. Latent Profile Analysis (LPA) was conducted on a large sample of adolescents (n = 1416) to identify variants based on their levels of callousness and anxiety. Seed-to-voxel connectivity analysis was subsequently performed on resting-state fMRI data to compare connectivity patterns of the nucleus accumbens across subgroups. LPA failed to identify the primary variant when using total score of CU traits. Using a family-wise cluster correction, groups did not differ on functional connectivity. However, at an uncorrected threshold the secondary variant showed distinct functional connectivity between the nucleus accumbens and posterior insula, lateral orbitofrontal cortex, supplementary motor area, and parietal regions. Secondary LPA analysis using only the callousness subscale successfully distinguish both variants. Group differences replicated results of deficits in functional connectivity between the nucleus accumbens and posterior insula and supplementary motor area, but additionally showed effect in the superior temporal gyrus which was specific to the primary variant. The current study supports the importance of examining the neurobiological markers across subgroups of adolescents at risk for conduct problems to precise our understanding of this heterogeneous population.

Functional Abnormality of the Reward System in Depressed Adolescents and Young Adults with and without Suicidal Behavior

OBJECTIVE

To identify local and functional connectivity abnormalities in the brain's reward network in depressed adolescents and young adults with and without suicidal behavior.

METHODS

Magnetic resonance imaging data were obtained from 41 major depressive disorder (MDD) patients with suicidal behavior (sMDD, males/females: 12/29), 44 MDD patients without suicidal behavior (nMDD, males/females: 13/32), and 52 healthy controls (HCs, males/females: 17/35). The Young Mania Scale, Hamilton Depression Scale, Columbia Suicide Scale, and Scale for Suicide Ideation were used to evaluate emotional state and suicidal ideation and behaviors. The amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo) and functional connectivity of 11 regions of interest (ROIs) in the reward network were determined.

RESULTS

ALFF values in the vmPFC of the nMDD group were significantly lower than those in the HC group (p = 0.031). The ReHo values of the nMDD group were lower in the lVS but higher in the vmPFC than those of the HC group (P = 0.018 and 0.025, respectively). Functional connectivity of the AC with the vmPFC, lVS, rVS, and vmPFC was increased in the sMDD group compared with that in the nMDD group (P = 0.038, 0.034, 0.006, respectively).

CONCLUSION

Local and functional connectivity abnormalities in the reward network were found in the MDD groups. However, increased functional connectivity was found in only the sMDD group.

Structural and functional connectivity associations with anterior cingulate sulcal variability

Background

Sulcation of the anterior cingulate may be defined by presence of a paracingulate sulcus, a tertiary sulcus developing during the third gestational trimester with implications on cognitive function and disease.

Methods

In this retrospective analysis we examine task-free resting state functional connectivity and diffusion-weighted tract segmentation data from a cohort of healthy adults (< 60-year-old, n = 129), exploring the impact of ipsilateral paracingulate sulcal presence on structural and functional connectivity.

Results

Presence of a left paracingulate sulcus was associated with reduced fractional anisotropy in the left cingulum (P = 0.02) bundle and the peri-genual (P = 0.002) and dorsal (P = 0.03) but not the temporal cingulum bundle segments. Left paracingulate sulcal presence was associated with increased left peri-genual radial diffusivity (P = 0.003) and tract volume (P = 0.012). A significant, predominantly intraregional frontal component of altered resting state functional connectivity was identified in individuals possessing a left PCS (P = 0.01). Seed-based functional connectivity in pre-defined networks was not associated with paracingulate sulcal presence.

Conclusion

These results identify a novel association between neurodevelopmentally derived sulcation and altered structural connectivity in a healthy adult population with implications for conditions where this variation is of interest. Furthermore, they provide evidence of a link between the structural and functional connectivity of the brain in the presence of a paracingulate sulcus which may be mediated by a highly connected local functional network reliant on short association fibres.

Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource

We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject's connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.

Resting-State Functional Connectivity of the Anterior Cingulate Cortex Among Persons With Mood Disorders and Suicidal Behaviors

OBJECTIVE

To assess whether anterior cingulate cortex (ACC) abnormalities contribute to suicide risk in major depressive disorder and bipolar disorder, the investigators compared resting-state functional connectivity (rsFC) of ACC subdivisions between individuals with major depressive or bipolar disorder with and without a lifetime history of suicidal behavior.

METHODS

Forty-two inpatients with and 26 inpatients without a history of suicidal behavior (SB+ and SB-, respectively) associated with major depressive or bipolar disorder and 40 healthy control (HC) participants underwent rsFC neuroimaging. RsFC of the subgenual, perigenual, rostral, dorsal, and caudal subdivisions of the ACC was calculated. Possible confounders, such as psychosis and severity of depression, were controlled for, seed-to-voxel and post hoc region of interest (ROI)-to-ROI analyses were performed, and the accuracy of rsFC in classifying suicidal behavior was studied.

RESULTS

Compared with individuals in the SB- and HC groups, patients in the SB+ group had higher rsFC between the left rostral and right dorsal ACC seeds and visual cortex clusters. Conversely, rsFC between the left rostral and right dorsal ACC seeds and cingulate and frontal clusters was lower in the SB+ group than in the HC group. Left rostral ACC to left Brodmann's area 18 connectivity showed up to 75% discriminative accuracy in distinguishing SB+ from SB- patients.

CONCLUSIONS

A history of suicidal behavior among individuals with major depressive disorder or bipolar disorder was associated with altered rsFC of the rostral and caudal ACC, regions involved in conflict detection and error monitoring. Replication of these findings is needed to further explore the involvement of the ACC in the neurobiology of suicidal behavior and suicidal ideation.

Testing the triple network model of psychopathology in a transdiagnostic neurodevelopmental cohort

AIM

The triple network model of psychopathology posits that altered connectivity between the Salience (SN), Central Executive (CEN), and Default Mode Networks (DMN) may underlie neurodevelopmental conditions. However, this has yet to be tested in a transdiagnostic sample of young people.

METHOD

We investigated this in 175 children (60 girls) that represent a heterogeneous population who are experiencing neurodevelopmental difficulties in cognition and behavior, and 60 comparison children (33 girls). Hyperactivity/impulsivity and inattention were assessed by parent-report. Resting-state functional Magnetic Resonance Imaging data were acquired and functional connectivity was calculated between independent network components and regions of interest. We then examined whether connectivity between the SN, CEN and DMN was dimensionally related to hyperactivity/impulsivity and inattention, whilst controlling for age, gender, and motion.

RESULTS

Hyperactivity/impulsivity was associated with increased functional connectivity between the SN, CEN, and DMN in at-risk children, whereas it was associated with decreased functional connectivity between the CEN and DMN in comparison children. These effects replicated in an adult parcellation of brain function and when using increasingly stringent exclusion criteria for in-scanner motion.

CONCLUSION

Triple network connectivity characterizes transdiagnostic neurodevelopmental difficulties with hyperactivity/impulsivity. We suggest that this may arise from delayed network segregation, difficulties sustaining CEN activity to regulate behavior, and/or a heightened developmental mismatch between neural systems implicated in cognitive control relative to those implicated in reward/affect processing.

A comparison of functional activation and connectivity of the cerebellum in adults and children during single word processing

Meta-analyses on reading show cerebellar activation in adults, but not children, suggesting a possible age-dependent role of the cerebellum in reading. However, the few studies that compare adults and children during reading report mixed cerebellar activation results. Here, we studied (i) cerebellar activation during implicit word processing in adults and children and (ii) functional connectivity (FC) between the cerebellum and left cortical regions involved in reading. First, both groups activated bilateral cerebellum for word processing when compared to fixation, but not when compared to the active control. There were no differences between adults and children. Second, we found intrinsic FC between several cerebellar seed regions and cortical target regions in adults and children, as well as between-group differences. However, task-modulated FC specific to word processing revealed no within- nor between-group results. Together this study does not provide support for a role of the cerebellum in word processing at either age.

Age-Specific Diagnostic Classification of ASD Using Deep Learning Approaches

Autism Spectrum Disorder (ASD) is a highly heterogeneous condition, due to high variance in its etiology, comorbidity, pathogenesis, severity, genetics, and brain functional connectivity (FC). This makes it devoid of any robust universal biomarker. This study aims to analyze the role of age and multivariate patterns in brain FC and their accountability in diagnosing ASD by deep learning algorithms. We utilized functional magnetic resonance imaging data of three age groups (6 to 11, 11 to 18, and 6 to 18 years), available with public databases ABIDE-I and ABIDE-II, to discriminate between ASD and typically developing. The blood-oxygen-level dependent time series were extracted using the Gordon's, Harvard Oxford and Diedrichsen's atlases, over 236 regions of interest, as 236x236 sized FC matrices for each participant, with Pearson correlations. The feature sets, in the form of FC heat maps were computed with respect to each age group and were fed to a convolutional neural network, such as MobileNetV2 and DenseNet201 to build age-specific diagnostic models. The results revealed that DenseNet201 was able to adapt and extract better features from the heat maps, and hence returned better accuracy scores. The age-specific dataset, with participants of ages 6 to 11 years, performed best, followed by 11 to 18 years and 6 to 18 years, with accuracy scores of 72.19%, 71.88%, and 69.74% respectively, when tested using the DenseNet201. Our results suggest that age-specific diagnostic models are able to counter heterogeneity present in ASD, and that enables better discrimination.

EEG source functional connectivity in patients after a recent suicide attempt

OBJECTIVE

Electroencephalogram (EEG) based frequency measures within the alpha frequency range (AFR), including functional connectivity, show potential in assessing the underlying pathophysiology of depression and suicide-related outcomes. We investigated the association between AFR connectivity, suicidal thoughts and behaviors, and depression in a transdiagnostic sample of patients after a recent suicide attempt (SA).

METHODS

Lagged source-based measures of linear and nonlinear whole-brain connectivity within the standard AFR ([sAFR], 8-12 Hz) and the individually referenced AFR (iAFR) were applied to 70 15-minute resting-state EEGs from patients after a SA and 70 age- and gender-matched healthy controls (HC). Hypotheses were tested using network-based statistics and multiple regression models.

RESULTS

Results showed no significant differences between patients after a SA and HC in any of the assessed connectivity modalities. However, a subgroup analysis revealed significantly increased nonlinear connectivity within the sAFR for patients after a SA with a depressive disorder or episode ([DD], n = 53) compared to matched HC. Furthermore, a multiple regression model, including significant main effects for group and global nonlinear connectivity within the sAFR outperformed all other models in explaining variance in depressive symptom severity.

CONCLUSIONS

Our study further supports the importance of the AFR in pathomechanisms of suicidality and depression. The iAFR does not seem to improve validity of phase-based connectivity.

SIGNIFICANCE

Our results implicate distinct neurophysiological patterns in suicidal subgroups. Exploring the potential of these patterns for treatment stratification might advance targeted interventions for suicidal thoughts and behaviors.

Insights from personalized models of brain and behavior for identifying biomarkers in psychiatry

A main goal in translational neuroscience is to identify neural correlates of psychopathology ("biomarkers") that can be used to facilitate diagnosis, prognosis, and treatment. This goal has led to substantial research into how psychopathology symptoms relate to large-scale brain systems. However, these efforts have not yet resulted in practical biomarkers used in clinical practice. One reason for this underwhelming progress may be that many study designs focus on increasing sample size instead of collecting additional data within each individual. This focus limits the reliability and predictive validity of brain and behavioral measures in any one person. As biomarkers exist at the level of individuals, an increased focus on validating them within individuals is warranted. We argue that personalized models, estimated from extensive data collection within individuals, can address these concerns. We review evidence from two, thus far separate, lines of research on personalized models of (1) psychopathology symptoms and (2) fMRI measures of brain networks. We close by proposing approaches uniting personalized models across both domains to improve biomarker research.

Event-Related Spectral Perturbation, Inter Trial Coherence, and Functional Connectivity in motor execution: A comparative EEG study of old and young subjects

INTRODUCTION

The motor-related bioelectric brain activity of healthy young and old subjects was studied to understand the effect of aging on motor execution. A visually cued finger tapping movement paradigm and high-density EEG were used to examine the time and frequency characteristics.

METHODS

Twenty-two young and 22 healthy elderly adults participated in the study. Repeated trials of left and right index finger movements were recorded with a 128-channel EEG. Event-Related Spectral Perturbation (ERSP), Inter Trial Coherence (ITC), and Functional Connectivity were computed and compared between the age groups.

RESULTS

An age-dependent theta and alpha band ERSP decrease was observed over the frontal-midline area. Decrease of beta band ERSP was found over the ipsilateral central-parietal regions. Significant ITC differences were found in the delta and theta bands between old and young subjects over the contralateral parietal-occipital areas. The spatial extent of increased ITC values was larger in old subjects. The movement execution of older subjects showed higher global efficiency in the delta and theta bands, and higher local efficiency and node strengths in the delta, theta, alpha, and beta bands.

CONCLUSION

As functional compensation of aging, elderly motor networks involve more nonmotor, parietal-occipital, and frontal areas, with higher global and local efficiency, node strength. ERSP and ITC changes seem to be sensitive and complementary biomarkers of age-related motor execution.

Network Localization of Spontaneous Confabulation

OBJECTIVE

Spontaneous confabulation is a symptom in which false memories are conveyed by the patient as true. The purpose of the study was to identify the neuroanatomical substrate of this complex symptom and evaluate the relationship to related symptoms, such as delusions and amnesia.

METHODS

Twenty-five lesion locations associated with spontaneous confabulation were identified in a systematic literature search. The network of brain regions functionally connected to each lesion location was identified with a large connectome database (N=1,000) and compared with networks derived from lesions associated with nonspecific (i.e., variable) symptoms (N=135), delusions (N=32), or amnesia (N=53).

RESULTS

Lesions associated with spontaneous confabulation occurred in multiple brain locations, but they were all part of a single functionally connected brain network. Specifically, 100% of lesions were connected to the mammillary bodies (familywise error rate [FWE]-corrected p<0.05). This connectivity was specific for lesions associated with confabulation compared with lesions associated with nonspecific symptoms or delusions (FWE-corrected p<0.05). Lesions associated with confabulation were more connected to the orbitofrontal cortex than those associated with amnesia (FWE-corrected p<0.05).

CONCLUSIONS

Spontaneous confabulation maps to a common functionally connected brain network that partially overlaps, but is distinct from, networks associated with delusions or amnesia. These findings lend new insight into the neuroanatomical bases of spontaneous confabulation.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

BACKGROUND

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations.

METHODS

This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model.

RESULTS

All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%).

CONCLUSIONS

The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia

BACKGROUND

Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.

METHODS

Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes.

RESULTS

Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-p_FWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively.

CONCLUSION

ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

Assessing functional connectivity differences and work-related fatigue in surviving COVID-negative patients

The Coronavirus Disease 2019 (COVID-19) has affected all aspects of life around the world. Neuroimaging evidence suggests the novel coronavirus can attack the central nervous system (CNS), causing cerebro-vascular abnormalities in the brain. This can lead to focal changes in cerebral blood flow and metabolic oxygen consumption rate in the brain. However, the extent and spatial locations of brain alterations in COVID-19 survivors are largely unknown. In this study, we have assessed brain functional connectivity (FC) using resting-state functional MRI (RS-fMRI) in 38 (25 males) COVID patients two weeks after hospital discharge, when PCR negative and 31 (24 males) healthy subjects. FC was estimated using independent component analysis (ICA) and dual regression. When compared to the healthy group, the COVID group demonstrated significantly enhanced FC in the basal ganglia and precuneus networks (family wise error (fwe) corrected, pfwe < 0.05), while, on the other hand, reduced FC in the language network (pfwe < 0.05). The COVID group also experienced higher fatigue levels during work, compared to the healthy group (p < 0.001). Moreover, within the precuneus network, we noticed a significant negative correlation between FC and fatigue scores across groups (Spearman's ρ = -0.47, p = 0.001, r2 = 0.22). Interestingly, this relationship was found to be significantly stronger among COVID survivors within the left parietal lobe, which is known to be structurally and functionally associated with fatigue in other neurological disorders.

Resting-State Connectivity and Response to Psychotherapy Treatment in Adolescents and Adults With OCD: A Randomized Clinical Trial

OBJECTIVE

Cortical-subcortical hyperconnectivity related to affective-behavioral integration and cortical network hypoconnectivity related to cognitive control have been demonstrated in obsessive-compulsive disorder (OCD); the study objective was to examine whether these connectivity patterns predict treatment response.

METHODS

Adolescents (ages 12-17) and adults (ages 24-45) were randomly assigned to 12 sessions of exposure and response prevention (ERP) or stress management therapy (SMT), an active control. Before treatment, resting-state connectivity of ventromedial prefrontal cortical (vmPFC), cingulo-opercular, frontoparietal, and subcortical regions was assessed with functional MRI. OCD severity was assessed with the Yale-Brown Obsessive Compulsive Scale before, during, and after treatment. Usable fMRI and longitudinal symptom data were obtained from 116 patients (68 female; 54 adolescents; 60 medicated).

RESULTS

ERP produced greater decreases in symptom scores than SMT. ERP was selectively associated with less vmPFC-subcortical (caudate and thalamus) connectivity in both age groups and primarily in unmedicated participants. Greater symptom improvement with both ERP and SMT was associated with greater cognitive-control (cingulo-opercular and frontoparietal) and subcortical (putamen) connectivity across age groups. Developmental specificity was observed across ERP and SMT treatments, such that greater improvements with ERP than SMT were associated with greater frontoparietal-subcortical (nucleus accumbens) connectivity in adolescents but greater connectivity between frontoparietal regions in adults. Comparison of response-predictive connections revealed no significant differences compared with a matched healthy control group.

CONCLUSIONS

The results suggest that less vmPFC-subcortical connectivity related to affect-influenced behavior may be important for ERP engagement, whereas greater cognitive-control and motor circuit connectivity may generally facilitate response to psychotherapy. Finally, neural predictors of treatment response may differ by age.

Rostral anterior cingulate connectivity in older adults with subthreshold depressive symptoms: A preliminary study

Subthreshold depressive symptoms are highly prevalent among older adults and are associated with numerous health risks including cognitive decline and decreased physical health. One brain region central to neuroanatomical models of depressive disorders is the anterior cingulate cortex (ACC). The rostral portion of the ACC-comprised of the pregenual ACC and subgenual ACC-is implicated in emotion control and reward processing. The goal of the current study was to examine how functional connectivity in subregions of the rostral ACC relate to depressive symptoms, measured by the Beck Depression Inventory-Second Edition, in an ethnically diverse sample of 28 community-dwelling older adults. Based on meta-analyses of previous studies in primarily young adults with clinical depression, we hypothesized that greater depressive symptoms would be associated with primarily increased resting-state functional connectivity from both the subgenual ACC and pregenual ACC to default mode network regions and the dorsolateral PFC. We instead found that higher depressive symptoms were associated with lower functional connectivity of the ACC to the dorsolateral PFC and regions within the default mode network, including from the subgenual ACC to the dorsolateral PFC and anterior cingulate and from the pregenual ACC to the middle cingulate gyrus. This preliminary study highlights brain alterations at subthreshold levels of depressive symptoms in older adults, which could serve as targets for interventions.

The human egomotion network

All volitional movement in a three-dimensional space requires multisensory integration, in particular of visual and vestibular signals. Where and how the human brain processes and integrates self-motion signals remains enigmatic. Here, we applied visual and vestibular self-motion stimulation using fast and precise whole-brain neuroimaging to delineate and characterize the entire cortical and subcortical egomotion network in a substantial cohort (n=131). Our results identify a core egomotion network consisting of areas in the cingulate sulcus (CSv, PcM/pCi), the cerebellum (uvula), and the temporo-parietal cortex including area VPS and an unnamed region in the supramarginal gyrus. Based on its cerebral connectivity pattern and anatomical localization, we propose that this region represents the human homologue of macaque area 7a. Whole-brain connectivity and gradient analyses imply an essential role of the connections between the cingulate sulcus and the cerebellar uvula in egomotion perception. This could be via feedback loops involved updating visuo-spatial and vestibular information. The unique functional connectivity patterns of PcM/pCi hint at central role in multisensory integration essential for the perception of self-referential spatial awareness. All cortical egomotion hubs showed modular functional connectivity with other visual, vestibular, somatosensory and higher order motor areas, underlining their mutual function in general sensorimotor integration.

Functional Connectivity in Compulsive Sexual Behavior Disorder - Systematic Review of Literature and Study on Heterosexual Males

BACKGROUND

Compulsive Sexual Behavior Disorder (CSBD) was recently included in ICD-11 as a new impulse control disorder. While this certainly improved the diagnosis of CSBD, the underlying brain mechanisms of the disorder are still poorly understood. Better description of brain functional deficits is required.

AIM

Here we investigate patterns of resting-state brain functional connectivity (fc) in a group of CSBD patients compared to a group of healthy controls (HC).

METHODS

A MATLAB toolbox named CONN functional connectivity toolbox was employed to study patterns of brain connectivity. Also correlation between fc and severity of CSBD symptoms and other psychological characteristics, assessed with questionnaires, were examined.

OUTCOMES

We collected resting-state functional magnetic resonance imaging data from 81 heterosexual males: 52 CSBD patients and 29 HC.

RESULTS

We found increased fc between left inferior frontal gyrus and right planum temporale and polare, right and left insula, right Supplementary Motor Cortex (SMA), right parietal operculum, and also between left supramarginal gyrus and right planum polare, and between left orbitofrontal cortex and left insula when compared CSBD and HC. The decreased fc was observed between left middle temporal gyrus and bilateral insula and right parietal operculum. No significant correlations between psychological questionnaires assessing CSBD symptoms and resting-state functional connectivity were observed.

CLINICAL IMPLICATIONS

Results from our study extend the knowledge of brain mechanisms differentiating CSBD from HC.

STRENGTHS & LIMITATIONS

The study was the first large sample study showing 5 distinct functional brain networks differentiating CSBD patients and HC. However, the sample was limited only to heterosexual men, in the future a greater diversity in studied sample and longitudinal studies are needed. Also, the present study examined functional connectivity at the level of regions of interest (ROIs). Future studies could verify these results by examining functional connectivity at the voxel level.

CONCLUSION

The identified functional brain networks differentiate CSBD from HC and provide some support for incentive sensitization as mechanism underlying CSBD symptoms. The correlation between psychological assessment (ie, severity of CSBD, depression and anxiety symptoms, level of impulsivity and compulsivity) and resting-state functional connectivity need further examination. Draps M, Adamus S, Wierzba M, et al. Functional Connectivity in Compulsive Sexual Behavior Disorder - Systematic Review of Literature and Study on Heterosexual Males. J Sex Med 2022;19:1463-1471.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

A graph convolutional neural network for the automated detection of seizures in the neonatal EEG

BACKGROUND AND OBJECTIVE

Neonatal seizures are the most common clinical presentation of neurological conditions and can have adverse effects on the neurodevelopment of the neonatal brain. Visual detection of these events from continuous EEG recordings is a laborious and time-consuming task. We propose a novel algorithm for the automated detection of neonatal seizures.

METHODS

In this study, we propose a novel deep learning model based on Graph Convolutional Neural Networks for the automated detection of neonatal seizures. Unlike other methods exploiting mainly the temporal information contained in EEG signals, our method also considers long-range spatial information, i.e., the interdependencies across EEG signals. The temporal information is embedded as graph signals in the graph representation of the EEG recordings and includes EEG features extracted from the EEG signals in the time and frequency domains. The spatial information is represented as functional connections among the EEG channels (calculated by the phase-locking value and the mean squared coherence) or as maps of Euclidean distances. These different spatial representations were evaluated to assess their efficiency in providing more discriminative features for an effective detection of neonatal seizures. The model performance was assessed on a publicly available dataset of continuous EEG signals recorded from 39 neonates by means of the area under the curve (AUC) and the AUC for specificity values greater than 90% (AUC90).

RESULTS

After applying post-processing, consisting in smoothing the output of the classifiers, the models based on the mean squared coherence, the phase-locking value, and the Euclidean distance respectively reached a median AUC of 99.1% (IQR: 96.8%-99.6%), 99% (IQR: 95.2%-99.7%), and 97.3% (IQR: 86.3%-99.6%), and a median AUC90 of 96%, 95.7%, and 94.9%. These values are superior or comparable to those reached by methods considered as state-of-the-art in this field.

CONCLUSIONS

Our results show that the EEG graph representations drawn from functional connectivity measures can effectively leverage interdependencies among EEG signals and lead to reliable detection of neonatal seizures. Furthermore, our model has the advantage of requiring only temporal annotations on seizures for the training phase, making it more appealing for clinical applications.

Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: A proof-of-concept EEG study

OBJECTIVE

Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation (ESS) for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity.

APPROACH

Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. Independent components of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks.

RESULTS

Independent Component analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions.

SIGNIFICANCE

The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.

Empathy, Defending, and Functional Connectivity While Witnessing Social Exclusion

Peers are present for most bullying episodes. Peers who witness bullying can play an important role in either stopping or perpetuating the behaviour. Defending can greatly benefit victimized peers. Empathy is strongly associated with defending. Yet, less is known about defenders' neural response to witnessing social distress, and how this response may relate to the link between empathy and defending. Forty-six first-year undergraduate students (M_age = 17.7; 37 women), with varied history of peer defending, underwent fMRI scanning while witnessing a depiction of social exclusion. Functional connectivity analysis was performed across brain regions that are involved in cognitive empathy, empathetic distress, and compassion. History of defending was positively associated with functional connectivity (Exclusion > Inclusion) between the left orbitofrontal cortex (OFC) - medial prefrontal cortex (MPFC), and right OFC - left and right amygdalae. Defending was negatively associated with functional connectivity between the left OFC - anterior cingulate cortex. The relationship between history of defending and empathy (specifically, empathetic perspective taking) was moderated by functional connectivity of the right OFC - left amygdala. These findings suggest that coactivation of brain regions involved in compassionate emotion regulation and empathetic distress play a role in the relationship between empathy and peer defending.

Disruption of cerebellar-cortical functional connectivity predicts balance instability in alcohol use disorder

BACKGROUND

A neural substrate of alcohol-related instability of gait and balance is the cerebellum. Whether disruption of neural communication between cerebellar and cortical brain regions exerts an influence on ataxia in alcohol use disorder (AUD) was the focus of this study.

METHODS

Study groups comprised 32 abstinent AUD participants and 22 age- and sex-matched healthy controls (CTL). All participants underwent clinical screening, motor testing, and resting-state functional MR imaging analyzed for functional connectivity (FC) among 90 regions across the whole cerebrum and cerebellum. Ataxia testing quantified gait and balance with the Fregly-Graybiel Ataxia Battery conducted with and without vision.

RESULTS

The AUD group achieved lower scores than the CTL group on balance performance, which was disproportionately worse for eyes open than eyes closed in the AUD relative to the CTL group. Differences in ataxia were accompanied by differences in FC marked by cerebellar-frontal and cerebellar-parietal hyperconnectivity and cortico-cortical hypoconnectivity in the AUD relative to the control group. Lifetime alcohol consumption correlated significantly with AUD-related FC aberrations, which explained upwards of 69% of the AUD ataxia score variance.

CONCLUSION

Heavy, chronic alcohol consumption is associated with disorganized neural communication among cerebellar-cortical regions and contributes to ataxia in AUD. Ataxia, which is known to accelerate with age and be exacerbated with AUD, can threaten functional independence. Longitudinal studies are warranted to address whether extended sobriety quells ataxia and normalizes aberrant FC contributing to instability.

The longitudinal development of large-scale functional brain networks for arithmetic ability from childhood to adolescence

Arithmetic ability is an important high-level cognitive function that requires interaction among multiple brain regions. Previous studies on arithmetic development have focused on task-induced activation in isolated brain regions or functional connectivity among particular seed regions. However, it remains largely unknown whether and how functional connectivity among large-scale brain modules contributes to arithmetic development. In the present study, we used a longitudinal sample of task-based functional magnetic resonance imaging (fMRI) data comprising 63 typically developing children, with two testing points being about two years apart. With graph theory, we examined the longitudinal development of large-scale brain modules for a multiplication task in younger (mean age 9.88 at time 1) and older children (mean age 12.34 at time 1), respectively. The results showed that the default-mode (DMN) and frontal-parietal networks (FPN) became increasingly segregated over time. Specifically, intra-connectivity within the DMN and FPN increased significantly with age, and inter-connectivity between the DMN and visual network decreased significantly with age. Such developmental changes were mainly observed in the younger children, but not in the older children. Moreover, the change in network segregation of the DMN was positively correlated with longitudinal gain in arithmetic performance in the younger children, and individual difference in network segregation of the FPN was positively correlated with arithmetic performance at time 2 in the older children. Taken together, the present results highlight the development of the functional architecture in large-scale brain networks from childhood to adolescence, which may provide insights into potential neural mechanisms underlying arithmetic development.

Multimodal Gradient Mapping of Rodent Hippocampus

The hippocampus plays a central role in supporting our coherent and enduring sense of self and our place in the world. Understanding its functional organisation is central to understanding this complex role. Previous studies suggest function varies along a long hippocampal axis, but there is disagreement about the presence of sharp discontinuities or gradual change along that axis. Other open questions relate to the underlying drivers of this variation and the conservation of organisational principles across species. Here, we delineate the primary organisational principles underlying patterns of hippocampal functional connectivity (FC) in the mouse using gradient analysis on resting state fMRI data. We further applied gradient analysis to mouse gene co-expression data to examine the relationship between variation in genomic anatomy and functional organisation. Two principal FC gradients along a hippocampal axis were revealed. The principal gradient exhibited a sharp discontinuity that divided the hippocampus into dorsal and ventral compartments. The second, more continuous, gradient followed the long axis of the ventral compartment. Dorsal regions were more strongly connected to areas involved in spatial navigation while ventral regions were more strongly connected to areas involved in emotion, recapitulating patterns seen in humans. In contrast, gene co-expression gradients showed a more segregated and discrete organisation. Our findings suggest that hippocampal functional organisation exhibits both sharp and gradual transitions and that hippocampal genomic anatomy exerts only a subtle influence on this organisation.

An insula-based network mediates the relation between rumination and interoceptive sensibility in the healthy population

BACKGROUND

Individuals sometimes continuously centered their attention on the same thoughts. When such process tends to be negative and self-referential, we delineated this mental state as rumination, which may undermine body's perception of endogenous signal, but little is known about the certainly relationship and the potential neural mechanisms.

METHODS

Rumination and interoceptive sensibility were measured by questionnaires, then insula-related network of rumination dimensions were examined by the whole brain resting-state functional connectivity (FC) in 479 college students, and whether the insula-based network mediate the relationship between rumination and interoceptive sensibility were tested.

RESULTS

Rumination (including brooding reflective pondering) and interoceptive sensibility showed positive correlations. The neural mechanisms of brooding and reflective pondering were all related to the insula-networks, to be specific, brooding was positively correlated with the FC between the left posterior insula (PI) and left parahippocampal gyrus/ hippocampus (PHG), reflective pondering were positively correlated with the FC between the insula subregion and the dorsolateral prefrontal cortex. Moreover, the relationship between brooding and interoceptive sensibility was mediated by the FC between left PI and left PHG.

LIMITATIONS

We just tested the relationship between rumination and interoceptive sensibility at a cross-sectional level, but it is unclear that whether the longitudinal relationship would be predicted by the related network.

CONCLUSIONS

Our findings provided new insights into neural mechanisms of brooding and reflective pondering, also the integration of brooding and interoceptive sensibility. The insula-related networks may contribute crucially to rumination and interoception.

Differentiating ictal/subclinical spikes and waves in childhood absence epilepsy by spectral and network analyses: A pilot study

OBJECTIVE

Childhood absence epilepsy (CAE) is a disease with distinct seizure semiology and electroencephalographic (EEG) features. Differentiating ictal and subclinical generalized spikes and waves discharges (GSWDs) in the EEG is challenging, since they appear to be identical upon visual inspection. Here, spectral and functional connectivity (FC) analyses were applied to routine EEG data of CAE patients, to differentiate ictal and subclinical GSWDs.

METHODS

Twelve CAE patients with both ictal and subclinical GSWDs were retrospectively selected for this study. The selected EEG epochs were subjected to frequency analysis in the range of 1-30 Hz. Further, FC analysis based on the imaginary part of coherency was used to determine sensor level networks.

RESULTS

Delta, alpha and beta band frequencies during ictal GSWDs showed significantly higher power compared to subclinical GSWDs. FC showed significant network differences for all frequency bands, demonstrating weaker connectivity between channels during ictal GSWDs.

CONCLUSION

Using spectral and FC analyses significant differences between ictal and subclinical GSWDs in CAE patients were detected, suggesting that these features could be used for machine learning classification purposes to improve EEG monitoring.

SIGNIFICANCE

Identifying differences between ictal and subclinical GSWDs using routine EEG, may improve understanding of this syndrome and the management of patients with CAE.

Contributions from resting state functional connectivity and familial risk to early adolescent-onset MDD: Results from the Adolescent Brain Cognitive Development study

BACKGROUND

Family history of Major Depressive Disorder (MDD) is a robust predictor of MDD onset, especially in early adolescence. We examined the relationships between familial risk for depression and alterations to resting state functional connectivity (rsFC) within the default mode network (wDMN) and between the DMN and the left/right hippocampus (DMN-LHIPP/DMN-RHIPP) to the risk for early adolescent MDD onset.

METHODS

We examined 9403 youth aged nine to eleven from the Adolescent Brain Cognitive Development study. Depressive symptoms were measured with the parent-reported Child Behavior Checklist. Both youth and their parents completed the Kiddie Schedule for Affective Disorders and Schizophrenia, which provided MDD diagnoses. A family history screen was administered to determine familial risk for depression. Youth underwent a resting state functional magnetic resonance imaging scan, providing us with rsFC data.

RESULTS

Negative wDMN rsFC was associated with child-reported current depression, both child- and parent-reported past depression, and parent-reported current depressive symptoms. No difference was found in wDMN, DMN-LHIPP or DMN-RHIPP rsFC in children with or without familial risk for depression. Familial risk for depression interacted with wDMN rsFC in association with child-reported past MDD diagnosis and parent-reported current depressive symptoms.

LIMITATIONS

Information such as length of depressive episodes and age of onset of depression was not collected.

CONCLUSIONS

Altered wDMN rsFC in youth at familial risk for depression may be associated with increased risk for MDD onset in adolescence, but longitudinal studies are needed to test this hypothesis.

Neural correlates of alcohol use disorder severity among nontreatment-seeking heavy drinkers: An examination of the incentive salience and negative emotionality domains of the alcohol and addiction research domain criteria

BACKGROUND

The Alcohol and Addiction Research Domain Criteria (AARDoC) propose that alcohol use disorder is associated with neural dysfunction in three primary domains: incentive salience, negative emotionality, and executive function. Prior studies in heavy drinking samples have examined brain activation changes associated with alcohol and negative affect cues, representing the incentive salience and negative emotionality domains, respectively. Yet studies examining such cue-induced changes in functional connectivity (FC) are relatively sparse.

METHODS

Nontreatment-seeking heavy drinking adults (N = 149, 56.0% male, 48.6% non-white, mean age 34.8 years (SD = 10.0)) underwent functional magnetic resonance imaging during presentation of alcohol, negative, and neutral pictures. We focused on FC changes involving the nucleus accumbens and amygdala in addition to activation and FC correlations with self-reported AUD severity.

RESULTS

For alcohol cues versus neutral cues, we observed accumbens FC changes in the cerebellum and prefrontal cortex (PFC), and amygdala FC changes with occipital, parietal, and hippocampal regions. AUD severity correlated positively with activation in the cerebellum (p < 0.05), accumbens FC in the cingulate gyri, somatosensory gyri, and cerebellum (p < 0.05), and with amygdala FC in the PFC and inferior parietal lobule (p < 0.05) for alcohol cues versus neutral cues. For negative cues versus neutral cues, we observed accumbens FC changes in the lateral temporal, occipital, and parietal regions, and amygdala FC changes in the fusiform and lingual gyri (p < 0.05).

CONCLUSIONS

The present findings provide empirical support for the AARDoC domains of incentive salience and negative emotionality and indicate that AUD severity is associated with salience and response control for reward cues. When covarying for differences in nonalcohol substance use and mood disorder diagnoses, AUD severity was also associated with emotional reactivity for negative cues.

Inferring functional connectivity through graphical directed information

Objective.

Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections.

Approach.

Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series.

Main results.

GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections.

Significance.

GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.

Aberrant Functional Connectivity of Basal Forebrain Subregions with Cholinergic System in Short-term and Chronic Insomnia Disorder

BACKGROUND

To systematically investigate structural and functional abnormalities in subregions of the basal forebrain (BF) using structural and resting-state fMRI, and to examine their clinical relevance in short-term and chronic insomnia disorder (ID).

METHODS

Thirty-four patients with short-term ID, 41 patients with chronic ID, and 46 healthy controls (HCs) were recruited. Grey matter volume and seed-based resting-state functional connectivity (RSFC) in each BF subregion (Ch1,2,3 and 4) were computed and compared among the three groups. Spearman correlation was used to estimate the relationships between MRI-based alterations and clinical variables.

RESULTS

The short-term group exhibited lower RSFC with the bilateral striatum and bilateral Ch_4 than HCs and the chronic group. In the left Ch_4, subjects in the chronic group exhibited lower RSFC with the left middle cingulate cortex than HCs and the short-term group. The short-term group exhibited lower RSFC with the left parahippocampal gyrus (PHG) than HCs and the chronic group. The chronic group exhibited the highest RSFC with the left middle frontal gyrus (MFG), followed by HCs and the short-term group. In the right Ch_4, the chronic group exhibited the lowest RSFC with the right superior temporal gyrus, followed by HCs and the short-term group. Moreover, in the short-term group, negative correlations were found between the left Ch_4 and left MFG RSFC and Epworth Sleepiness Scale scores.

CONCLUSIONS

These findings suggest that the Ch_4 may be a key node for establishing diagnostic and categorical biomarkers of ID, which could be useful in developing more effective treatment strategies for insomnia.

Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction

In this work, we proposed a novel architecture for real-time quantitative characterization of functional brain connectivity networks derived from Electroencephalogram (EEG). It consists of two main parts - calculation of Phase Lag Index (PLI) to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131µs, utilizes 71% logic resources, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS Application Specific Integrated Circuit (ASIC) technology.

Dynamic Functional Connectivity as a complex random walk: Definitions and the dFCwalk toolbox

•We have developed a framework to describe the dynamics of Functional Connectivity (dFC) estimated from brain activity time-series as a complex random walk in the space of possible functional networks. This conceptual and methodological framework considers dFC as a smooth reconfiguration process, combining "liquid" and "coordinated" aspects. Unlike other previous approaches, our method does not require the explicit extraction of discrete connectivity states.•In our previous work, we introduced several metrics for the quantitative characterization of the dFC random walk. First, dFC speed analyses extract the distribution of the time-resolved rate of reconfiguration of FC along time. These distributions have a clear peak (typical dFC speed, that can already serve as a biomarker) and fat tails (denoting deviations from Gaussianity that can be detected by suitable scaling analyses of FC network streams). Second, meta-connectivity (MC) analyses identify groups of functional links whose fluctuations co-vary in time and that define veritable dFC modules organized along specific dFC meta-hub controllers (differing from conventional FC modules and hubs). The decomposition of whole-brain dFC by MC allows performing dFC speed analyses separately for each of the detected dFC modules.•We present here blocks and pipelines for dFC random walk analyses that are made easily available through a dedicated MATLABⓇ toolbox (dFCwalk), openly downloadable. Although we applied such analyses mostly to fMRI resting state data, in principle our methods can be extended to any type of neural activity (from Local Field Potentials to EEG, MEG, fNIRS, etc.) or even non-neural time-series.

Multimodal MRI analysis of basal forebrain structure and function across the Alzheimer's disease spectrum

BACKGROUND

Dysfunction of the cholinergic basal forebrain (cBF) is associated with cognitive decline in Alzheimer's disease (AD). Multimodal MRI allows for the investigation of cBF changes in-vivo. In this study we assessed alterations in cBF functional connectivity (FC), mean diffusivity (MD), and volume across the spectrum of AD. We further assessed effects of amyloid pathology on these changes.

METHODS

Participants included healthy controls, and subjects with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or AD dementia (ADD) from the multicenter DELCODE study. Resting-state functional MRI (rs-fMRI) and structural MRI data was available for 477 subjects, and a subset of 243 subjects also had DTI data available. Differences between diagnostic groups were investigated using seed-based FC, volumetric, and MD analyses of functionally defined anterior (a-cBF) and posterior (p-cBF) subdivisions of a cytoarchitectonic cBF region-of-interest. In complementary analyses groups were stratified according to amyloid status based on CSF Aβ42/40 biomarker data, which was available in a subset of participants.

RESULTS

a-cBF and p-cBF subdivisions showed regional FC profiles that were highly consistent with previously reported patterns, but there were only minimal differences between diagnostic groups. Compared to controls, cBF volumes and MD were significantly different in MCI and ADD but not in SCD. The Aβ42/40 stratified analyses largely matched these results.

CONCLUSIONS

We reproduced subregion-specific FC profiles of the cBF in a clinical sample spanning the AD spectrum. At least in this multicentric cohort study, cBF-FC did not show marked changes along the AD spectrum, and multimodal MRI did not provide more sensitive measures of AD-related cBF changes compared to volumetry.

Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression

OBJECTIVE

New antidepressant treatments are needed that are effective, rapid acting, safe, and tolerable. Intermittent theta-burst stimulation (iTBS) is a noninvasive brain stimulation treatment that has been approved by the U.S. Food and Drug Administration for treatment-resistant depression. Recent methodological advances suggest that the current iTBS protocol might be improved through 1) treating patients with multiple sessions per day at optimally spaced intervals, 2) applying a higher overall pulse dose of stimulation, and 3) precision targeting of the left dorsolateral prefrontal cortex (DLPFC) to subgenual anterior cingulate cortex (sgACC) circuit. The authors examined the feasibility, tolerability, and preliminary efficacy of Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT), an accelerated, high-dose resting-state functional connectivity MRI (fcMRI)-guided iTBS protocol for treatment-resistant depression.

METHODS

Twenty-two participants with treatment-resistant depression received open-label SAINT. fcMRI was used to individually target the region of the left DLPFC most anticorrelated with sgACC in each participant. Fifty iTBS sessions (1,800 pulses per session, 50-minute intersession interval) were delivered as 10 daily sessions over 5 consecutive days at 90% resting motor threshold (adjusted for cortical depth). Neuropsychological testing was conducted before and after SAINT.

RESULTS

One participant withdrew, leaving a sample size of 21. Nineteen of 21 participants (90.5%) met remission criteria (defined as a score <11 on the Montgomery-Åsberg Depression Rating Scale). In the intent-to-treat analysis, 19 of 22 participants (86.4%) met remission criteria. Neuropsychological testing demonstrated no negative cognitive side effects.

CONCLUSIONS

SAINT, an accelerated, high-dose, iTBS protocol with fcMRI-guided targeting, was well tolerated and safe. Double-blinded sham-controlled trials are needed to confirm the remission rate observed in this initial study.

Optimising network modelling methods for fMRI

A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.

Identifying brain network topology changes in task processes and psychiatric disorders

A central goal in neuroscience is to understand how dynamic networks of neural activity produce effective representations of the world. Advances in the theory of graph measures raise the possibility of elucidating network topologies central to the construction of these representations. We leverage a result from the description of lollipop graphs to identify an iconic network topology in functional magnetic resonance imaging data and characterize changes to those networks during task performance and in populations diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of resting state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; resting scans becoming less default-like with more integrated sensory paths. The study of brain network topologies and their relationship to cognitive models of information processing raises new opportunities for understanding brain function and its disorders.

An Electroencephalography Connectomic Profile of Posttraumatic Stress Disorder

OBJECTIVE

The authors sought to identify brain regions whose frequency-specific, orthogonalized resting-state EEG power envelope connectivity differs between combat veterans with posttraumatic stress disorder (PTSD) and healthy combat-exposed veterans, and to determine the behavioral correlates of connectomic differences.

METHODS

The authors first conducted a connectivity method validation study in healthy control subjects (N=36). They then conducted a two-site case-control study of veterans with and without PTSD who were deployed to Iraq and/or Afghanistan. Healthy individuals (N=95) and those meeting full or subthreshold criteria for PTSD (N=106) underwent 64-channel resting EEG (eyes open and closed), which was then source-localized and orthogonalized to mitigate effects of volume conduction. Correlation coefficients between band-limited source-space power envelopes of different regions of interest were then calculated and corrected for multiple comparisons. Post hoc correlations of connectomic abnormalities with clinical features and performance on cognitive tasks were conducted to investigate the relevance of the dysconnectivity findings.

RESULTS

Seventy-four brain region connections were significantly reduced in PTSD (all in the eyes-open condition and predominantly using the theta carrier frequency). Underconnectivity of the orbital and anterior middle frontal gyri were most prominent. Performance differences in the digit span task mapped onto connectivity between 25 of the 74 brain region pairs, including within-network connections in the dorsal attention, frontoparietal control, and ventral attention networks.

CONCLUSIONS

Robust PTSD-related abnormalities were evident in theta-band source-space orthogonalized power envelope connectivity, which furthermore related to cognitive deficits in these patients. These findings establish a clinically relevant connectomic profile of PTSD using a tool that facilitates the lower-cost clinical translation of network connectivity research.

Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

Background

Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks.

Objective

In this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis.

Material and Methods

The functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method.

Results

Experimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band.

Conclusion

The current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.

Effect of Intrinsic Patterns of Functional Brain Connectivity in Moderating Antidepressant Treatment Response in Major Depression

OBJECTIVE

Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo.

METHODS

Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest-based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms.

RESULTS

Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity.

CONCLUSIONS

This study identified specific functional network-based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.

Connectomics and molecular imaging in neurodegeneration

Our understanding on human neurodegenerative disease was previously limited to clinical data and inferences about the underlying pathology based on histopathological examination. Animal models and in vitro experiments have provided evidence for a cell-autonomous and a non-cell-autonomous mechanism for the accumulation of neuropathology. Combining modern neuroimaging tools to identify distinct neural networks (connectomics) with target-specific positron emission tomography (PET) tracers is an emerging and vibrant field of research with the potential to examine the contributions of cell-autonomous and non-cell-autonomous mechanisms to the spread of pathology. The evidence provided here suggests that both cell-autonomous and non-cell-autonomous processes relate to the observed in vivo characteristics of protein pathology and neurodegeneration across the disease spectrum. We propose a synergistic model of cell-autonomous and non-cell-autonomous accounts that integrates the most critical factors (i.e., protein strain, susceptible cell feature and connectome) contributing to the development of neuronal dysfunction and in turn produces the observed clinical phenotypes. We believe that a timely and longitudinal pursuit of such research programs will greatly advance our understanding of the complex mechanisms driving human neurodegenerative diseases.

Oscillatory Properties of Functional Connections Between Sensory Areas Mediate Cross-Modal Illusory Perception

The presentation of simple auditory stimuli can significantly impact visual processing and even induce visual illusions, such as the auditory-induced double flash illusion (DFI). These cross-modal processes have been shown to be driven by occipital oscillatory activity within the alpha band. Whether this phenomenon is network specific or can be generalized to other sensory interactions remains unknown. The aim of the current study was to test whether cross-modal interactions between somatosensory-to-visual areas leading to the same (but tactile-induced) DFI share similar properties with the auditory DFI. We hypothesized that if the effects are mediated by the oscillatory properties of early visual areas per se, then the two versions of the illusion should be subtended by the same neurophysiological mechanism (i.e., the speed of the alpha frequency). Alternatively, if the oscillatory activity in visual areas predicting this phenomenon is dependent on the specific neural network involved, then it should reflect network-specific oscillatory properties. In line with the latter, results recorded in humans (both sexes) show a network-specific oscillatory profile linking the auditory DFI to occipital alpha oscillations, replicating previous findings, and tactile DFI to occipital beta oscillations, a rhythm typical of somatosensory processes. These frequency-specific effects are observed for visual (but not auditory or somatosensory) areas and account for auditory-visual connectivity in the alpha band and somatosensory-visual connectivity in the beta band. We conclude that task-dependent visual oscillations reflect network-specific oscillatory properties favoring optimal directional neural communication timing for sensory binding.SIGNIFICANCE STATEMENT We investigated the oscillatory correlates of the auditory- and tactile-induced double flash illusion (DFI), a phenomenon where two interleaved beeps (taps) set within 100 ms apart and paired with one visual flash induce the sensation of a second illusory flash. Results confirm previous evidence that the speed of individual occipital alpha oscillations predict the temporal window of the auditory-induced illusion. Importantly, they provide novel evidence that the tactile-induced DFI is instead mediated by the speed of individual occipital beta oscillations. These task-dependent occipital oscillations are shown to be mediated by the oscillatory properties of the neural network engaged in the task to favor optimal temporal integration between the senses.

Effect of Sex Hormones on Brain Connectivity Related to Sexual Function in Perimenopausal Women: A Resting-State fMRI Functional Connectivity Study

BACKGROUND

Perimenopause is associated with increased risk of depression, vasomotor symptoms, and sexual dysfunction.

AIMS

To explore the effect of sex hormones on the functional connectivity (FC) of different brain regions related to sexual function in perimenopausal women.

METHODS

32 premenopausal women (mean age, 47.75 ± 1.55 years) and 25 perimenopausal women (mean age, 51.60 ± 1.63 years) underwent sex hormone level measurements and resting-state fMRI.

MAIN OUTCOME MEASURES

Serum levels of sex hormones, including prolactin (PRL), follicle-stimulating hormone (FSH), luteotropic hormone (LH), estradiol (E2), free testosterone (free-T), and progesterone (P), were measured. 10 brain regions related to sexual function were selected according to a meta-analysis, and FCs of the selected regions of interest were calculated as Pearson's correlation coefficient.

RESULTS

Compared with premenopausal women, perimenopausal women showed increased FC between the right area 13 (A13_r) and the right medial superior frontal gyrus (mSFG), between the left dorsal granular insula (dIg_L) and the right superior frontal gyrus (SFG) (Gaussian random field-corrected at the voxel level, P < .001, and cluster level, P < .025). Furthermore, the PRL level was negatively correlated with the FC of A13_R with the right mSFG and the FC of dIg_L with the right SFG.

CLINICAL TRANSLATION

These findings may be applicable to assessing brain dysfunction with FC changes in women approaching menopause.

STRENGTHS & LIMITATIONS

This study is the first to evaluate a direct relationship between sex hormone levels and brain FC changes in women approaching menopause. Sexual function was not assessed, which may weaken the conclusions related to sexual function.

CONCLUSIONS

The results show that women approaching menopause suffered from aberrant intrinsic FC in regions related to sexual function, and reveal a direct relationship between serum sex hormone levels and FC changes related to sexual function. Lu W, Guo W, Cui D, et al. Effect of Sex Hormones on Brain Connectivity Related to Sexual Function in Perimenopausal Women: A Resting-State fMRI Functional Connectivity Study. J Sex Med 2019;16:711-720.