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.