Heritable anisotropy associated with cognitive impairments among patients with schizophrenia and their non-psychotic relatives in multiplex families

BACKGROUND

To test the functional implications of impaired white matter (WM) connectivity among patients with schizophrenia and their relatives, we examined the heritability of fractional anisotropy (FA) measured on diffusion tensor imaging data acquired in Pittsburgh and Philadelphia, and its association with cognitive performance in a unique sample of 175 multigenerational non-psychotic relatives of 23 multiplex schizophrenia families and 240 unrelated controls (total = 438).

METHODS

We examined polygenic inheritance (h2r) of FA in 24 WM tracts bilaterally, and also pleiotropy to test whether heritability of FA in multiple WM tracts is secondary to genetic correlation among tracts using the Sequential Oligogenic Linkage Analysis Routines. Partial correlation tests examined the correlation of FA with performance on eight cognitive domains on the Penn Computerized Neurocognitive Battery, controlling for age, sex, site and mother's education, followed by multiple comparison corrections.

RESULTS

Significant total additive genetic heritability of FA was observed in all three-categories of WM tracts (association, commissural and projection fibers), in total 33/48 tracts. There were significant genetic correlations in 40% of tracts. Diagnostic group main effects were observed only in tracts with significantly heritable FA. Correlation of FA with neurocognitive impairments was observed mainly in heritable tracts.

CONCLUSIONS

Our data show significant heritability of all three-types of tracts among relatives of schizophrenia. Significant heritability of FA of multiple tracts was not entirely due to genetic correlations among the tracts. Diagnostic group main effect and correlation with neurocognitive performance were mainly restricted to tracts with heritable FA suggesting shared genetic effects on these traits.

Seed-Based Connectivity Prediction of Initial Outcome of Subthalamic Nuclei Deep Brain Stimulation

Subthalamic nuclei deep brain stimulation (STN-DBS) is a well-established treatment for Parkinson's disease (PD). Some studies have confirmed the long-term efficacy is associated with brain connectivity; however, whether the initial outcome is associated with brain connectivity and efficacy of prediction based on these factors has not been well investigated. In the present study, a total of 98 patients were divided into a training set (n = 78) and a test set (n = 20). The stimulation and medication responses were calculated based on the motor performance. The functional and structural connectomes were established based on a public database and used to measure the association between stimulation response and brain connectivity. The prediction of initial outcome was achieved via a machine learning algorithm-support vector machine based on the model established with the training set. It was found that the initial outcome of STN-DBS was associated with functional/structural connectivities between the volume of tissue activated and multiple brain regions, including the supplementary motor area, precentral and frontal areas, cingulum, temporal cortex, and striatum. These factors could be used to predict the initial outcome, with an r value of 0.4978 (P = 0.0255). Our study demonstrates a correlation between a specific connectivity pattern and initial outcome of STN-DBS, which could be used to predict the initial outcome of DBS.

The physiological basis underlying functional connectivity differences in older adults: A multi-modal analysis of resting-state fMRI

The purpose of this study was to determine if differences in functional connectivity strength (FCS) with age were confounded by vascular parameters including resting cerebral blood flow (CBF₀), cerebrovascular reactivity (CVR), and BOLD-CBF coupling. Neuroimaging data were collected from 13 younger adults (24 ± 2 years) and 14 older adults (71 ± 4 years). A dual-echo resting state pseudo-continuous arterial spin labeling sequence was performed, as well as a BOLD breath-hold protocol. A group independent component analysis was used to identify networks, which were amalgamated into a region of interest (ROI). Within the ROI, FC strength (FCS) was computed for all voxels and compared across the groups. CBF₀, CVR and BOLD-CBF coupling were examined within voxels where FCS was different between young and older adults. FCS was greater in old compared to young (P = 0.001). When the effect of CBF₀, CVR and BOLD-CBF coupling on FCS was examined, BOLD-CBF coupling had a significant effect (P = 0.003) and group differences in FCS were not present once all vascular parameters were considered in the statistical model (P = 0.07). These findings indicate that future studies of FCS should consider vascular physiological markers in order to improve our understanding of aging processes on brain connectivity.

Adaptive and maladaptive brain functional network reorganization after stroke in hemianopia patients: an EEG-tracking study

OBJECTIVE

Hemianopia following occipital stroke is believed to be mainly due to local damage at or near the lesion site. Yet, MRI studies suggest functional connectivity network (FCN) reorganization also in distant brain regions. Because it is unclear if reorganization is adaptive or maladaptive, compensating for, or aggravating vision loss, we characterized FCNs electrophysiologically to explore local and global brain plasticity and correlated FCN reorganization with visual performance.

METHODS

Resting-state EEG was recorded in chronic, unilateral stroke patients and healthy age-matched controls (n=24 each). The correlation of oscillating EEG activity was calculated with the imaginary part of coherence between pairs of interested regions, and FCN graph theory metrics (degree, strength, clustering coefficient) were correlated with stimulus detection and reaction time.

RESULTS

Stroke brains showed altered FCNs in the alpha- and beta-band in numerous occipital, temporal and frontal brain structures. On a global level, FCN had a less efficient network organization while on the local level node networks reorganized especially in the intact hemisphere. Here, the occipital network was 58% more rigid (with a more "regular" network structure) while the temporal network was 32% more efficient (showing greater "small-worldness"), both of which correlated with worse or better visual processing, respectively.

CONCLUSIONS

Occipital stroke is associated with both local and global FCN reorganization, but this can be both, adaptive and maladaptive. We propose that the more "regular" FCN structure in the intact visual cortex indicates maladaptive plasticity where less processing efficacy with reduced signal/noise ratio may cause perceptual deficits in the intact visual field. In contrast, reorganization in intact temporal brain regions is presumably adaptive, possibly supporting enhanced peripheral movement perception.

The role of central autonomic nervous system dysfunction in Takotsubo syndrome: a systematic review

Introduction

Takotsubo syndrome (TTS), also known as stress cardiomyopathy or “broken heart” syndrome, is a mysterious condition that often mimics an acute myocardial infarction. Both are characterized by left ventricular systolic dysfunction. However, this dysfunction is reversible in the majority of TTS patients.

Purpose

Recent studies surprisingly demonstrated that TTS, initially perceived as a benign condition, has a long-term prognosis akin to myocardial infarction. Therefore, the health consequences and societal impact of TTS are not trivial. The pathophysiological mechanisms of TTS are not yet completely understood. In the last decade, attention has been increasingly focused on the putative role of the central nervous system in the pathogenesis of TTS.

Conclusion

In this review, we aim to summarize the state of the art in the field of the brain–heart axis, regional structural and functional brain abnormalities, and connectivity aberrancies in TTS.

Multi-Region Local Field Potential Signatures in Response to the Formalin-induced Inflammatory Stimulus in Male Rats

Pain can be ignited by noxious chemical (e.g., acid), mechanical (e.g., pressure), and thermal (e.g., heat) stimuli and generated by the activation of sensory neurons and their axonal terminals called nociceptors in the periphery. Nociceptive information transmitted from the periphery is projected to the central nervous system (thalamus, somatosensory cortex, insular, anterior cingulate cortex, amygdala, periaqueductal grey, prefrontal cortex, etc.) to generate a unified experience of pain. Local field potential (LFP) recording is one of the neurophysiological tools to investigate the combined neuronal activity, ranging from several hundred micrometers to a few millimeters (radius), located around the embedded electrode. The advantage of recording LFP is that it provides stable simultaneous activities in various brain regions in response to external stimuli. In this study, differential LFP activities from the contralateral anterior cingulate cortex (ACC), ventral tegmental area (VTA), and bilateral amygdala in response to peripheral noxious formalin injection were recorded in anesthetized male rats. The results indicated increased power of delta, theta, alpha, beta, and gamma bands in the ACC and amygdala but no change of gamma-band in the right amygdala. Within the VTA, intensities of the delta, theta, and beta bands were only enhanced significantly after formalin injection. It was found that the connectivity (i.t. the coherence) among these brain regions reduced significantly under the formalin-induced nociception, which suggests a significant interruption within the brain. With further study, it will sort out the key combination of structures that will serve as the signature for pain state.

Gray matter network properties show distinct associations with CSF p-tau 181 levels and amyloid status in individuals without dementia

Gray matter networks are altered with amyloid accumulation in the earliest stage of AD, and are associated with decline throughout the AD spectrum. It remains unclear to what extent gray matter network abnormalities are associated with hyperphosphorylated-tau (p-tau). We studied the relationship of cerebrospinal fluid (CSF) p-tau181 with gray matter networks in non-demented participants from the European Prevention of Alzheimer's Dementia (EPAD) cohort, and studied dependencies on amyloid and cognitive status. Gray matter networks were extracted from baseline structural 3D T1w MRI. P-tau181 and abeta were measured with the Roche cobas Elecsys System. We studied the associations of CSF biomarkers levels with several network's graph properties. We further studied whether the relationships of p-tau 181 and network measures were dependent on amyloid status and cognitive stage (CDR). We repeated these analyses for network properties at a regional level, where we averaged local network values across cubes within each of 116 areas as defined by the automated anatomical labeling (AAL) atlas. Amyloid positivity was associated with higher network size and betweenness centrality, and lower gamma, clustering and small-world coefficients. Higher CSF p-tau 181 levels were related to lower betweenness centrality, path length and lambda coefficients (all p < 0.01). Three-way interactions between p-tau181, amyloid status and CDR were found for path length, lambda and clustering (all p < 0.05): Cognitively unimpaired amyloid-negative participants showed lower path length and lambda values with higher CSF p-tau181 levels. Amyloid-positive participants with impaired cognition demonstrated lower clustering coefficients in association to higher CSF p-tau181 levels. Our results suggest that alterations in gray matter network clustering coefficient is an early and specific event in AD.

TMS-induced brain connectivity modulation in Genetic Generalized Epilepsy

OBJECTIVE

In epilepsy patients, Transcranial Magnetic Stimulation (TMS) may result in the induction and modulation of epileptiform discharges (EDs). We hereby investigate the modulatory effects of TMS on brain connectivity in Genetic Generalized Epilepsy (GGE) and explore their potential as a diagnostic biomarker in GGE.

METHODS

Patients with GGE (n=18) and healthy controls (n=11) were investigated with a paired-pulse TMS-EEG protocol. The brain network was studied at local and at global levels using Coherence as an EEG connectivity measure. Comparison of patients vs controls was performed in a time-resolved manner by analyzing comparatively pre- vs post-TMS brain networks.

RESULTS

There was statistically significant TMS-induced modulation of connectivity at specific frequency bands within groups and difference in TMS-induced modulation between the two groups. The most significant difference between patients and controls related to connectivity modulation in the γ band at 1-3 sec post-TMS (p=0.004).

CONCLUSIONS

TMS modulates the healthy and epileptic brain connectivity in different ways. Our results indicate that TMS-EEG connectivity analysis can be a basis for a diagnostic biomarker of GGE.

SIGNIFICANCE

The analysis identifies specific time periods and frequency bands of interest of TMS-induced connectivity modulation and elucidates the effect of TMS on the healthy and epileptic brain connectivity.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

Language recovery through a two-stage awake surgery in an aphasic patient with a voluminous left fronto-temporo-insular glioma: case report

Awake surgery is difficult in glioma patients with preoperative aphasia. A 29-year-old right-handed bilingual (Spanish/English) patient experienced intractable seizures with severe language disorders due to a voluminous left fronto-temporo-insular tumor. We performed awake procedure with initial laborious language mapping, but with real-time improvement throughout the debulking, allowing preservation of the connectivity. A substantial residue was left. Postoperative cognitive rehabilitation resulted in a dramatic functional improvement, in both languages, permitting a complementary awake surgery, this time with a perfect collaboration of the patient. This multistep strategy enabled 92% of resection while enhancing quality of life with language recovery and epilepsy control.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Sensory integration in interoception: Interplay between top-down and bottom-up processing

Although the neural systems supporting interoception have been outlined in general, the exact processes underlying the integration of visceral signals still await research. Based on the predictive coding concept, we aimed to reveal the neural networks responsible for the bottom-up (stimulus-dependent) and top-down (model-dependent) processing of interoceptive information. In a study of 30 female participants, we utilized two classical body perception experiments-the rubber hand illusion and a heartbeat detection task (cardioception), with the latter being implemented in fMRI settings. We interpreted a stronger rubber hand illusion, as measured by higher proprioceptive drift, as a tendency to rely on actual sensory experience, i.e., bottom-up processing, while lower proprioceptive drift served as an indicator of the prevalence of top-down, model-based influences. To reveal the bottom-up and top-down processes in cardioception, we performed a seed-based connectivity analysis of the heartbeat detection task, using as seeds the areas with known roles in sensory integration and entering proprioceptive drift as a covariate. The results revealed a left thalamus-dependent network positively associated with proprioceptive drift (bottom-up processing) and a left amygdala-dependent network negatively associated with drift (top-down processing). Bottom-up processing was related to thalamic connectivity with the left frontal operculum and anterior insula, anterior cingulate cortex, hypothalamus, right planum polare and right inferior frontal gyrus. Top-down processing was related to amygdalar connectivity with the rostral prefrontal cortex and an area involving the left frontal opercular and anterior insular cortex, with the latter area being an intersection of the two networks. Thus, we revealed the neural mechanisms underlying the integration of interoceptive information through the interaction between the current sensory experience and internal models.

Modeling brain connectivity dynamics in functional magnetic resonance imaging via particle filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that alterations in the interaction among brain areas can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.

From competition to cooperation: Visual neglect across the hemispheres

Visuospatial neglect is a frequent and disabling consequence of injuries to the right hemisphere. Patients with neglect show signs of impaired attention for left-sided events, which depends on dysfunction of fronto-parietal networks. After unilateral injury, such as stroke, these networks and their contralateral homologs can reorganize following multiple potential trajectories, which can be either adaptive or maladaptive. This article presents possible factors influencing the profile of evolution of neglect towards recovery or chronicity, and highlights potential mechanisms that may constrain these processes in time and space. The integrity of white matter pathways within and between the hemisphere appears to pose crucial connectivity constraints for compensatory brain plasticity from remote brain regions. Specifically, the availability of a sufficient degree of inter-hemispheric connectivity might be critical to shift the role of the undamaged left hemisphere in spatial neglect, from exerting maladaptive effects, to promoting compensatory activity.

Associations of dietary markers with brain volume and connectivity: A systematic review of MRI studies

The high prevalence of unhealthy dietary patterns and related brain disorders, such as dementia, emphasizes the importance of research that examines the effect of dietary factors on brain health. Identifying markers of brain health, such as volume and connectivity, that relate to diet is an important first step towards understanding the lifestyle determinants of healthy brain ageing. We conducted a systematic review of 52 studies (total n = 21,221 healthy participants aged 26-80 years, 55 % female) that assessed with a range of MRI measurements, which brain areas, connections, and cerebrovascular factors were associated with dietary markers. We report associations between regional brain measures and dietary health. Collectively, lower diet quality was related to reduced brain volume and connectivity, especially in white and grey matter of the frontal, temporal and parietal lobe, cingulate, entorhinal cortex and the hippocampus. Associations were also observed in connecting fibre pathways and in particular the default-mode, sensorimotor and attention networks. However, there were also some inconsistencies in research methods and findings. We recommend that future research use more comprehensive and consistent dietary measures, more representative samples, and examine the role of key subcortical regions previously highlighted in relevant animal work.

A systematic review of resting state functional MRI connectivity changes and cognitive impairment in multiple sclerosis

INTRODUCTION

Cognitive impairment in multiple sclerosis (MS) is increasingly being investigated with resting state functional MRI (rs-fMRI) functional connectivity (FC) . However, results remain difficult to interpret, showing both high and low FC associated with cognitive impairment. We conducted a systematic review of rs-fMRI studies in MS to understand whether the direction of FC change relates to cognitive dysfunction, and how this may be influenced by the choice of methodology.

METHODS

Embase, Medline and PsycINFO were searched for studies assessing cognitive function and rs-fMRI FC in adults with MS.

RESULTS

Fifty-seven studies were included in a narrative synthesis. Of these, 50 found an association between cognitive impairment and FC abnormalities. Worse cognition was linked to high FC in 18 studies, and to low FC in 17 studies. Nine studies found patterns of both high and low FC related to poor cognitive performance, in different regions or for different MR metrics. There was no clear link to increased FC during early stages of MS and reduced FC in later stages, as predicted by common models of MS pathology. Throughout, we found substantial heterogeneity in study methodology, and carefully consider how this may impact on the observed findings.

DISCUSSION

These results indicate an urgent need for greater standardisation in the field - in terms of the choice of MRI analysis and the definition of cognitive impairment. This will allow us to use rs-fMRI FC as a biomarker in future clinical studies, and as a tool to understand mechanisms underpinning cognitive symptoms in MS.

Qualitative Deficits in Verbal Fluency in Parkinson's Disease with Mild Cognitive Impairment: A Clinical and Neuroimaging Study

BACKGROUND

Mild cognitive impairment (MCI) in Parkinson's disease (PD) is considered a risk factor for PD with dementia (PDD). Verbal fluency tasks are widely used to assess executive function in PDD. However, in cases of PD with MCI (PD-MCI), the relative diagnostic accuracy of different qualitative verbal fluency measures and their related neural mechanisms remain unknown.

OBJECTIVE

This study aimed to investigate the relative diagnostic accuracy of qualitative (clustering and switching) verbal fluency strategies and their correlates with functional imaging in PD-MCI.

METHODS

Forty-five patients with PD (26 with MCI and 19 without MCI) and 25 healthy controls underwent comprehensive neurocognitive testing and resting-state functional magnetic resonance imaging. MCI in patients with PD was diagnosed according to established clinical criteria. The diagnostic accuracy of verbal fluency measures was determined via receiver operating characteristic analysis. Changes in brain functional connectivity between groups and across clinical measures were assessed using seed-to-voxel analyses.

RESULTS

Patients with PD-MCI generated fewer words and switched less frequently in semantic and phonemic fluency tasks compared to other groups. Switching in semantic fluency showed high diagnostic accuracy for PD-MCI and was associated with reduced functional connectivity in the salience network.

CONCLUSION

Our results indicate that reduced switching in semantic fluency tasks is a sensitive and specific marker for PD-MCI. Qualitative verbal fluency deficits and salience network dysfunction represent early clinical changes observed in PD-MCI.

Structural and functional brain connectivity in moderate-late preterm infants with low-grade intraventricular hemorrhage

PURPOSE

Brain functional connectivity (FC) changes and microstructural abnormalities are reported in infants born moderate and late preterm (MLPT). We evaluated the effect of low-grade (grades I, II) intraventricular hemorrhage (IVH) in MLPT babies on brain structural connectivity (SC) and FC.

METHODS

Babies born MLPT between January 2014 and May 2017 underwent brain ultrasound (US) at 72 h and 7 days after birth, and MRI at around term equivalent. The MRI protocol comprised T1- and T2-weighted sequences, diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). SC and FC were assessed using graph analysis.

RESULTS

Of 350 MLPT neonates, 15 showed low-grade IVH on US at 72 h, for which brain MRI was available in 10. These 10 infants, with mean gestational age (GA) 34.0 ± 0.8 weeks, comprised the study group, and 10 MLPT infants of mean GA 33.9 ± 1.1 weeks, with no abnormalities on brain US and MRI, were control subjects. All study subjects presented modularity, small world topology, and rich club organization for both SC and FC. The patients with low-grade IVH had lower FC rich club coefficient and lower SC betweenness centrality in the left frontoparietal operculum, and lower SC rich club coefficient in the right superior orbitofrontal cortex than the control subjects.

CONCLUSIONS

Topological and functional properties of mature brain connectivity are present in MLPT infants. IVH in these infants was associated with structural and functional abnormalities in the left frontoparietal operculum and right orbitofrontal cortex, regions related to language and cognition.

Resting-state brain connectivity in healthy young and middle-aged adults at risk of progressive Alzheimer's disease

Functional brain connectivity of the resting-state networks has gained recent attention as a possible biomarker of Alzheimer's Disease (AD). In this paper, we review the literature of functional connectivity differences in young adults and middle-aged cognitively intact individuals with non-modifiable risk factors of AD (n = 17). We focus on three main intrinsic resting-state networks: The Default Mode network, Executive network, and the Salience network. Overall, the evidence from the literature indicated early vulnerability of functional connectivity across different at-risk groups, particularly in the Default Mode Network. While there was little consensus on the interpretation on directionality, the topography of the findings showed frequent overlap across studies, especially in regions that are characteristic of AD (i.e., precuneus, posterior cingulate cortex, and medial prefrontal cortex areas). We conclude that while resting-state functional connectivity markers have great potential to identify at-risk individuals, implementing more data-driven approaches, further longitudinal and cross-validation studies, and the analysis of greater sample sizes are likely to be necessary to fully establish the effectivity and utility of resting-state network-based analyses.

Brain activation and connectivity in anorexia nervosa and body dysmorphic disorder when viewing bodies: relationships to clinical symptoms and perception of appearance

Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are characterized by distorted perception of appearance, yet no studies have directly compared the neurobiology associated with body perception. We compared AN and BDD in brain activation and connectivity in relevant networks when viewing images of others' bodies and tested their relationships with clinical symptoms and subjective appearance evaluations. We acquired fMRI data from 64 unmedicated females (20 weight-restored AN, 23 BDD, 21 controls) during a matching task using unaltered or spatial-frequency filtered photos of others' bodies. Using general linear model and independent components analyses we compared brain activation and connectivity in visual, striatal, and parietal networks and performed univariate and partial least squares multivariate analyses to investigate relationships with clinical symptoms and appearance evaluations. AN and BDD showed partially overlapping patterns of hyperconnectivity in the dorsal visual network and hypoconnectivity in parietal network compared with controls. BDD, but not AN, demonstrated hypoactivity in dorsal visual and parietal networks compared to controls. Further, there were significant activity and connectivity differences between AN and BDD in both networks. In both groups, activity and/or connectivity were associated with symptom severity and appearance ratings of others' bodies. Thus, AN and BDD demonstrate both distinct and partially-overlapping aberrant neural phenotypes involved in body processing and visually encoding global features. Nevertheless, in each disorder, aberrant activity and connectivity show relationships to clinically relevant symptoms and subjective perception. These results have implications for understanding distinct and shared pathophysiology underlying perceptual distortions of appearance and may inform future novel treatment strategies.

Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity

BACKGROUND

Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS).

METHOD

An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High).

RESULTS

The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class.

CONCLUSION

The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.

Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis

BACKGROUND AND OBJECTIVE

Machine learning frameworks have demonstrated their potentials in dealing with complex data structures, achieving remarkable results in many areas, including brain imaging. However, a large collection of data is needed to train these models. This is particularly challenging in the biomedical domain since, due to acquisition accessibility, costs and pathology related variability, available datasets are limited and usually imbalanced. To overcome this challenge, generative models can be used to generate new data.

METHODS

In this study, a framework based on generative adversarial network is proposed to create synthetic structural brain networks in Multiple Sclerosis (MS). The dataset consists of 29 relapsing-remitting and 19 secondary-progressive MS patients. T1 and diffusion tensor imaging (DTI) acquisitions were used to obtain the structural brain network for each subject. Evaluation of the quality of newly generated brain networks is performed by (i) analysing their structural properties and (ii) studying their impact on classification performance.

RESULTS

We demonstrate that advanced generative models could be directly applied to the structural brain networks. We quantitatively and qualitatively show that newly generated data do not present significant differences compared to the real ones. In addition, augmenting the existing dataset with generated samples leads to an improvement of the classification performance (F1_score 81%) with respect to the baseline approach (F1_score 66%).

CONCLUSIONS

Our approach defines a new tool for biomedical application when connectome-based data augmentation is needed, providing a valid alternative to usual image-based data augmentation techniques.

EEG hyperscanning in motor rehabilitation: a position paper

Studying the human brain during interpersonal interaction allows us to answer many questions related to motor control and cognition. For instance, what happens in the brain when two people walking side by side begin to change their gait and match cadences? Adapted from the neuroimaging techniques used in single-brain measurements, hyperscanning (HS) is a technique used to measure brain activity from two or more individuals simultaneously. Thus far, HS has primarily focused on healthy participants during social interactions in order to characterize inter-brain dynamics. Here, we advocate for expanding the use of this electroencephalography hyperscanning (EEG-HS) technique to rehabilitation paradigms in individuals with neurological diagnoses, namely stroke, spinal cord injury (SCI), Parkinson's disease (PD), and traumatic brain injury (TBI). We claim that EEG-HS in patient populations with impaired motor function is particularly relevant and could provide additional insight on neural dynamics, optimizing rehabilitation strategies for each individual patient. In addition, we discuss future technologies related to EEG-HS that could be developed for use in the clinic as well as technical limitations to be considered in these proposed settings.

Network reorganization during verbal fluency task in fronto-temporal epilepsy: A functional near-infrared spectroscopy study

This is the first study to use functional near-infrared spectroscopy (fNIRS) to investigate how the lateralization of the epileptogenic zone affects the reconfiguration of task-related network patterns. Eleven left fronto-temporal epilepsy (L-FTE) and 11 right fronto-temporal epilepsy (R-FTE), as well as 22 age- and gender-matched controls, were enrolled. Signals from 52-channel fNIRS were recorded while the subject was undertaking verbal fluency tasks (VFTs), which included categorical (CFT) and letter (LFT) fluency tasks. Three analytic methods were used to study the network topology: network-based analysis, hub identification, and proportional threshold to select the top 20% strongest connections for both graph theory parameters and clinical correlation. Performance of CFT is accomplished primarily using the ventral pathway, and bilateral ventral pathways are augmented in fronto-temporal epilepsy patients by strengthening the inter-hemispheric connections, especially for R-FTE. LFT mainly employed the dorsal pathway, and further prioritized the left dorsal pathway in strengthening intra-hemispheric connections in fronto-temporal epilepsy, especially L-FTE. The top 20% of the strongest connections only present differences in CFT network compared with the controls. R-FTE increased inter-hemispheric network density, while L-FTE decreased inter-hemispheric average characteristic path length. Accumulative seizure burden only affects L-FTE network. Better LFT performance and longer educational years seem to promote left fronto-temporal networks, and decreased the demand from RR intra-hemispheric connectivity in L-FTE. LFT scores in R-FTE are maintained by preserved RR intra-hemispheric networks. However, CFT scores and educational years seem to have no effect on the CFT network topology in both FTE.

Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity

Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, brain graph super-resolution is still poorly investigated because of the complex nature of non-Euclidean graph data. In this paper, we propose the first-ever deep graph super-resolution (GSR) framework that attempts to automatically generate high-resolution (HR) brain graphs with N^' nodes (i.e., anatomical regions of interest (ROIs)) from low-resolution (LR) graphs with N nodes where N<N^'. First, we formalize our GSR problem as a node feature embedding learning task. Once the HR nodes' embeddings are learned, the pairwise connectivity strength between brain ROIs can be derived through an aggregation rule based on a novel Graph U-Net architecture. While typically the Graph U-Net is a node-focused architecture where graph embedding depends mainly on node attributes, we propose a graph-focused architecture where the node feature embedding is based on the graph topology. Second, inspired by graph spectral theory, we break the symmetry of the U-Net architecture by super-resolving the low-resolution brain graph structure and node content with a GSR layer and two graph convolutional network layers to further learn the node embeddings in the HR graph. Third, to handle the domain shift between the ground-truth and the predicted HR brain graphs, we incorporate adversarial regularization to align their respective distributions. Our proposed AGSR-Net framework outperformed its variants for predicting high-resolution functional brain graphs from low-resolution ones. Our AGSR-Net code is available on GitHub at https://github.com/basiralab/AGSR-Net.