Penalized model-based clustering of fMRI data

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.

High spatial correlation in brain connectivity between micturition and resting states within bladder-related networks using 7 T MRI in multiple sclerosis women with voiding dysfunction

Background

Several studies have reported brain activations and functional connectivity (FC) during micturition using functional magnetic resonance imaging (fMRI) and concurrent urodynamics (UDS) testing. However, due to the invasive nature of UDS procedure, non-invasive resting-state fMRI is being explored as a potential alternative. The purpose of this study is to evaluate the feasibility of utilizing resting states as a non-invasive alternative for investigating the bladder-related networks in the brain.

Methods

We quantitatively compared FC in brain regions belonging to the bladder-related network during the following states: ‘strong desire to void’, ‘voiding initiation (or attempt at voiding initiation)’, and ‘voiding (or continued attempt of voiding)’ with FC during rest in nine multiple sclerosis women with voiding dysfunction using fMRI data acquired at 7 T and 3 T.

Results

The inter-subject correlation analysis showed that voiding (or continued attempt of voiding) is achieved through similar network connections in all subjects. The task-based bladder-related network closely resembles the resting-state intrinsic network only during voiding (or continued attempt of voiding) process but not at other states.

Conclusion

Resting states fMRI can be potentially utilized to accurately reflect the voiding (or continued attempt of voiding) network. Concurrent UDS testing is still necessary for studying the effects of strong desire to void and initiation of voiding (or attempt at initiation of voiding).

Supplementary Information

The online version contains supplementary material available at 10.1007/s00345-021-03599-4.

Acute aerobic exercise enhances cortical connectivity between structures involved in shaping mood and improves self-reported mood: An EEG effective-connectivity study in young male adults

There seems to be a general consensus among researchers that acute aerobic exercise (exercise hereafter) improves mood, but the neural mechanisms which drive these effects are far from being clear. The current study investigated the cortical connectivity patterns that underlie changes in mood after exercise. Twenty male adults underwent three different experimental protocols that were carefully controlled in terms of underlying metabolism and were administered in a randomized order: moderate-intensity continuous exercise, high-intensity interval exercise, and seated rest condition. Before and after each experimental protocol, we collected data on the participants' mood using the UMACL questionnaire and recorded their resting-state EEG. We focused on the effective connectivity patterns exerted by the dorso-lateral prefrontal cortex (dlPFC) over the temporal region (TMP), as these are important cortical structures involved in shaping mood. The cortical connectivity patterns in the resting-state EEG were evaluated using the directed transfer function (DTF), which is an autoregressive effective connectivity method. The results showed that both moderate-intensity exercise and high-intensity interval exercise improved participants' self-reported mood. Crucially, this improvement was accompanied by stronger influences of dlPFC over TMP. The observed changes in the effective connectivity patterns between dlPFC and TMP might help to better understand the effects of exercise on mood.

High-resolution connectomic fingerprints: Mapping neural identity and behavior

Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.

Dysfunction in nonsense-mediated decay, protein homeostasis, mitochondrial function, and brain connectivity in ALS-FUS mice with cognitive deficits

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) represent two ends of the same disease spectrum of adult-onset neurodegenerative diseases that affect the motor and cognitive functions, respectively. Multiple common genetic loci such as fused in sarcoma (FUS) have been identified to play a role in ALS and FTD etiology. Current studies indicate that FUS mutations incur gain-of-toxic functions to drive ALS pathogenesis. However, how the disease-linked mutations of FUS affect cognition remains elusive. Using a mouse model expressing an ALS-linked human FUS mutation (R514G-FUS) that mimics endogenous expression patterns, we found that FUS proteins showed an age-dependent accumulation of FUS proteins despite the downregulation of mouse FUS mRNA by the R514G-FUS protein during aging. Furthermore, these mice developed cognitive deficits accompanied by a reduction in spine density and long-term potentiation (LTP) within the hippocampus. At the physiological expression level, mutant FUS is distributed in the nucleus and cytosol without apparent FUS aggregates or nuclear envelope defects. Unbiased transcriptomic analysis revealed a deregulation of genes that cluster in pathways involved in nonsense-mediated decay, protein homeostasis, and mitochondrial functions. Furthermore, the use of in vivo functional imaging demonstrated widespread reduction in cortical volumes but enhanced functional connectivity between hippocampus, basal ganglia and neocortex in R514G-FUS mice. Hence, our findings suggest that disease-linked mutation in FUS may lead to changes in proteostasis and mitochondrial dysfunction that in turn affect brain structure and connectivity resulting in cognitive deficits.

Disentangling the effects of age and mild traumatic brain injury on brain network connectivity: A resting state fMRI study

INTRODUCTION

Cognitive complaints are common shortly after mild traumatic brain injury (mTBI) but may persist up to years. Age-related cognitive decline can worsen these symptoms. However, effects of age on mTBI sequelae have scarcely been investigated.

METHODS

Fifty-four mTBI patients (median age: 35 years, range 19-64 years, 67% male) and twenty age- and sex-matched healthy controls were studied using resting state functional magnetic resonance imaging in the sub-acute phase. Independent component analysis was used to identify intrinsic connectivity networks (ICNs). A multivariate approach was adopted to evaluate the effects of age and group on the ICNs in terms of (static) functional network connectivity (FNC), intensities of spatial maps (SMs) and time-course spectral power (TC).

RESULTS

We observed significant age-related changes for a) FNC: changes between 10 pairs of ICNs, mostly involving the default mode (DM) and/or the cognitive-control (CC) domains; b) SMs: intensity decrease in clusters across three domains and intensity increase in clusters across two domains, including the CC but not the DM and c) TC: spectral power decrease within the 0-0.15 Hz range and increase within the 0.20-0.25 Hz range for increasing age within networks located in frontal areas, including the anterior DM. Groups only differed for TC within the 0.065-0.10 Hz range in the cerebellar ICN and no age × group interaction effect was found.

CONCLUSIONS

We showed robust effects of age on connectivity between and within ICNs that are associated with cognitive functioning. Differences between mTBI patients and controls were only found for activity in the cerebellar network, increasingly recognized to participate in cognition. Our results suggest that to allow for capturing the true effects related to mTBI and its effects on cognitive functioning, age should be included as a covariate in mTBI studies, in addition to age-matching groups.

Theta and gamma connectivity is linked with affective and cognitive symptoms in Parkinson's disease

BACKGROUND

The progression of Parkinson's disease (PD) can often exacerbate symptoms of depression, anxiety, and/or cognitive impairment. In this study, we explore the possibility that multiple brain network responses are associated with symptoms of depression, anxiety and cognitive impairment in PD. This association is likely to provide insights into a single multivariate relationship, where common affective symptoms occurring in PD cohorts are related with alterations to electrophysiological response.

METHODS

70 PD patients and 21 healthy age-matched controls (HC) participated in a high-density electroencephalography (EEG) study. Functional connectivity differences between PD and HC groups of oscillatory activity at rest and during completion of an emotion-cognition task were examined to identify key brain oscillatory activities. A canonical correlation analysis (CCA) was applied to identify a putative multivariate relationship between connectivity patterns and affective symptoms in PD groups.

RESULTS

A CCA analysis identified a single mode of co-variation linking theta and gamma connectivity with affective symptoms in PD groups. Increases in frontotemporal gamma, frontal and parietal theta connectivity were related with increased anxiety and cognitive impairment. Decreases in temporal region theta and frontoparietal gamma connectivity were associated with higher depression ratings and PD patient age.

LIMITATIONS

This study only reports on optimal dosage of dopaminergic treatment ('on' state) in PD and did not investigate at "off" medication".

CONCLUSIONS

Theta and gamma connectivity during rest and task-states are linked to affective and cognitive symptoms within fronto-temporo-parietal networks, suggesting a potential assessment avenue for understanding brain-behaviour associations in PD with electrophysiological task paradigms.

Network topology of the marmoset connectome

The brain is a complex network of interconnected and interacting neuronal populations. Global efforts to understand the emergence of behavior and the effect of perturbations depend on accurate reconstruction of white matter pathways, both in humans and in model organisms. An emerging animal model for next-generation applied neuroscience is the common marmoset (Callithrix jacchus). A recent open respository of retrograde and anterograde tract tracing presents an opportunity to systematically study the network architecture of the marmoset brain (Marmoset Brain Architecture Project; http://www.marmosetbrain.org). Here we comprehensively chart the topological organization of the mesoscale marmoset cortico-cortical connectome. The network possesses multiple nonrandom attributes that promote a balance between segregation and integration, including near-minimal path length, multiscale community structure, a connective core, a unique motif composition, and multiple cavities. Altogether, these structural attributes suggest a link between network architecture and function. Our findings are consistent with previous reports across a range of species, scales, and reconstruction technologies, suggesting a small set of organizational principles universal across phylogeny. Collectively, these results provide a foundation for future anatomical, functional, and behavioral studies in this model organism.

Importance of self-connections for brain connectivity and spectral connectomics

Spectral analysis and neural field theory are used to investigate the role of local connections in brain connectivity matrices (CMs) that quantify connectivity between pairs of discretized brain regions. This work investigates how the common procedure of omitting such self-connections (i.e., the diagonal elements of CMs) in published studies of brain connectivity affects the properties of functional CMs (fCMs) and the mutually consistent effective CMs (eCMs) that correspond to them. It is shown that retention of self-connections in the fCM calculated from two-point activity covariances is essential for the fCM to be a true covariance matrix, to enable correct inference of the direct total eCMs from the fCM, and to ensure their compatibility with it; the deCM and teCM represent the strengths of direct connections and all connections between points, respectively. When self-connections are retained, inferred eCMs are found to have net inhibitory self-connections that represent the local inhibition needed to balance excitation via white matter fibers at longer ranges. This inference of spatially unresolved connectivity exemplifies the power of spectral connectivity methods, which also enable transformation of CMs to compact diagonal forms that allow accurate approximation of the fCM and total eCM in terms of just a few modes, rather than the full [Formula: see text] CM entries for connections between N brain regions. It is found that omission of fCM self-connections affects both local and long-range connections in eCMs, so they cannot be omitted even when studying the large-scale. Moreover, retention of local connections enables inference of subgrid short-range inhibitory connectivity. The results are verified and illustrated using the NKI-Rockland dataset from the University of Southern California Multimodal Connectivity Database. Deletion of self-connections is common in the field; this does not affect case-control studies but the present results imply that such fCMs must have self-connections restored before eCMs can be inferred from them.

Fronto-temporal brain activity and connectivity track implicit attention to positive and negative social words in a novel socio-emotional Stroop task

Previous inconsistencies on the effects of implicitly processing positively - vs. negatively - connotated emotional words might reflect the influence of uncontrolled psycholinguistic dimensions, and/or social facets inherent in putative "emotional" stimuli. Based on the relevance of social features in semantic cognition, we developed a socio-emotional Stroop task to assess the influence of social vs. individual (non-social) emotional content, besides negative vs. positive valence, on implicit word processing. The effect of these variables was evaluated in terms of performance and RTs, alongside associated brain activity/connectivity. We matched conditions for several psycholinguistic variables, and assessed a modulation of brain activity/connectivity by trial-wise RT, to characterize the maximum of condition- and subject-specific variability. RTs were tracked by insular and anterior cingulate activations likely reflecting implicit attention to stimuli, interfering with task-performance based on condition-specific processing of their subjective salience. Slower performance for negative than neutral/positive words was tracked by left-hemispheric structures processing negative stimuli and emotions, such as fronto-insular cortex, while the lack of specific activations for positively-connotated words supported their marginal facilitatory effect. The speeding/slowing effects of processing positive/negative individual emotional stimuli were enhanced by social words, reflecting in specific activations of the right anterior temporal and orbitofrontal cortex, respectively. RTs to social positive and negative words modulated connectivity from these regions to fronto-striatal and sensorimotor structures, respectively, likely promoting approach vs. avoidance dispositions shaping their facilitatory vs. inhibitory effect. These results might help assessing the neural correlates of impaired social cognition and emotional regulation, and the effects of rehabilitative interventions.

Editorial: Network Communication in the Brain

Communication models describe the flow of signals among nodes of a network. In neural systems, communication models are increasingly applied to investigate network dynamics across the whole brain, with the ultimate aim to understand how signal flow gives rise to brain function. Communication models range from diffusion-like processes to those related to infectious disease transmission and those inspired by engineered communication systems like the internet. This Focus Feature brings together novel investigations of a diverse range of mechanisms and strategies that could shape communication in mammal whole-brain networks.

Signal propagation via cortical hierarchies

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.

In the present report we asked how signals travel on brain networks and what types of nodes they potentially visit en route. We traced individual path motifs to investigate the propensity of communication paths to explore the putative unimodal-transmodal cortical hierarchy. We find that the architecture of the network promotes signaling via the hierarchy, suggesting a link between the structure and function of the network. Importantly, we also find instances where detours are promoted, particularly as paths traverse attention-related networks. Finally, information about hierarchical position aids navigation in some parts of the network, over and above spatial location. Altogether, the present results touch on several emerging themes in network neuroscience, including the nature of structure-function relationships, network communication and the role of cortical hierarchies.

Communicability distance reveals hidden patterns of Alzheimer's disease

The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer's disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer's disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

Estimating c-level partial correlation graphs with application to brain imaging

Alzheimer's disease (AD) is a chronic neurodegenerative disease that changes the functional connectivity of the brain. The alteration of the strong connections between different brain regions is of particular interest to researchers. In this article, we use partial correlations to model the brain connectivity network and propose a data-driven procedure to recover a $c$-level partial correlation graph based on PET data, which is the graph of the absolute partial correlations larger than a pre-specified constant $c$. The proposed procedure is adaptive to the "large p, small n" scenario commonly seen in whole brain studies, and it incorporates the variation of the estimated partial correlations, which results in higher power compared to the existing methods. A case study on the FDG-PET images from AD and normal control (NC) subjects discovers new brain regions, Sup Frontal and Mid Frontal in the frontal lobe, which have different brain functional connectivity between AD and NC.

Changes in brain glucose metabolism and connectivity in somatoform disorders: an 18F-FDG PET study

Somatoform disorders (SFD) are defined as a syndrome characterized by somatic symptoms which cannot be explained by organic reasons. Chronic or recurrent forms of somatization lead to heavy emotional and financial burden to the patients and their families. However, the underlying etiology of SFD is largely unknown. The purpose of this study is to investigate the changed brain glucose metabolic pattern in SFD. In this study, 18 SFD patients and 21 matched healthy controls were enrolled and underwent an ¹⁸F-FDG PET scan. First, we explored the altered brain glucose metabolism in SFD. Then, we calculated the mean ¹⁸F-FDG uptake values for 90 AAL regions, and detected the changed brain metabolic connectivity between the most significantly changed regions and all other regions. In addition, the Pearson coefficients between the neuropsychological scores and regional brain ¹⁸F-FDG uptake values were computed for SFD patients. We found that SFD patients showed extensive hypometabolism in bilateral superolateral prefrontal cortex, insula, and regions in bilateral temporal gyrus, right angular gyrus, left gyrus rectus, right fusiform gyrus, right rolandic operculum and bilateral occipital gyrus. The metabolic connectivity between right insula and prefrontal areas, as well as within prefrontal areas was enhanced in SFD. And several brain regions were associated with the somatic symptoms, including insula, putamen, middle temporal gyrus, superior parietal gyrus and orbital part of inferior frontal gyrus. Our study revealed widespread alterations of the brain glucose metabolic pattern in SFD patients. Those findings might elucidate the neuronal mechanisms with glucose metabolism and shed light on the pathology of SFD.

Magnetic resonance image connectivity analysis provides evidence of central nervous system mode of action for parasacral transcutaneous electro neural stimulation - A pilot study

INTRODUCTION

Parasacral transcutaneous electriconeural stimulation (pTENS) is a common treatment modality for patients with overactive bladder (OAB). Its mechanism of effectiveness has yet to be elucidated. Recent studies with fMRI in adults with implanted sacral nerve stimulators impute its effectiveness on changes in the brain involving the anterior cingulate cortex (ACC) and prefrontal cortex (PFC).

AIM

The study set out to evaluate brain connectivity utilizing functional MRI to the outline the mechanism of action of pTENS in the brain.

METHODS

Ten adult volunteers without urinary tract symptoms underwent fMRI. Electrodes were placed on the skin at sacral level (S2) (Experimental Stimulation - pTENS) and on the right scapular region (Sham Stimulation - sTENS). Stimulation was done twice on each site for 6 min at a frequency of 10 Hz and pulse width of 260 μs and intensity determined by the motor threshold. A 6 min resting state fMRI was also done twice as control. Functional connectivity data was acquired during each state (resting, pTENS and sTENS). Standard functional connectivity preprocessing was performed. Seed connectivity was examined to investigate changes in ACC functional connectivity between the stimulations and resting-state conditions. Significance was assessed at p < 0.05 corrected for multiple comparisons.

RESULTS

For all conditions (pTENS, sTENS, and rest), standard patterns of ACC connectivity were detectable with strong connectivity between the ACC and subcortical regions and between the ACC and the frontal lobe. Functional connectivity between ACC seed and the dorsal lateral prefrontal cortex (DLPFC) was significantly increased during pTENS compared to rest. sTENS did not increase connectivity between the ACC seed and DLPFC when compared to rest.

DISCUSSION

Preliminary results indicate that ACC is a major site of activation during pTENS. Increased connectivity between ACC and DLPFC may be a possible mechanism of pTENS effectiveness, which appears to be specific to pTENS compared to sTENS. This study is limited to the small size at this time which prevents further investigation at other sites in the brain.

CONCLUSIONS

The study confirms our original aim which was to define if parasacral TENS actually has a central effect.

Rotated spiral RARE for high spatial and temporal resolution volumetric arterial spin labeling acquisition

Background:

Arterial Spin Labeling (ASL) MRI can provide quantitative images that are sensitive to both time averaged blood flow and its temporal fluctuations. 3D image acquisitions for ASL are desirable because they are more readily compatible with background suppression to reduce noise, can reduce signal loss and distortion, and provide uniform flow sensitivity across the brain. However, single-shot 3D acquisition for maximal temporal resolution typically involves degradation of image quality through blurring or noise amplification by parallel imaging. Here, we report a new approach to accelerate a common stack of spirals 3D image acquisition by pseudo golden-angle rotation and compressed sensing reconstruction without any degradation of time averaged blood flow images.

Methods:

28 healthy volunteers were imaged at 3T with background-suppressed unbalanced pseudo-continuous ASL combined with a pseudo golden-angle Stack-of-Spirals 3D RARE readout. A fully-sampled perfusion-weighted volume was reconstructed by 3D non-uniform Fast Fourier Transform (nuFFT) followed by sum-of-squares combination of the 32 individual channels. Coil sensitivities were estimated followed by reconstruction of the 39 single-shot volumes using an L₁-wavelet Compressed-Sensing reconstruction. Finally, brain connectivity analyses were performed in regions where BOLD signal suffers from low signal-to-noise ratio and susceptibility artifacts.

Results:

Image quality, assessed with a non-reference 3D blurring metric, of full time averaged blood flow was comparable to a conventional interleaved acquisition. The temporal resolution provided by the acceleration enabled identification and quantification of resting-state networks even in inferior regions such as the amygdala and inferior frontal lobes, where susceptibility artifacts can degrade conventional resting-state fMRI acquisitions.

Conclusion:

This approach can provide measures of blood flow modulations and resting-state networks for free within any research or clinical protocol employing ASL for resting blood flow.

Changes in resting-state brain connectivity following computerized cognitive behavioral therapy for insomnia in dialysis patients: A pilot study

OBJECTIVE

Insomnia is prevalent among dialysis patients and affects their mortality. Although cognitive behavioral therapy for insomnia (CBTi) is recommended, attending regular face-to-face CBTi sessions is a major challenge for patients. We evaluated the effectiveness of a self-directed computerized CBTi (cCBTi) in dialysis patients, and investigated changes in resting-state brain connectivity and inflammatory cytokines following cCBTi.

METHOD

Thirty-five patients undergoing maintenance hemodialysis or peritoneal dialysis who had insomnia were screened for participation in the study, with 17 participants included in the final analyses. A self-directed cCBTi protocol accessed via tablet computer during dialysis or at home was developed and applied. Information about sleep, anxiety, depression, laboratory data, and resting-state functional magnetic resonance imaging data was obtained 3-5 days before and after cCBTi.

RESULTS

cCBTi improved sleep quality, and this was correlated with increased resting-state brain connectivity between the default-mode network and the premotor/dorsolateral prefrontal cortex. The decrement of interleukin-1β levels were correlated with improved sleep quality and increased brain connectivity after cCBTi.

CONCLUSION

Our pilot study findings suggest that cCBTi is effective for dialysis patients with insomnia, and the therapeutic effects of cCBTi are related to changes in brain functional connectivity and inflammatory cytokines.

Using structural connectivity to augment community structure in EEG functional connectivity

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

An Updated Review of Tuberous Sclerosis Complex-Associated Autism Spectrum Disorder

Tuberous sclerosis complex (TSC) is a neurocutaneous disorder caused by mutations of either the TSC1 or TSC2 gene. Various neuropsychiatric features, including autism, are prevalent in TSC. Recently, significant progress has been possible with the prospective calculation of the prevalence of autism in TSC, identification of early clinical and neurophysiological biomarkers to predict autism, and investigation of different therapies to prevent autism in this high-risk population. The author provides a narrative review of recent findings related to biomarkers for diagnosis of autism in TSC, as well as recent studies related to the management of TSC-associated autism. Further sophisticated modeling and analysis are required to understand the role of different models-tuber models, seizures and related neurophysiological factors models, genotype models, and brain connectivity models-to unravel the neurobiological basis of autism in TSC. Early neuropsychologic assessments may be beneficial in this high-risk group. Targeted intervention to improve visual skill, cognition, and fine motor skills with later addition of social skill training can be helpful. Multicenter, prospective studies are ongoing to identify if presymptomatic treatment with vigabatrin in patients with TSC can improve outcomes, including autism. Several studies indicated reasonable safety of everolimus in young children, and its potential application in high-risk infants with TSC, before the closure of the temporal window of permanent changes, maybe undertaken shortly.

Resting-State Functional Connectivity: Signal Origins and Analytic Methods

Resting state functional connectivity (RSFC) has been widely studied in functional magnetic resonance imaging (fMRI) and is observed by a significant temporal correlation of spontaneous low-frequency signal fluctuations (SLFs) both within and across hemispheres during rest. Different hypotheses of RSFC include the biophysical origin hypothesis and cognitive origin hypothesis, which show that the role of SLFs and RSFC is still not completely understood. Furthermore, RSFC and age studies have shown an "age-related compensation" phenomenon. RSFC data analysis methods include time domain analysis, seed-based correlation, regional homogeneity, and principal and independent component analyses. Despite advances in RSFC, the authors also discuss challenges and limitations, ranging from head motion to methodological limitations.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FC_EEG to second range of FC_fMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.

A case of co-occuring synesthesia, autism, prodigious talent and strong structural brain connectivity

BACKGROUND

Synesthesia is a sensory phenomenon where certain domain-specific stimuli trigger additional sensations of e.g. color or texture. The condition occurs in about 4% of the general population, but is overrepresented in individuals with Autism Spectrum Disorder (ASD), where it might also be associated with the presence of prodigious talents.

CASE PRESENTATION

Here we describe the case of a young transsexual man with Asperger Syndrome, synesthesia and a prodigious talent for foreign language acquisition. In our case, not only letters, numbers, spoken words, music, noises, weekdays and months lead to highly consistent, vivid color sensations but also his own and others' emotions, geometric shapes, any mathematical symbol, and letters from an unfamiliar alphabet (Hebrew). These color associations seem to aid categorization, differentiation and storage of information and might thereby contribute to the young man's language acquisition ability. We investigated the young man's structural brain connectivity in comparison to adults with or without ASD, applying global fiber tracking to diffusion-weighted Magnetic Resonance Imaging (MRI) data. The case presented with increased connectivity, especially between regions involved in visual and emotion processing, memory, and higher order associative binding regions. An electroencephalography experiment investigating synesthetic color and shape sensations while listening to music showed a negligible occipital alpha suppression, indicating that these internally generated synesthetic sensations derive from a different brain mechanism than when processing external visual information.

CONCLUSIONS

Taken together, this case study endorses the notion of a link between synesthesia, prodigious talent and autism, adding to the currently still sparse literature in this field. It provides new insights into the possible manifestations of synesthesia in individuals with ASD and its potential contribution to prodigious talents in people with an otherwise unexceptional cognitive profile. Additionally, this case impressively illustrates how synesthesia can be a key element not only of sensory perception but also social and emotional processing and contributes to existing evidence of increased brain connectivity in association with synesthesia.

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Use of complex visual stimuli allows controlled recruitment of cortical networks in infants

OBJECTIVE

To characterize cortical networks activated by patterned visual stimuli in infants, and to evaluate their potential for assessment of visual processing and their associations with neurocognitive development.

METHODS

Three visual stimuli, orientation reversal (OR), global form (GF), and global motion (GM), were presented to cohort of five-month-old infants (N = 26). Eye tracker was used to guide the stimulation and to choose epochs for analysis. Visual responses were recorded with electroencephalography and analysed in source space using weighted phase lag index as the connectivity measure. The networks were quantified using several metrics that were compared between stimuli and correlated to cognitive outcomes.

RESULTS

Responses to OR/GF/GM stimuli were observed in nearly all (96/100/100%) recordings. All stimuli recruited cortical networks that were partly condition-specific in their characteristics. The more complex GF and GM conditions recruited wider global networks than OR. Additionally, strength of the GF network showed positive association with later cognitive performance.

CONCLUSIONS

Network analysis suggests that visual stimulation recruits large-scale cortical networks that extend far beyond the conventional visual streams and that differ between stimulation conditions.

SIGNIFICANCE

The method allows controlled recruitment of wide cortical networks, which holds promise for the early assessment of visual processing and its related higher-order cognitive processes.