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.

Comparing different EEG connectivity methods in young males with ASD

Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.

Within-network brain connectivity in Crohn's disease patients with gadolinium deposition in the cerebellum

PURPOSE

Patients with Crohn's disease (CD) undergo multiple gadolinium-based contrast agent injections across their lifespan to enhance signal intensity of the intestinal wall and differentiate active from quiescent inflammatory disease. Thus, CD patients are prone to gadolinium accumulation in the brain and represent a non-neurological population to explore gadolinium-related brain toxicity. Possible effects are expected to be greater on the cerebellar network due to the high propensity of the dentate nucleus to accumulate gadolinium. Herein, we provide a whole-brain network analysis of resting-state fMRI dynamics in long-term quiescent CD patients with normal renal function and MRI evidence of gadolinium deposition in the brain.

METHODS

Fifteen patients with CD and 16 healthy age- and gender-matched controls were enrolled in this study. Relevant resting-state networks (RSNs) were identified using independent component analysis (ICA) from functional magnetic resonance imaging data. An unpaired two-sample t test (with age and sex as nuisance variables) was used to investigate between different RSNs. Clusters were determined by using threshold-free cluster enhancement and a family-wise error corrected cluster significance threshold of p < 0.05.

RESULTS

Patients showed significantly decreased resting-state functional connectivity (p < 0.05, FWE corrected) of several regions of the right frontoparietal (FPR) and the dorsal attention (DAN) RSNs. No differences between the two groups were found in the functional connectivity maps of all the other RSNs, including the cerebellar network.

CONCLUSION

Our findings suggest a non-significant impact of gadolinium deposition on within-network cerebellar functional connectivity of long-term quiescent CD patients.

Quantifying volume conducted potential using stimulation artefact in cortico-cortical evoked potentials

BACKGROUND

Cortico-cortical evoked potentials (CCEP) are a technique using low frequency stimulation to infer regions of cortical connectivity in patients undergoing Stereo-electroencephalographic (SEEG) monitoring for refractory epilepsy. Little attention has been given to volume conducted components of CCEP responses, and how they may inflate CCEP connectivity.

NEW METHOD

Using data from 37 SEEG-CCEPs patients, a novel method was developed to quantify stimulation artefact by measuring the peak-to-peak voltage difference in the first 10 ms after CCEP stimulation. Early responses to CCEP stimulation were also quantified by calculating the root mean square of the 10-100 ms period after each stimulation pulse. Both the early CCEP responses and amplitude of stimulation artefact were regressed by physical distance, stimulation waveform, stimulation intensity and tissue type to identify conduction related properties.

RESULTS

Both stimulation artefact and early responses were correlated strongly with the inverse square of the distance from the stimulating electrode. Once corrected for the inverse square distance from the electrode, stimulation artefact and CCEP responses showed a linear relationship, indicating a volume conducted component.

COMPARISON WITH EXISTING METHODS

This is the first study to use stimulation artefact to quantify volume conducted potentials, and is the first to quantify volume conducted potentials in SEEG. A single prior study utilizing electrocorticography has shown that parts of early CCEP responses are due to volume conduction.

CONCLUSIONS

The linear relationship between stimulation artefact amplitude and CCEP early responses, once corrected for distance, suggests that stimulation artefact can be used as a measure to quantify the volume conducted components.

Neural circuits of idiopathic Normal Pressure Hydrocephalus: A perspective review of brain connectivity and symptoms meta-analysis

Idiopathic normal pressure hydrocephalus (iNPH) is a prevalent reversible neurological disorder characterized by impaired locomotion, cognition and urinary control with ventriculomegaly. Symptoms can be relieved with cerebrospinal fluid drainage, which makes iNPH the leading cause of reversible dementia. Because of a limited understanding of pathophysiological mechanisms, unspecific symptoms and the high prevalence of comorbidity (i.e. Alzheimer's disease), iNPH is largely underdiagnosed. For these reasons, there is an urgent need for developing noninvasive quantitative biomarkers for iNPH diagnosis and prognosis. Structural and functional changes of brain circuits in relation to symptoms and treatment response are expected to deliver major advances in this direction. We review structural and functional brain connectivity findings in iNPH and complement those findings with iNPH symptom meta-analyses in healthy populations. Our goal is to reinforce our conceptualization of iNPH as to brain network mechanisms and foster the development of new hypotheses for future research and treatment options.

Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients

Blood oxygenation level dependent (BOLD) resting-state functional magnetic resonance imaging (rs-fMRI) may serve as a sensitive marker to identify possible changes in the architecture of large-scale networks following mild traumatic brain injury (mTBI). Differences in functional connectivity (FC) measurements derived from BOLD rs-fMRI may however be confounded by changes in local cerebrovascular physiology and neurovascular coupling mechanisms, without changes in the underlying neuronally driven connectivity of networks. In this study, multi-modal neuroimaging data including BOLD rs-fMRI, baseline cerebral blood flow (CBF0) and cerebrovascular reactivity (CVR; acquired using a hypercapnic gas breathing challenge) were collected in 23 subjects with reported mTBI (14.6±14.9 months post-injury) and 27 age-matched healthy controls. Despite no group differences in CVR within the networks of interest (P > 0.05, corrected), significantly higher CBF0 was documented in the mTBI subjects (P < 0.05, corrected), relative to the controls. A normalization method designed to account for differences in CBF0 post-mTBI was introduced to evaluate the effects of such an approach on reported group differences in network connectivity. Inclusion of regional perfusion measurements in the computation of correlation coefficients within and across large-scale networks narrowed the differences in FC between the groups, suggesting that this approach may elucidate unique changes in connectivity post-mTBI while accounting for shared variance with CBF0. Altogether, our results provide a strong paradigm supporting the need to account for changes in physiological modulators of BOLD in order to expand our understanding of the effects of brain injury on large-scale FC of cortical networks.

Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Identification

Early Mild Cognitive Impairment (EMCI) involves very subtle changes in brain pathological process, and thus identification of EMCI can be challenging. By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network modeling method for EMCI identification. Specifically, we employ the structural connectivity based on diffusion tensor imaging (DTI), as a constraint, to guide the regression of BOLD time series from resting state functional magnetic resonance imaging (rs-fMRI). In addition, we introduce multiscale persistent homology features to avoid the uncertainty of regularization parameter selection. An empirical study on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrates that the proposed method effectively improves classification performance compared with several competing approaches, and reasonably yields connectivity patterns specific to different diagnostic groups.

Modeling functional resting-state brain networks through neural message passing on the human connectome

In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer's disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.

Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks

OBJECTIVE

It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.

METHODS

Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.

RESULTS

MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.

CONCLUSION

Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Machine learning in resting-state fMRI analysis

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

Mapping the human middle longitudinal fasciculus through a focused anatomo-imaging study: shifting the paradigm of its segmentation and connectivity pattern

Τhe middle longitudinal fasciculus (MdLF) was initially identified in humans as a discrete subcortical pathway connecting the superior temporal gyrus (STG) to the angular gyrus (AG). Further anatomo-imaging studies, however, proposed more sophisticated but conflicting connectivity patterns and have created a vague perception on its functional anatomy. Our aim was, therefore, to investigate the ambiguous structural architecture of this tract through focused cadaveric dissections augmented by a tailored DTI protocol in healthy participants from the Human Connectome dataset. Three segments and connectivity patterns were consistently recorded: the MdLF-I, connecting the dorsolateral Temporal Pole (TP) and STG to the Superior Parietal Lobule/Precuneus, through the Heschl's gyrus; the MdLF-II, connecting the dorsolateral TP and the STG with the Parieto-occipital area through the posterior transverse gyri and the MdLF-III connecting the most anterior part of the TP to the posterior border of the occipital lobe through the AG. The lack of an established termination pattern to the AG and the fact that no significant leftward asymmetry is disclosed tend to shift the paradigm away from language function. Conversely, the theory of "where" and "what" auditory pathways, the essential relationship of the MdLF with the auditory cortex and the functional role of the cortical areas implicated in its connectivity tend to shift the paradigm towards auditory function. Allegedly, the MdLF-I and MdLF-II segments could underpin the perception of auditory representations; whereas, the MdLF-III could potentially subserve the integration of auditory and visual information.

An extension of Phase Linearity Measurement for revealing cross frequency coupling among brain areas

BACKGROUND

Brain areas need to coordinate their activity in order to enable complex behavioral responses. Synchronization is one of the mechanisms neural ensembles use to communicate. While synchronization between signals operating at similar frequencies is fairly straightforward, the estimation of synchronization occurring between different frequencies of oscillations has proven harder to capture. One specifically hard challenge is to estimate cross-frequency synchronization between broadband signals when no a priori hypothesis is available about the frequencies involved in the synchronization.

METHODS

In the present manuscript, we expand upon the phase linearity measurement, an iso-frequency synchronization metrics previously developed by our group, in order to provide a conceptually similar approach able to detect the presence of cross-frequency synchronization between any components of the analyzed broadband signals.

RESULTS

The methodology has been tested on both synthetic and real data. We first exploited Gaussian process realizations in order to explore the properties of our new metrics in a synthetic case study. Subsequently, we analyze real source-reconstructed data acquired by a magnetoencephalographic system from healthy controls in a clinical setting to study the performance of our metrics in a realistic environment.

CONCLUSIONS

In the present paper we provide an evolution of the PLM methodology able to reveal the presence of cross-frequency synchronization between broadband data.

Gamma electroencephalographic coherence and theory of mind in healthy subjects

PURPOSE

Structural brain imaging has revealed that damage to different brain regions may impair theory of mind (ToM) while functional imaging has shown that distributed neural circuits are activated by ToM and empathy. However, the coherence of the electroencephalogram (EEG) frequencies in a definite time span may change during these processes, indicating different neurophysiological correlates. This study evaluated the changes of EEG coherence during ToM tasks in comparison with Empathy, Physical causality, and baseline conditions, aiming to determine the neurophysiological correlates of ToM.

METHODS

Sixteen healthy adults underwent a visual activation paradigm using 30 comic strips concerning ToM, Empathy, or Physical causality during EEG recording. The interhemispheric coherence was estimated using a bivariate autoregressive (AR) parametric model. The coherence spectra were analyzed in the alpha, beta, and gamma frequency EEG bands.

RESULTS

Coherence analysis taking all of the responses showed that in the gamma band, in comparison with the Empathy, Physical causality, and baseline conditions, ToM was associated with significantly higher peaks between the frontal and parietal areas in the right hemisphere and, in comparison with the Physical causality and baseline conditions, in the left hemisphere. Analysis taking the correct responses confirmed these results.

CONCLUSIONS

In healthy adults, ToM processes are associated with immediate specific changes of brain connectivity, as expressed by high cortical coherence within the right frontal and parietal areas. These previously unexplored aspects indicate an online involvement of the right hemisphere networks in normal ToM. In patients with epilepsy, the study of EEG coherence during specific tasks may help determine the neural dysfunctions associated with impaired ToM. This article is part of the Special Issue "Epilepsy and social cognition across the lifespan".

Brain functional connectivity after cognitive-bias modification and behavioral changes in abstinent alcohol-use disorder patients

The use of the cognitive-bias modification (CBM) method has emerged as a therapeutic complement in the treatment of alcoholism, producing changes at behavioral and brain level. Nevertheless, the impact of the CBM procedure could be improved by the memory retrieval-extinction process (REP). Different studies have demonstrated that the retrieval of drug memories before extinction training later reduced the reinstatement of drug-seeking behavior. The main aim of this work was to study the effect of the CBM procedure itself, as well as in combination with the activation of alcohol-related memories, on the brain oscillatory activity of abstinent patients with alcohol-use disorder. The study sample comprised 33 patients divided into three groups: A-CBM (alcohol-related memory activation + CBM), N-CBM (neutral memory activation + CBM) and N-INT (no-intervention) groups. A resting-state EEG was obtained before and after each protocol, along with the assessment of the automatic action tendencies. A-CBM group showed a general alpha synchronization increase after the protocol, while the other groups did not show any significant change. Besides, A-CBM group showed significant intra and inter-group differences in the automatic action tendencies after the protocol, reflected in higher avoidance bias toward appetitive, aversive and without context alcohol-related stimuli. The alpha phase synchronization increase could be the neural manifestation of the conditioning produced between the alcohol-related stimuli and the automatic avoidance response. Moreover, the activation of the alcohol-related memories favors this conditioning with those alcohol-related stimuli associated with the activated memories, because it increases their threat level for the abstinence maintenance.

Low-calorie sweeteners augment tissue-specific insulin sensitivity in a large animal model of obesity

PURPOSES

Whether low-calorie sweeteners (LCS), such as sucralose and acesulfame K, can alter glucose metabolism is uncertain, particularly given the inconsistent observations relating to insulin resistance in recent human trials. We hypothesized that these discrepancies are accounted for by the surrogate tools used to evaluate insulin resistance and that PET ¹⁸FDG, given its capacity to quantify insulin sensitivity in individual organs, would be more sensitive in identifying changes in glucose metabolism. Accordingly, we performed a comprehensive evaluation of the effects of LCS on whole-body and organ-specific glucose uptake and insulin sensitivity in a large animal model of morbid obesity.

METHODS

Twenty mini-pigs with morbid obesity were fed an obesogenic diet enriched with LCS (sucralose 1 mg/kg/day and acesulfame K 0.5 mg/kg/day, LCS diet group), or without LCS (control group), for 3 months. Glucose uptake and insulin sensitivity were determined for the duodenum, liver, skeletal muscle, adipose tissue and brain using dynamic PET ¹⁸FDG scanning together with direct measurement of arterial input function. Body composition was also measured using CT imaging and energy metabolism quantified with indirect calorimetry.

RESULTS

The LCS diet increased subcutaneous abdominal fat by ≈ 20% without causing weight gain, and reduced insulin clearance by ≈ 40%, while whole-body glucose uptake and insulin sensitivity were unchanged. In contrast, glucose uptake in the duodenum, liver and brain increased by 57, 66 and 29% relative to the control diet group (P < 0.05 for all), while insulin sensitivity increased by 53, 55 and 28% (P < 0.05 for all), respectively. In the brain, glucose uptake increased significantly only in the frontal cortex, associated with improved metabolic connectivity towards the hippocampus and the amygdala.

CONCLUSIONS

In miniature pigs, the combination of sucralose and acesulfame K is biologically active. While not affecting whole-body insulin resistance, it increases insulin sensitivity and glucose uptake in specific tissues, mimicking the effects of obesity in the adipose tissue and in the brain.

Functional and effective connectivity based features of EEG signals for object recognition

Classifying different object categories is one of the most important aims of brain-computer interface researches. Recently, interactions between brain regions were studied using different methods, such as functional and effective connectivity techniques. Functional and effective connectivity techniques are applied to estimate human brain areas connectivity. The main purpose of this study is to compare classification accuracy of the most advanced functional and effective methods in order to classify 12 basic object categories using Electroencephalography (EEG) signals. In this paper, 19 channels EEG signals were collected from 10 healthy subjects; when they were visiting color images and instructed to select the target images among others. Correlation, magnitude square coherence, wavelet coherence (WC), phase synchronization and mutual information were applied to estimate functional cortical connectivity. On the other hand, directed transfer function, partial directed coherence, generalized partial directed coherence (GPDC) were used to obtain effective cortical connectivity. After feature extraction, the scalar feature selection methods including T-test and one-sided-anova were applied to rank and select the most informative features. The selected features were classified by a one-against-one support vector machine classifier. The results indicated that the use of different techniques led to different classifying accuracy and brain lobes analysis. WC and GPDC are the most accurate methods with performances of 80.15% and 64.43%, respectively.