Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk.

Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.

Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG.

In this study we investigate systematic patterns of rapidly changing sensor-level interdependencies in resting MEG data obtained from 23 children experiencing reading difficulties (RD) and 27 non-impaired readers (NI). Three-minute MEG time series were band-passed and subjected to blind source separation (BSS) prior to estimating sensor interdependencies using the weighted phase synchronization measure (wPLI). Dynamic sensor-level network properties were then derived for two network metrics (global and local efficiency). The temporal decay of long-range temporal correlations in network metrics (LRTC) was quantified using the scaling exponent (SE) in detrended fluctuation analysis (DFA) plots. Having established the reliability of SE estimates as robust descriptors of network dynamics, we found that RD students displayed significantly reduced (a) overall sensor-level network organization across all frequency bands (global efficiency), and (b) temporal correlations between sensors covering the left temporoparietal region and the remaining sensors in the ß3 band (local efficiency). Importantly, both groups displayed scale-free global network connectivity dynamics. The direct application of DFA to MEG signals failed to reveal significant group differences. Results are discussed in relation to prior evidence for disrupted temporoparietal functional circuits for reading in developmental reading disability.

On the quantization of time-varying phase synchrony patterns into distinct functional connectivity microstates (FCµstates) in a multi-trial visual ERP paradigm.

The analysis of functional brain connectivity has been supported by various techniques encompassing spatiotemporal interactions between distinct areas and enabling the description of network organization. Different brain states are known to be associated with specific connectivity patterns. We introduce here the concept of functional connectivity microstates (FCµstates) as short lasting connectivity patterns resulting from the discretization of temporal variations in connectivity and mediating a parsimonious representation of coordinated activity in the brain. Modifying a well-established framework for mining brain dynamics, we show that a small sized repertoire of FCµstates can be derived so as to encapsulate both the inter-subject and inter-trial response variability and further provide novel insights into cognition. The main practical advantage of our approach lies in the fact that time-varying connectivity analysis can be simplified significantly by considering each FCµstate as prototypical connectivity pattern, and this is achieved without sacrificing the temporal aspects of dynamics. Multi-trial datasets from a visual ERP experiment were employed so as to provide a proof of concept, while phase synchrony was emphasized in the description of connectivity structure. The power of FCµstates in knowledge discovery is demonstrated through the application of network topology descriptors. Their time-evolution and association with event-related responses is explored.

Genetic risk for schizophrenia is associated with altered visually-induced gamma band activity: evidence from a population sample stratified polygenic risk.

Gamma oscillations (30-90 Hz) have been proposed as a signature of cortical visual information processing, particularly the balance between excitation and inhibition, and as a biomarker of neuropsychiatric diseases. Magnetoencephalography (MEG) provides highly reliable visual-induced gamma oscillation estimates, both at sensor and source level. Recent studies have reported a deficit of visual gamma activity in schizophrenia patients, in medication naive subjects, and high-risk clinical participants, but the genetic contribution to such a deficit has remained unresolved. Here, for the first time, we use a genetic risk score approach to assess the relationship between genetic risk for schizophrenia and visual gamma activity in a population-based sample drawn from a birth cohort. We compared visual gamma activity in a group (N = 104) with a high genetic risk profile score for schizophrenia (SCZ-PRS) to a group with low SCZ-PRS (N = 99). Source-reconstructed V1 activity was extracted using beamformer analysis applied to MEG recordings using individual MRI scans. No group differences were found in the induced gamma peak amplitude or peak frequency. However, a non-parametric statistical contrast of the response spectrum revealed more robust group differences in the amplitude of high-beta/gamma power across the frequency range, suggesting that overall spectral shape carries important biological information beyond the individual frequency peak. Our findings show that changes in gamma band activity correlate with liability to schizophrenia and suggest that the index changes to synaptic function and neuronal firing patterns that are of pathophysiological relevance rather than consequences of the disorder.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

BACKGROUND: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. NEW METHOD: Based on preprocessed MRI images from the organizers of a neuroimaging challenge,3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. RESULTS: In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. COMPARISON WITH EXISTING METHOD(S): The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. CONCLUSIONS: Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.

Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes.

We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

Exploiting the temporal patterning of transient VEP signals: a statistical single-trial methodology with implications to brain-computer interfaces (BCIs).

BACKGROUND: When visual evoked potentials (VEPs) are deployed in brain-computer interfaces (BCIs), the emphasis is put on stimulus design. In the case of transient VEPs (TVEPs) brain responses are never treated individually, i.e. on a single-trial (ST) basis, due to their poor signal quality. Therefore their main characteristic, which is the emergence during early latencies, remains unexplored. NEW METHOD: Following a pattern-analytic methodology, we investigated the possibility of using single-trial TVEP responses to differentiate between the different spatial locations where a particular visual stimulus appeared and decide whether it was attended or unattended by the subject. RESULTS: Covert spatial attention modulates the temporal patterning of TVEPs in such a way that a brief ST-segment, from a single synthesized sensor, is sufficient for a Mahalanobis-Taguchi (MT) system to decode subject's intention. COMPARISON WITH EXISTING METHOD(S): In contrast to previous VEP-based approaches, stimulus-related information and user's intention are being decoded from transient ST-signals via exploiting aspects of brain response in the temporal domain. CONCLUSIONS: We demonstrated that in the TVEP signals there is sufficient discriminative information, coming in the form of a temporal code. We were able to introduce an efficient scheme that can fully exploit this information for the benefit of online classification. The measured performance brings high expectations for incorporating these ideas in BCI-control.

A tensorial approach to access cognitive workload related to mental arithmetic from EEG functional connectivity estimates.

The association of functional connectivity patterns with particular cognitive tasks has long been a topic of interest in neuroscience, e.g., studies of functional connectivity have demonstrated its potential use for decoding various brain states. However, the high-dimensionality of the pairwise functional connectivity limits its usefulness in some real-time applications. In the present study, the methodology of tensor subspace analysis (TSA) is used to reduce the initial high-dimensionality of the pairwise coupling in the original functional connectivity network to a space of condensed descriptive power, which would significantly decrease the computational cost and facilitate the differentiation of brain states. We assess the feasibility of the proposed method on EEG recordings when the subject was performing mental arithmetic task which differ only in the difficulty level (easy: 1-digit addition v.s. 3-digit additions). Two different cortical connective networks were detected, and by comparing the functional connectivity networks in different work states, it was found that the task-difficulty is best reflected in the connectivity structure of sub-graphs extending over parietooccipital sites. Incorporating this data-driven information within original TSA methodology, we succeeded in predicting the difficulty level from connectivity patterns in an efficient way that can be implemented so as to work in real-time.

Analyzing functional brain connectivity by means of commute times: a new approach and its application to track event-related dynamics.

There is growing interest in studying the association of functional connectivity patterns with particular cognitive tasks. The ability of graphs to encapsulate relational data has been exploited in many related studies, where functional networks (sketched by different neural synchrony estimators) are characterized by a rich repertoire of graph-related metrics. We introduce commute times (CTs) as an alternative way to capture the true interplay between the nodes of a functional connectivity graph (FCG). CT is a measure of the time taken for a random walk to setout and return between a pair of nodes on a graph. Its computation is considered here as a robust and accurate integration, over the FCG, of the individual pairwise measurements of functional coupling. To demonstrate the benefits from our approach, we attempted the characterization of time evolving connectivity patterns derived from EEG signals recorded while the subject was engaged in an eye-movement task. With respect to standard ways, which are currently employed to characterize connectivity, an improved detection of event-related dynamical changes is noticeable. CTs appear to be a promising technique for deriving temporal fingerprints of the brain's dynamic functional organization.