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A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.

Patterns of trial-averaged post-stimulus neural activity, e.g. event related potentials (ERPs) are traditionally interpreted as the correlates of cognitive operations. However, single-trial trajectories of neural responses approach these ERP components only in a loose and stochastic manner, questioning legitimacy of these components, proposed roles. Deconstructing the conventional ERP analyses, here we studied patterns of event related variability (ERV) in 2-6 month-old infants and adults using electroencephalography. Our analyses reveal that the ERP components are analogous to the biasing forces on the ongoing dynamics, which instrument the variability quenching and boosting events along neural trajectories. Moreover, the observed ERV possesses a rich temporal structure, modulated by both age and task, rivaling in complexity with that of the classical ERPs. Our findings suggest that since the early infancy, neural trajectories are actively controlled. This is compliant with the hypothesis that neural variability is a resource for cognitive processing, rather than unwanted noise.

The activation of the brain at rest is thought to be at the core of cognitive functions. There have been many attempts at characterizing the functional connectivity at rest from the structure. Recent attempts with diffusion kernel models point to the possibility of a single diffusion kernel that can give a good estimate of the functional connectivity. But our empirical investigations revealed that the hypothesis of a single scale best-fitting kernel across subjects is not tenable. Further, our experiments demonstrate that structure-function relationship across subjects seems to obey a multi-scale diffusion phenomenon. Based on this insight, we propose a multiple diffusion kernel model (MKL) along with a learning framework for estimating the optimal model parameters. We tested our hypothesis on 124 subjects' data from publicly available NKI_Rockland database. The results establish the viability of the proposed model and also demonstrate several promising features as compared to the single kernel approach. One of the key strengths of the proposed approach is that it does not require hand-tuning of model parameters but actually learns them as part of the optimization process. The learned parameters may be suitable candidates for future investigation of their role in distinguishing health and disease.

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 50 children between the ages of 6 and 17 years. Brain connectivity and graph theoretic methods have been particularly very useful in shedding light on the differences between high functioning autistic children compared to typically developing (TD) ones. However, very recent developments in network measures raise a cautionary note by highlighting gross under-and over-connectivity in ASD may be an oversimplified hypothesis. Thus the primary aim of our study is to investigate these notions in functional connectomics of ASD versus TD by subjecting the data to reproducibility experiments using two independent datasets.Further, we tested the hypothesis of alteration in network segregation and integration in the ASD subjects. We have analyzed the resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data from the University of California Los Angeles (UCLA) multimodal connectivity database (n=42 ASD, n=37 TD) and rs-fMRI data from the Autism Brain Imaging Data Exchange (ABIDE) (n=187 ASD, n=176 TD) dataset. We assessed the differences in connection strength between TD and ASD subjects. We also performed graph theoretical analysis to analyze the effect of disease on various network measures. Further, using the larger ABIDE dataset, we performed two-factor ANOVA test, to study the effect of age, disease and their interaction by classifying the TD and ASD participants into two cohorts: children (9-12 years, n=73 TD and n=87 ASD) and adolescents (13-16 years, n=103 TD and n=100 ASD). In ASD, we show the existence of atypical connectivity within and between functional networks as compared to TD. We also found in ASD both hypo-and hyperconnectivity within functional networks such as the default mode network (DMN). Further, graph theoretic analysis showed that there is significant effect of age and disease on modularity, clustering coefficient, and local efficiency. We also identified specific areas within the DMN, sensorimotor, visual and attention networks that are affected by age, disease and their interaction. Overall, our findings suggest that maturation, disease and their interaction are critical for unraveling the biological basis and developmental trajectory in ASD and other neuropsychiatric disorders.

Brain activity is intrinsically organised into spatiotemporal patterns, but it is still not clear whether these intrinsic patterns are functional or epiphenomenal. Using a simultaneous fMRI-EEG implementation of a well-known bistable visual task, we showed that the latent transient states in the intrinsic EEG oscillations can predict upcoming involuntarily perceptual transitions. The critical state predicting a dominant perceptual transition was characterised by the phase coupling between the precuneus (PCU), a key node of the Default Mode Network (DMN), and the primary visual cortex (V1). The interaction between the lifetime of this state and the PCU- > V1 Granger-causal effect is correlated with the perceptual fluctuation rate. Our study suggests that the brain’s endogenous dynamics are phenomenologically relevant, as they can elicit a diversion between potential visual processing pathways, while external stimuli remain the same. In this sense, the intrinsic DMN dynamics pre-empt the content of consciousness.

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