Henrique M. Fernandes

Structural connectivity in schizophrenia and its impact on the dynamics of spontaneous functional networks

The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. Using functional magnetic resonance imaging, it has been found that the large-scale resting-state functional connectivity (rsFC) in schizophrenia--measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal--exhibit altered network topology, with lower small-world index. The origin of these rsFC alterations and link with the underlying structural connectivity remain unclear. In this work, we used a computational model of spontaneous large-scale brain activity to explore the role of the structural connectivity in the large-scale dynamics of the brain in health and schizophrenia. The structural connectomes from 15 adolescent patients with early-onset schizophrenia and 15 age- and gender-matched controls were built from diffusion tensor imaging data to detect the white matter tracts between 90 brain areas. Brain areas, simulated using a reduced dynamic mean-field model, receive excitatory input from other areas in proportion to the number of fibre tracts between them. The simulated mean field activity was transformed into BOLD signal, and the properties of the simulated functional networks were analyzed. Our results suggest that the functional alterations observed in schizophrenia are not directly linked to alterations in the structural topology. Instead, subtly randomized and less small-world functional networks appear when the brain operates with lower global coupling, which shifts the dynamics from the optimal healthy regime.

The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling

Abstract In order to promote survival through flexible cognition and goal‐directed behaviour, the brain has to optimize segregation and integration of information into coherent, distributed dynamical states. Certain organizational features of the brain have been proposed to be essential to facilitate cognitive flexibility, especially hub regions in the so‐called rich club which show dense interconnectivity. These structural hubs have been suggested to be vital for integration and segregation of information. Yet, this has not been evaluated in terms of resulting functional temporal dynamics. A complementary measure covering the temporal aspects of functional connectivity could thus bring new insights into a more complete picture of the integrative nature of brain networks. Here, we use causal whole‐brain computational modelling to determine the functional dynamical significance of the rich club and compare this to a new measure of the most functionally relevant brain regions for binding information over time (“dynamical workspace of binding nodes”). We found that removal of the iteratively generated workspace of binding nodes impacts significantly more on measures of integration and encoding of information capability than the removal of the rich club regions. While the rich club procedure produced almost half of the binding nodes, the remaining nodes have low degree yet still play a significant role in the workspace essential for binding information over time and as such goes beyond a description of the structural backbone. HighlightsWe propose a novel method for finding the most functionally relevant brain regions for binding information over time.This “dynamical workspace of binding nodes” is determined using causal whole‐brain computational modelling.We compare the functional dynamical significance of binding nodes to the rich club members.Removal of binding nodes compared to rich club nodes significantly decreases integration.

Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome

Deep brain stimulation (DBS) is a remarkably effective clinical tool, used primarily for movement disorders. DBS relies on precise targeting of specific brain regions to rebalance the oscillatory behaviour of whole-brain neural networks. Traditionally, DBS targeting has been based upon animal models (such as MPTP for Parkinson’s disease) but has also been the result of serendipity during human lesional neurosurgery. There are, however, no good animal models of psychiatric disorders such as depression and schizophrenia, and progress in this area has been slow. In this paper, we use advanced tractography combined with whole-brain anatomical parcellation to provide a rational foundation for identifying the connectivity ‘fingerprint’ of existing, successful DBS targets. This knowledge can then be used pre-surgically and even potentially for the discovery of novel targets. First, using data from our recent case series of cingulate DBS for patients with treatment-resistant chronic pain, we demonstrate how to identify the structural ‘fingerprints’ of existing successful and unsuccessful DBS targets in terms of their connectivity to other brain regions, as defined by the whole-brain anatomical parcellation. Second, we use a number of different strategies to identify the successful fingerprints of structural connectivity across four patients with successful outcomes compared with two patients with unsuccessful outcomes. This fingerprinting method can potentially be used pre-surgically to account for a patient’s individual connectivity and identify the best DBS target. Ultimately, our novel fingerprinting method could be combined with advanced whole-brain computational modelling of the spontaneous dynamics arising from the structural changes in disease, to provide new insights and potentially new targets for hitherto impenetrable neuropsychiatric disorders.

How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brains wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

Insights into brain architectures from the homological scaffolds of functional connectivity networks

In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brains functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brains complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffold and comparing it to local graph metrics traditionally employed in neuroimaging studies. We conclude that the persistence scaffold enables the identification of network elements that may support the functional integration of information across distributed brain networks.

Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson's disease.

Deep brain stimulation (DBS) for Parkinson’s disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson’s disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments.

Brain fingerprints of olfaction: a novel structural method for assessing olfactory cortical networks in health and disease

Olfactory deficits are a common (often prodromal) symptom of neurodegenerative or psychiatric disorders. As such, olfaction could have great potential as an early biomarker of disease, for example using neuroimaging to investigate the breakdown of structural connectivity profile of the primary olfactory networks. We investigated the suitability for this purpose in two existing neuroimaging maps of olfactory networks. We found problems with both existing neuroimaging maps in terms of their structural connectivity to known secondary olfactory networks. Based on these findings, we were able to merge the existing maps to a new template map of olfactory networks with connections to all key secondary olfactory networks. We introduce a new method that combines diffusion tensor imaging with probabilistic tractography and pattern recognition techniques. This method can obtain comprehensive and reliable fingerprints of the structural connectivity underlying the neural processing of olfactory stimuli in normosmic adults. Combining the novel proposed method for structural fingerprinting with the template map of olfactory networks has great potential to be used for future neuroimaging investigations of olfactory function in disease. With time, the proposed method may even come to serve as structural biomarker for early detection of disease.

Disrupted connectivity in schizophrenia: modelling the impact of structural connectivity changes on the dynamics of spontaneous functional networks

The neuropathology of schizophrenia remains unclear. Some insight has come from modern neuroimaging techniques, which offer an unparalleled opportunity to explore in vivo the structure and function of the brain. In particular, a number of studies have found significant alterations in large-scale resting-state functional connectivity (FC) in the disease. The origin of these FC alterations and its potential link with the underlying structure, remain unclear. The FC between brain areas during rest (measured as the temporal correlations of the blood-oxygen-level-dependent (BOLD) signal recorded with functional MRI (fMRI)), is known to be strongly shaped by the underlying structural connectivity. However, the relationship between anatomical and functional brain connectivity is not trivial and computational models of large-scale neural dynamics are unique tools to explore this relationship [1-3]. Importantly, models can be used to predict the effects of structural alterations on the large-scale brain dynamics [4,5], which is beyond reach on the experimental side. In this work, the structural connectomes from XX patients with schizophrenia and from XX age- and gender-matched controls were built from DTI data using advanced tractography algorithms to detect the white matter tracts between 90 brain areas. In the model, each brain area was represented by a pool of spiking neurons, and its activity was described by a dynamic mean field model. Each brain area -or node in the global network- receives excitatory input from structurally connected regions in proportion to the number of fibre tracts detected, which may vary from subject to subject. The large-scale spontaneous activity, simulated with the model using the different structural connectomes, was compared between patients and controls. We have found that, in schizophrenia, the coupling weights are weaker, which shifts the bifurcation point (above which the dynamics becomes unstable) to a higher global coupling weight. In addition, the simulated mean field activity was transformed into BOLD signal, and the properties of the simulated FCs were analyzed using measures from graph theory. Our results indicate that the subtle randomization of functional networks occurring in schizophrenia is related to alterations in the underlying structural connectivity, which shift the dynamical regime of the brain at rest further away from the bifurcation point, which may have an impact on the behavioural symptoms of schizophrenia.

Disrupted structural connectivity in Pediatric Bipolar Disorder

Background: Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Methods: The network properties of whole-brain structural connectomes, constructed using probabilistic tractography and diffusion tensor imaging (DTI), were compared between groups of un-medicated PBD patients with psychosis euthymic and matched healthy controls. Specific network measures were correlated with neurocognitive and psychotic scores, thus providing a comprehensive characterization of topological brain changes underlying PBD. Results: Widespread changes in the structural connectivity of PBD patients were found in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, and hippocampus. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular structure and increased network asymmetries compared to healthy participants. Moreover, patients’ IQ and psychotic symptoms significantly correlated with the local efficiency of the orbitofrontal cortex. Conclusion: The results show that PBD is associated with significant changes in structural network topology, which may indicate a reduced capacity for balanced whole-brain integration of information. Localized network changes involve brain regions associated with emotional processing and regulation, as well as cognitive processing, some of which correlate with cognitive and clinical measures. These findings suggest that structural brain connectivity changes may contribute to the deficits in emotion processing and regulation found in PBD.

Neural Plasticity in Human Brain Connectivity: The Effects of Deep Brain Stimulation

Abstract Neural plasticity in adult humans is no longer believed to be impossible. The adult brain shows neuronal regeneration and plasticity in a number of domains. We know that certain disorders or accidents can change the brain in a malicious way. However, in more recent years we have come to learn that formation of new neurons also occurs in adults and that, for example, learning tasks can affect the structure of the brain and reorganize the brain network. The best example of this happens on the microscale with task repetition leading to strengthened neural connections. This mechanism is often referred to as Hebbian learning although other mechanisms could also be at play. Recent studies have shown that these changes in the brain can occur on a macroscale following deep brain stimulation (DBS). Following constant DBS (analogous to repetition in learning), some connections between the brain areas are strengthened resulting in long-term structural changes in the brain on the macroscale.Neural plasticity in adult humans is no longer believed to be impossible. The adult brain shows neuronal regeneration and plasticity in a number of domains. We know that certain disorders or accidents can change the brain in a malicious way. However, in more recent years we have come to learn that formation of new neurons also occurs in adults and that, for example, learning tasks can affect the structure of the brain and reorganize the brain network. The best example of this happens on the microscale with task repetition leading to strengthened neural connections. This mechanism is often referred to as Hebbian learning although other mechanisms could also be at play. Recent studies have shown that these changes in the brain can occur on a macroscale following deep brain stimulation (DBS). Following constant DBS (analogous to repetition in learning), some connections between the brain areas are strengthened resulting in long-term structural changes in the brain on the macroscale.