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

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 treatmentsIn this work, Gustavo Deco is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P and by the the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). Morten Kringelbach is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Victor M Saenger is supported by the Research Personnel Training program PSI2013-42091-P funded by the Spanish Ministry of Economy and Competitiveness.info:eu-repo/semantics/publishedVersio

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 brain's 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.This work was supported by (R.G.B.) the FI-DGR scholarship of the Catalan Government through the Age`ncia de Gestio d’Ajuts Universitari i de Recerca, under Agreement No. 2013FI-B1-00099, (G.Z.L.) the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 720270 (HBP SGA1), (G.D.) the European Research Council Advanced Grant: DYSTRUCTURE (295129) and the Spanish Research Project No. PSI2013- 42091-P, (Z.K.) European Community’s Seventh Framework Programme [FP7/2007-2013] under agreement PITN-GA- 2011-290011, (V.M.K.) European Community’s Seventh Framework Programme [FP7/2007-2013] under Agreement No. PITN-GA-2012-316746 and (M.L.K.) by the European Research Council Consolidator Grant No. CAREGIVING (615539)

Single or multi-frequency generators in on-going brain activity: a mechanistic whole-brain model of empirical MEG data

During rest, envelopes of band-limited on-going MEG 1 signals co-vary across the brain in consistent patterns, which have been related to resting-state networks measured with fMRI. To investigate the genesis of such envelope correlations, we consider a whole-brain network model assuming two distinct fundamental scenarios: one where each brain area generates oscillations in a single frequency, and a novel one where each brain area can generate oscillations in multiple frequency bands. The models share, as a common generator of damped oscillations, the normal form of a supercritical Hopf bifurcation operating at the critical border between the steady state and the oscillatory regime. The envelopes of thesimulated signals are compared with empirical MEG data using new methods to analyse the envelope dynamics in terms of their phase coherence and stability across the spectrum of carrier frequencies. Considering the whole-brain model with a single frequency generator in each brain area, we obtain the best fit with the empirical MEG data when the fundamental frequency is tuned at 12Hz. However, when multiple frequency generators are placed at each local brain area, we obtain an improved fit of the spatio-temporal structure of on-going MEG data across all frequency bands. Our results indicate that the brain is likely to operate on multiple frequency channels during rest, introducing a novel dimension for future models of large-scale brain activity.</p

Evidence from a rare case study for Hebbian-like changes in structural connectivity induced by long-term deep brain stimulation

It is unclear whether Hebbian-like learning occurs at the level of long-range white matter connections in humans, i.e., where measurable changes in structural connectivity (SC) are correlated with changes in functional connectivity. However, the behavioral changes observed after deep brain stimulation (DBS) suggest the existence of such Hebbian-like mechanisms occurring at the structural level with functional consequences. In this rare case study, we obtained the full network of white matter connections of one patient with Parkinson's disease (PD) before and after long-term DBS and combined it with a computational model of ongoing activity to investigate the effects of DBS-induced long-term structural changes. The results show that the long-term effects of DBS on resting-state functional connectivity is best obtained in the computational model by changing the structural weights from the subthalamic nucleus (STN) to the putamen and the thalamus in a Hebbian-like manner. Moreover, long-term DBS also significantly changed the SC towards normality in terms of model-based measures of segregation and integration of information processing, two key concepts of brain organization. This novel approach using computational models to model the effects of Hebbian-like changes in SC allowed us to causally identify the possible underlying neural mechanisms of long-term DBS using rare case study data. In time, this could help predict the efficacy of individual DBS targeting and identify novel DBS targets

Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder

Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined "static" functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted-MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re-emerging FC states during rest in 51 antidepressant-free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas-important for cognitive control-with default mode network, striatum, and salience areas, involved in emotional and self-referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal-striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted-MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.NORTE‐01‐0145‐FEDER‐000023, by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Dr. G.D. is supported by the Spanish Research Project PSI2016‐75688‐P (AEI/FEDER) and by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). Dr. M.L.K. is supported by the ERC Consolidator Grant: CAREGIVING (no. 615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Dr. H.G.R. is supported by a NWO/ZonMW VENI‐Grant #016.126.059. Dr. PE acknowledges support from the EPSRC award EP/N014529/1 funding the EPSRC Centre for Mathematics of Precision Healthcare at Imperia