Brain dynamics predictive of response to psilocybin for treatment-resistant depression.

Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here, we leveraged the differential outcome in responders and non-responders to psilocybin (10 and 25 mg, 7 days apart) therapy for depression-to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used large-scale brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a healthy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-hydroxytryptamine 2a and 5-hydroxytryptamine 1a, which psilocin, the active metabolite of psilocybin, has an appreciable affinity for, and where it acts as a full-to-partial agonist. Serotonergic transmission has long been associated with depression, and our findings provide causal mechanistic evidence for the role of brain regions in the recovery from depression via psilocybin.

Modulation of limbic resting-state networks by subthalamic nucleus deep brain stimulation.

Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson's disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific modulation induced by STN-DBS using Leading Eigenvector Dynamics Analysis (LEiDA). We calculated the occupancy of resting-state networks (RSNs) in functional MRI data from 10 patients with Parkinson's disease implanted with STN-DBS and statistically compared between ON and OFF conditions. STN-DBS was found to specifically modulate the occupancy of networks overlapping with limbic RSNs. STN-DBS significantly increased the occupancy of an orbitofrontal limbic subsystem with respect to both DBS OFF (p = 0.0057) and 49 age-matched healthy controls (p = 0.0033). Occupancy of a diffuse limbic RSN was increased with STN-DBS OFF when compared with healthy controls (p = 0.021), but not when STN-DBS was ON, which indicates rebalancing of this network. These results highlight the modulatory effect of STN-DBS on components of the limbic system, particularly within the orbitofrontal cortex, a structure associated with reward processing. These results reinforce the value of quantitative biomarkers of RSN activity in evaluating the disseminated impact of brain stimulation techniques and the personalization of therapeutic strategies.

Dynamic sensitivity analysis: Defining personalised strategies to drive brain state transitions via whole brain modelling.

Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.

On screen experiment showed that becoming a parent for the first time shifted people's priorities from themselves to their infant at 1 year of age.

AIM: This study used a screen-based perceptual matching task to see how non-parents, people trying to get pregnant, and those who had given birth prioritised shapes and labels relating to self or infant conditions. METHODS: The study took place at Aarhus University Hospital in Denmark from December 2016 to November 2021. Recruitment methods included family planning clinics, social media, online recruitment systems and local bulletin boards. The modified perceptual matching task linked five shapes to five labels, including self and infant. RESULTS: We found that 67 males and females with a mean age of 24.4 ± 3 years, who had no plans to become parents in the near future, reacted faster and more accurately to self-shapes and labels (p < 0.001), which validated the experiment. The 56 participants aged 27.1 ± 4.4 years who were actively trying to become parents showed no statistically significant prioritisation. A subset of 21 participants aged 28.7 ± 4.4 years showed faster response times to infant than self-shapes and labels 1 year after giving birth (p < 0.001). CONCLUSION: Healthy first-time parents showed faster reactions to infant than self-conditions 1 year after giving birth, in contrast to the other two groups.

A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort.

Connectivity-based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes-the foundations of brain network fingerprinting-by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole-brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort-MGH-HCP. State-of-the-art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra-hemispheric, increase of inter-hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.

The Shape of the Olfactory Bulb Predicts Olfactory Function.

The olfactory bulb (OB) plays a key role in the processing of olfactory information. A large body of research has shown that OB volumes correlate with olfactory function, which provides diagnostic and prognostic information in olfactory dysfunction. Still, the potential value of the OB shape remains unclear. Based on our clinical experience we hypothesized that the shape of the OB predicts olfactory function, and that it is linked to olfactory loss, age, and gender. The aim of this study was to produce a classification of OB shape in the human brain, scalable to clinical and research applications. Results from patients with the five most frequent causes of olfactory dysfunction (n = 192) as well as age/gender-matched healthy controls (n = 77) were included. Olfactory function was examined in great detail using the extended "Sniffin' Sticks" test. A high-resolution structural T2-weighted MRI scan was obtained for all. The planimetric contours (surface in mm2) of OB were delineated manually, and then all surfaces were added and multiplied to obtain the OB volume in mm3. OB shapes were outlined manually and characterized on a selected slice through the posterior coronal plane tangential to the eyeballs. We looked at OB shapes in terms of convexity and defined two patterns/seven categories based on OB contours: convex (olive, circle, and plano-convex) and non-convex (banana, irregular, plane, and scattered). Categorization of OB shapes is possible with a substantial inter-rater agreement (Cohen's Kappa = 0.73). Our results suggested that non-convex OB patterns were significantly more often observed in patients than in controls. OB shapes were correlated with olfactory function in the whole group, independent of age, gender, and OB volume. OB shapes seemed to change with age in healthy subjects. Importantly, the results indicated that OB shapes were associated with certain causes of olfactory disorders, i.e., an irregular OB shape was significantly more often observed in post-traumatic olfactory loss. Our study provides evidence that the shape of the OB can be used as a biomarker for olfactory dysfunction.

Automatic Segmentation of the Olfactory Bulb.

The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer's disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject's smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater's segmentation and 5.92 ± 0.81 for the second rater's manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3-5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.

Validation of Olfactory Network Based on Brain Structural Connectivity and Its Association With Olfactory Test Scores.

Olfactory perception is a complicated process involving multiple cortical and subcortical regions, of which the underlying brain dynamics are still not adequately mapped. Even in the definition of the olfactory primary cortex, there is a large degree of variation in parcellation templates used for investigating olfaction in neuroimaging studies. This complicates comparison between human olfactory neuroimaging studies. The present study aims to validate an olfactory parcellation template derived from both functional and anatomical data that applies structural connectivity (SC) to ensure robust connectivity to key secondary olfactory regions. Furthermore, exploratory analyses investigate if different olfactory parameters are associated with differences in the strength of connectivity of this structural olfactory fingerprint. By combining diffusion data with an anatomical atlas and advanced probabilistic tractography, we found that the olfactory parcellation had a robust SC network to key secondary olfactory regions. Furthermore, the study indicates that higher ratings of olfactory significance were associated with increased intra- and inter-hemispheric SC of the primary olfactory cortex. Taken together, these results suggest that the patterns of SC between the primary olfactory cortex and key secondary olfactory regions has potential to be used for investigating the nature of olfactory significance, hence strengthening the theory that individual differences in olfactory behaviour are encoded in the structural network fingerprint of the olfactory cortex.

Habitual coffee drinkers display a distinct pattern of brain functional connectivity.

Coffee is the most widely consumed source of caffeine worldwide, partly due to the psychoactive effects of this methylxanthine. Interestingly, the effects of its chronic consumption on the brain's intrinsic functional networks are still largely unknown. This study provides the first extended characterization of the effects of chronic coffee consumption on human brain networks. Subjects were recruited and divided into two groups: habitual coffee drinkers (CD) and non-coffee drinkers (NCD). Resting-state functional magnetic resonance imaging (fMRI) was acquired in these volunteers who were also assessed regarding stress, anxiety, and depression scores. In the neuroimaging evaluation, the CD group showed decreased functional connectivity in the somatosensory and limbic networks during resting state as assessed with independent component analysis. The CD group also showed decreased functional connectivity in a network comprising subcortical and posterior brain regions associated with somatosensory, motor, and emotional processing as assessed with network-based statistics; moreover, CD displayed longer lifetime of a functional network involving subcortical regions, the visual network and the cerebellum. Importantly, all these differences were dependent on the frequency of caffeine consumption, and were reproduced after NCD drank coffee. CD showed higher stress levels than NCD, and although no other group effects were observed in this psychological assessment, increased frequency of caffeine consumption was also associated with increased anxiety in males. In conclusion, higher consumption of coffee and caffeinated products has an impact in brain functional connectivity at rest with implications in emotionality, alertness, and readiness to action.

Reorganization of brain structural networks in aging: A longitudinal study.

Normal aging is characterized by structural and functional changes in the brain contributing to cognitive decline. Structural connectivity (SC) describes the anatomical backbone linking distinct functional subunits of the brain and disruption of this communication is thought to be one of the potential contributors for the age-related deterioration observed in cognition. Several studies already explored brain network's reorganization during aging, but most focused on average connectivity of the whole-brain or in specific networks, such as the resting-state networks. Here, we aimed to characterize longitudinal changes of white matter (WM) structural brain networks, through the identification of sub-networks with significantly altered connectivity along time. Then, we tested associations between longitudinal changes in network connectivity and cognition. We also assessed longitudinal changes in topological properties of the networks. For this, older adults were evaluated at two timepoints, with a mean interval time of 52.8 months (SD = 7.24). WM structural networks were derived from diffusion magnetic resonance imaging, and cognitive status from neurocognitive testing. Our results show age-related changes in brain SC, characterized by both decreases and increases in connectivity weight. Interestingly, decreases occur in intra-hemispheric connections formed mainly by association fibers, while increases occur mostly in inter-hemispheric connections and involve association, commissural, and projection fibers, supporting the last-in-first-out hypothesis. Regarding topology, two hubs were lost, alongside with a decrease in connector-hub inter-modular connectivity, reflecting reduced integration. Simultaneously, there was an increase in the number of provincial hubs, suggesting increased segregation. Overall, these results confirm that aging triggers a reorganization of the brain structural network.

Signatures of white-matter microstructure degradation during aging and its association with cognitive status.

Previous studies have shown an association between cognitive decline and white matter integrity in aging. This led to the formulation of a "disconnection hypothesis" in the aging-brain, which states that the disruption in cortical network communication may explain the cognitive decline during aging. Although some longitudinal studies have already investigated the changes occurring in white matter microstructure, most focused on specific white matter tracts. Our study aims to characterize the longitudinal whole-brain signatures of white matter microstructural change during aging. Furthermore, we assessed the relationship between distinct longitudinal alterations in white matter integrity and cognition. White matter microstructural properties were estimated from diffusion magnetic resonance imaging, and cognitive status characterized from extensive neurocognitive testing. The same individuals were evaluated at two timepoints, with a mean interval time of 52.8 months (SD = 7.24) between first and last assessment. Our results show that age is associated with a decline in cognitive performance and a degradation in white matter integrity. Additionally, significant associations were found between diffusion measures and different cognitive dimensions (memory, executive function and general cognition). Overall, these results suggest that age-related cognitive decline is related to white matter alterations, and thus give support to the "disconnected hypothesis" of the aging brain.

Chemosensory Sensitivity after Coffee Consumption Is Not Static: Short-Term Effects on Gustatory and Olfactory Sensitivity.

Chemosensory sensitivity has great variation between individuals. This variation complicates the chemosensory diagnostics, as well as the creation of a meal with universally high hedonic value. To ensure accurate characterization of chemosensory function, a common rule of thumb is to avoid food/beverages one hour before chemosensory testing. However, the scientific foundation of this time of fast remains unclear. Furthermore, the role of coffee on immediate chemosensitivity is not known and may have implications for optimization of gastronomy and hedonia. The aim of this study is to investigate the modularity effects of coffee consumption on immediate gustatory and olfactory sensitivity. We included 155 participants. By applying tests for olfactory and gustatory sensitivity before and after coffee intake, we found no changes in olfactory sensitivity, but significantly altered sensitivity for some basic tastants. We repeated our experimental paradigm using decaffeinated coffee and found similar results. Our results demonstrate that coffee (regular and decaffeinated) alters the subsequent perception of taste, specifically by increasing the sensitivity to sweet and decreasing the sensitivity to bitter. Our findings provide the first evidence of how coffee impacts short-term taste sensitivity and consequently the way we sense and perceive food following coffee intake-an important insight in the context of gastronomy, as well as in chemosensory testing procedures.

Breakdown of Whole-brain Dynamics in Preterm-born Children.

The brain operates at a critical point that is balanced between order and disorder. Even during rest, unstable periods of random behavior are interspersed with stable periods of balanced activity patterns that support optimal information processing. Being born preterm may cause deviations from this normal pattern of development. We compared 33 extremely preterm (EPT) children born at < 27 weeks of gestation and 28 full-term controls. Two approaches were adopted in both groups, when they were 10 years of age, using structural and functional brain magnetic resonance imaging data. The first was using a novel intrinsic ignition analysis to study the ability of the areas of the brain to propagate neural activity. The second was a whole-brain Hopf model, to define the level of stability, desynchronization, or criticality of the brain. EPT-born children exhibited fewer intrinsic ignition events than controls; nodes were related to less sophisticated aspects of cognitive control, and there was a different hierarchy pattern in the propagation of information and suboptimal synchronicity and criticality. The largest differences were found in brain nodes belonging to the rich-club architecture. These results provide important insights into the neural substrates underlying brain reorganization and neurodevelopmental impairments related to prematurity.

Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis.

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. Here we compare the network properties of whole-brain structural connectomes of euthymic PBD patients with psychosis, a variant of PBD, and matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients with psychosis in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed, revealing trends of association between patients' IQ and affective psychotic symptoms with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD with psychosis is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which show trends of association with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

Re-Test Reliability of Gustatory Testing and Introduction of the Sensitive Taste-Drop-Test.

The sense of taste holds a key integrate role in assessing the flavor of food before swallowing is initiated. If the expectations for taste are not met, palatability and pleasure of the food can decrease. In patients suffering from taste disorders, this may impair appetite and nutritional state. Testing gustatory function can be important for diagnostics and assessment of treatment effects. However, the gustatory tests applied are required to be both sensitive and reliable. In this study, we investigate the re-test validity of popular Taste Strips gustatory test for gustatory screening. Furthermore, we introduce a new sensitive Taste-Drop-Test, which was found to be superior for detecting a more accurate measure of tastant sensitivity.

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.

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

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.

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 brain's 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 brain's 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.

Tractography Study of Deep Brain Stimulation of the Anterior Cingulate Cortex in Chronic Pain: Key to Improve the Targeting.

BACKGROUND: Deep brain stimulation (DBS) of the anterior cingulate cortex (ACC) is a new treatment for alleviating intractable neuropathic pain. However, it fails to help some patients. The large size of the ACC and the intersubject variability make it difficult to determine the optimal site to position DBS electrodes. The aim of this work was therefore to compare the ACC connectivity of patients with successful versus unsuccessful DBS outcomes to help guide future electrode placement. METHODS: Diffusion magnetic resonance imaging (dMRI) and probabilistic tractography were performed preoperatively in 8 chronic pain patients (age 53.4 ± 6.1 years, 2 females) with ACC DBS, of whom 6 had successful (SO) and 2 unsuccessful outcomes (UOs) during a period of trialing. RESULTS: The number of patients was too small to demonstrate any statistically significant differences. Nevertheless, we observed differences between patients with successful and unsuccessful outcomes in the fiber tract projections emanating from the volume of activated tissue around the electrodes. A strong connectivity to the precuneus area seems to predict unsuccessful outcomes in our patients (UO: 160n/SO: 27n), with (n), the number of streamlines per nonzero voxel. On the other hand, connectivity to the thalamus and brainstem through the medial forebrain bundle (MFB) was only observed in SO patients. CONCLUSIONS: These findings could help improve presurgical planning by optimizing electrode placement, to selectively target the tracts that help to relieve patients' pain and to avoid those leading to unwanted effects.

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.