Normative pathways in the functional connectome.

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.UK Medical Research Council (grant: G0802226) National Institute for Health Research (NIHR) (grant: 06-05-01) Alzheimer’s Research UK (ARUK- SRF2017B-1) Gates Cambridge Scholarshi

Application of Deep Learning to Brain Connectivity Classification in Large MRI Datasets

The use of machine learning for whole-brain classification of magnetic resonance imaging (MRI) data is of clear interest, both for understanding phenotypic differences in brain structure and function and for diagnostic applications. Developments of deep learning models in the past decade have revolutionized photographic image and speech recognition, bringing promise to do the same to other fields of science. However, there are many practical and theoretical challenges in the translation of such methods to the unique context of MRIs of the brain. This thesis presents a theoretical underpinning for whole-brain classification of extremely large datasets of multi-site MRIs, including machine learning model architecture, dataset curation methods, machine learning visualization methods, encoding of MRI data, and feature extraction. To replicate large sample sizes typically applied to deep learning models, a dataset of over 50,000 functional and structural MRIs was amassed from nine different databases, and the undertaken analyses were conducted on three covariates commonly found across these collections: sex, resting state/task, and autism spectrum disorder. I find that deep learning is not only a method that has promise for clinical application in the future, but also a powerful statistical tool for analyzing complex, nonlinear relationships in brain data where conventional statistics may fail. However, results are also dependent on factors such as dataset imbalances, confounding factors such as motion and head size, selected methods of encoding MRI data, variability of machine learning models and selected methods of visualizing the machine learning results. In this thesis, I present the following methodological innovations: (1) a method of balancing datasets as a means of regressing out measurable confounding factors; (2) a means of removing spatial biases from deep learning visualization methods; (3) methods of encoding functional and structural datasets as connectivity matrices; (4) the use of ensemble models and convolutional neural network architectures to improve classification accuracy and consistency; (5) adaptation of deep learning visualization methods to study brain connections utilized in the classification process. Additionally, I discuss interpretations, limitations, and future directions of this research.Gates Cambridge Scholarshi

Comparison of patient-specific and normative connectivity profiles in deep brain stimulation

Objective: Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and estimated the clinical improvement that they may generate. Methods: Data from 33 patients suffering from Parkinson’s disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely, either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to estimate the clinical improvement in out-of-sample data. Results: All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multicenter cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) or a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made, and the underlying optimal connectivity profiles were highly similar. Conclusion: Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Nevertheless, although the results were not significantly different, they hint at the fact that patient-specific connectivity has potential for estimating slightly more variance when compared to group connectomes. Furthermore, the use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets such as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.Zielsetzung: Konnektivitätsprofile des Gehirns, die von Elektroden zur Tiefenhirnstimulation (THS) ausgehen, haben sich als informativ für die Schätzung von Variabilität im Behandlungserfolg bei THS-PatientInnen erwiesen. Angesichts von Einschränkungen bei der Erhebung und Verarbeitung patientenspezifischer, diffusionsgewichteter Bilddaten wurden in einer Reihe von Studien normative Atlanten des menschlichen Konnektivitätsprofils verwendet. Bis heute ist unklar, ob patientenspezifische Konnektivitätsinformation die Genauigkeit solcher Analysen verbessern würde. Ziel dieser Studie war der Vergleich zwischen Ähnlichkeiten und Unterschieden patientenspezifischer, krankheits-gematchter und normativer, struktureller Konnektivitätsdaten, sowie der Fähigkeit dieser Methoden zur Vorhersage eines etwaigen klinischen Behandlungserfolges. Methoden: Die Analysen basierten auf retrospektiven Daten von 33 Parkinson-PatientInnen, welche an drei verschiedenen Zentren operiert worden waren. Stimulationsabhängige Konnektivitätsprofile mit Ursprung in aktiven DBS-Kontakten wurden mittels der drei Modalitäten geschätzt, also entweder basierend auf patientenspezifischen, diffusionsgewichteten MRT-Daten, oder auf krankheits-gematchten sowie auf normativen Gruppenkonnektivitätsdaten (erhoben an gesunden, jungen ProbandInnen). Auf Grundlage dieser Profile wurden Modelle optimaler Konnektivität konstruiert und zur Schätzung des klinischen Behandlungserfolgs in unabhängigen Daten herangezogen. Ergebnisse: Alle drei Modalitäten führten zu sehr ähnlichen optimalen Konnektivitätsprofilen, mit Hilfe derer sich auf Grundlage einer neuartigen multizentrischen Kohorte vorherige Forschungsbefunde weitgehend reproduzieren ließen. In einem datengesteuerten Ansatz, bei dem optimale Konnektivitätsprofile über das gesamte Gehirn hinweg geschätzt wurden, wurden Vorhersagen über den klinischen Behandlungserfolg in unabhängigen Daten berechnet. Unter Verwendung entweder der patientenspezifischen Konnektivität (R = 0,43 bei p = 0,001), eines alters- und krankheits-gematchten Gruppenkonnektivitätsprofils (R = 0,25, p = 0,048) oder eines normativen Konnektivitätsprofils basierend auf Daten gesunder/junger ProbandInnen (R = 0,31 bei p = 0,028) konnten signifikante Vorhersagen getroffen werden, wobei die zugrunde liegenden optimalen Konnektivitätsprofile große Ähnlichkeit aufwiesen. Schlussfolgerung: Unsere Ergebnisse, welche patientenspezifische sowie normative Konnektivitätsprofile einbeziehen, führen zu ähnlichen Hauptschlussfolgerungen darüber, welche Hirnareale mit klinischem Behandlungserfolg assoziiert sind. Obwohl sich die Ergebnisse nicht signifikant unterschieden, deuten sie dennoch darauf hin, dass patientenspezifische Konnektivität über Potenzial zur Schätzung geringfügig höherer Varianz im Vergleich zu gruppenbasierten Konnektivitäsprofilen verfügt. Darüber hinaus stützen sich Analysen, welche auf normativen Konnektivitätsprofile basieren, auf Datensätze mit hohem Signal-Rausch-Verhältnis, welche durch spezialisierte MRT-Technologie erfasst wurden, während klinische Datensätze, wie sie auch in dieser Studie herangezogen wurden, diesen an Qualität möglicherweise nicht gleichkommen. Unsere Befunde stützen die Rolle von Konnektivitätsprofilen, welche von THS-Elektroden ausgehen, als eine vielversprechende Methode zur Untersuchung von THS-Effekten und möglicherweise zur Verbesserung der THS-Programmierung

Normative vs. patient-specific brain connectivity in deep brain stimulation

Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and estimation of clinical improvement that they may generate. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely either patient-specific diffusion-MRI data, disease-matched or normative group connectome data (acquired in healthy young subjects). Based on these profiles, models of optimal connectivity were constructed and used to estimate the clinical improvement in out of sample data. All three modalities resulted in highly similar optimal connectivity profiles that could largely reproduce findings from prior research based on a novel multi-center cohort. In a data-driven approach that estimated optimal whole-brain connectivity profiles, out-of-sample predictions of clinical improvements were calculated. Using either patient-specific connectivity (R = 0.43 at p = 0.001), an age- and disease-matched group connectome (R = 0.25, p = 0.048) and a normative connectome based on healthy/young subjects (R = 0.31 at p = 0.028), significant predictions could be made and underlying optimal connectivity profiles were highly similar. Our results of patient-specific connectivity and normative connectomes lead to similar main conclusions about which brain areas are associated with clinical improvement. Still, although results were not significantly different, they hint at the fact that patient-specific connectivity may bear the potential of estimating slightly more variance when compared to group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient\u27s lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions

Connectomes as constitutively epistemic objects: critical perspectives on modeling in current neuroanatomy

in a nervous system of a given species. This chapter provides a critical perspective on the role of connectomes in neuroscientific practice and asks how the connectomic approach fits into a larger context in which network thinking permeates technology, infrastructure, social life, and the economy. In the first part of this chapter, we argue that, seen from the perspective of ongoing research, the notion of connectomes as “complete descriptions” is misguided. Our argument combines Rachel Ankeny’s analysis of neuroanatomical wiring diagrams as “descriptive models” with Hans-Joerg Rheinberger’s notion of “epistemic objects,” i.e., targets of research that are still partially unknown. Combining these aspects we conclude that connectomes are constitutively epistemic objects: there just is no way to turn them into permanent and complete technical standards because the possibilities to map connection properties under different modeling assumptions are potentially inexhaustible. In the second part of the chapter, we use this understanding of connectomes as constitutively epistemic objects in order to critically assess the historical and political dimensions of current neuroscientific research. We argue that connectomics shows how the notion of the “brain as a network” has become the dominant metaphor of contemporary brain research. We further point out that this metaphor shares (potentially problematic) affinities to the form of contemporary “network societies.” We close by pointing out how the relation between connectomes and networks in society could be used in a more fruitful manner

Changes in structural network topology correlate with severity of hallucinatory behavior in Parkinson's disease

Inefficient integration between bottom-up visual input and higher order visual processing regions is implicated in visual hallucinations in Parkinson's disease (PD). Here, we investigated white matter contributions to this perceptual imbalance hypothesis. Twenty-nine PD patients were assessed for hallucinatory behavior. Hallucination severity was correlated to connectivity strength of the network using the network-based statistic approach. The results showed that hallucination severity was associated with reduced connectivity within a subnetwork that included the majority of the diverse club. This network showed overall greater between-module scores compared with nodes not associated with hallucination severity. Reduced between-module connectivity in the lateral occipital cortex, insula, and pars orbitalis and decreased within-module connectivity in the prefrontal, somatosensory, and primary visual cortices were associated with hallucination severity. Conversely, hallucination severity was associated with increased between- and within-module connectivity in the orbitofrontal and temporal cortex, as well as regions comprising the dorsal attentional and default mode network. These results suggest that hallucination severity is associated with marked alterations in structural network topology with changes in participation along the perceptual hierarchy. This may result in the inefficient transfer of information that gives rise to hallucinations in PD. Author SummaryInefficient integration of information between external stimuli and internal perceptual predictions may lead to misperceptions or visual hallucinations in Parkinson's disease (PD). In this study, we show that hallucinatory behavior in PD patients is associated with marked alterations in structural network topology. Severity of hallucinatory behavior was associated with decreased connectivity in a large subnetwork that included the majority of the diverse club, nodes with a high number of between-module connections. Furthermore, changes in between-module connectivity were found across brain regions involved in visual processing, top-down prediction centers, and endogenous attention, including the occipital, orbitofrontal, and posterior cingulate cortex. Together, these findings suggest that impaired integration across different sides across different perceptual processing regions may result in inefficient transfer of information

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson's disease patient's thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients

Addressing Brain Circuit Dysfunction Underlying DBS-Induced Parkinsonism

Deep brain stimulation (DBS) of the globus pallidus internus (GPi) is an effective treatment for dystonia, a medical condition characterized by involuntary muscle contractions and postures. However, GPi-DBS may lead to stimulation-related side effects, such as parkinsonism. This dissertation aims to clarify the neural circuits underlying this effect, providing data to inform its optimal management while contributing to the understanding of parkinsonism as a symptom in other movement disorders. This work involved the collection of clinical and imaging data from 32 patients with dystonia undergoing GPi-DBS, 6 (18.75%) of whom developed parkinsonism. Electrode locations, reconstructed from neuroimaging, were used to create patientspecific volumes of tissue activated (VTAs), according to stimulation parameters. A voxelby- voxel comparison revealed a cluster of voxels that were significantly more present in VTAs from patients with stimulation-induced parkinsonism than those from patients without this side effect. Also, patients that developed parkinsonism had higherVTAinside the GPi (P=0.005). Furthermore, significant differenceswere foundin analyses of functional connectivity, obtained through a resting-state normative cohort. Connectivity between the DBS site and the whole brain showed that parkinsonic VTAs had preferential connectivity to regions related to cognition, while the others exhibited stronger connectivity to motorrelated areas. Moreover, functional connectivity to specific regions implicated in previous parkinsonism studies, such as the striatum, claustrum and precentral gyrus, revealed significant differences. Patients with parkinsonism had reduced connectivity between the VTA and these regions, in comparison to those without the side effect. Such differences were not observed for control areas and connectivity valueswere able to accurately predict the outcome of parkinsonism after stimulation reprogramming. These results suggest that connectivity-based tools could help further understand the pathophysiology of parkinsonism, guide the selection of DBS parameters, and help uncover new targets for other neuromodulation techniques.A estimulação cerebral profunda (ECP) no globus pallidus interno (GPi) constitui um tratamento eficaz para a distonia, um condição caracterizada por contrações musculares involuntárias. No entanto, esta terapia pode causar reações adversas debilitantes, tais como parkinsonismo. Esta dissertação visa clarificar os circuitos neuronais subjacentes a este evento adverso, reunir informações para melhorar a sua gestão e contribuir para o entendimento de parkinsonismo como um sintoma prevalente noutras patologias. Para tal, foram explorados dados clínicos e imagens médicas de 32 pacientes com distonia submetidos a GPi-ECP, dos quais 6 (18.75%) desenvolveram parkinsonismo. A localização dos elétrodos, reconstruídos através das neuroimagens, foi usada para simular o volume de tecido ativado (VTA) em cada doente, de acordo com os respetivos parâmetros de estimulação. Os VTAs dos doentes que desenvolveram parkinsonismo exibiam um conjunto de voxéis significativamente diferente dos VTAs de doentes sem o sintoma, e apresentavam uma maior abrangência de estimulação no GPi (P=0.005). Além disso, diferenças significativas foram encontradas em análises de conectividade funcional, obtidas através de um conectoma normativo. A conectividade entre os alvos da ECP e o restante cérebro revelou que os VTAs dos doentes com parkinsonismo estavam mais associados a regiões cognitivas, enquanto que os dos doentes sem parkinsonismo exibiam mais conectividade com áreas motoras. Adicionalmente, a conectividade funcional entre os VTAs e regiões descritas em estudos prévios sobre parkinsonismo, como o corpo estriado, claustro e giro pré-central, encontrava-se reduzida nos doentes com parkinsonismo, comparativamente aos doentes sem o sintoma. Não foram observadas diferenças em regiões de controlo e estes valores de conectividade foram capazes de prever a melhoria de parkinsonismo após reajustes na estimulação. Estes resultados sugerem que abordagens baseadas em conectividade podem ajudar a compreender a fisiopatologia do parkinsonismo, guiar a seleção de parâmetros da ECP e descobrir novos alvos terapêuticos para outras técnicas de neuroestimulação