Biology subjects

Horn, A.

Publications and source records attributed to Horn, A..

35 records · Page 2Linked to original sources

bioRxiv2024

Cortical beta oscillations map to shared brain networks modulated by dopamine

Brain rhythms can facilitate neural communication for the maintenance of brain function. Beta rhythms (13-35 Hz) have been proposed to serve multiple domains of human ability, including motor control, cognition, memory and ewmotion, but the overarching organisational principles remain unknown. To uncover the circuit architecture of beta oscillations, we leverage normative brain data, analysing over 30 hours of invasive brain signals from 1772 channels from cortical areas in epilepsy patients, to demonstrate that beta is the most distributed cortical brain rhythm. Next, we identify a shared brain network from beta dominant areas with deeper brain structures, like the basal ganglia, by mapping parametrised oscillatory peaks to whole-brain functional and structural MRI connectomes. Finally, we show that these networks share significant overlap with dopamine uptake as indicated by positron emission tomography. Our study suggests that beta oscillations emerge in cortico-subcortical brain networks that are modulated by dopamine. It provides the foundation for a unifying circuit-based conceptualisation of the functional role of beta activity beyond the motor domain and may inspire an extended investigation of beta activity as a feedback signal for closed-loop neurotherapies for dopaminergic disorders.

neuroscience↗

bioRxiv2023

Human hippocampal ripples tune cortical responses in uncertain visual contexts

To be able to encode information efficiently, our perceptual system should detect when situations are unpredictable (i.e., informative), and modulate brain dynamics to prepare for encoding. Here we show, with direct recordings from the human hippocampus and visual cortex, that after exposure to unpredictable visual stimulus streams, hippocampal ripple activity increases in frequency and duration prior to stimulus presentation, indicating context and experience-dependent prediction of predictability. Pre-stimulus hippocampal ripples suppress changes in visual (occipital) cortex gamma activity associated with uncertainty, and modulate post-stimulus prediction error gamma responses in higher-level visual (fusiform) cortex to surprising (i.e., unpredicted) stimuli. These results link hippocampal ripples with predictive coding accounts of neuronal message passing--and precision-weighted prediction errors--revealing a mechanism relevant for perceptual synthesis and subsequent memory encoding.

neuroscience↗

bioRxiv2023

Liquid-liquid interface microchannels with tunable mechanical properties mimic tissue deformations during neutrophil interstitial migration in vivo

Leukocytes navigate through interstitial spaces resulting in deformation of both the motile leukocytes and surrounding cells. Creating an in vitro system that models the deformable cellular environment encountered in vivo has been challenging. Here, we engineer microchannels with a liquid-liquid interface that exerts confining pressures (200-3000 Pa) similar to cells in tissues, and, thus, is deformable by cell generated forces. Consequently, the balance between migratory cell-generated and interfacial pressures determines the degree of confinement. Pioneer cells that first contact the interfacial barrier require greater deformation forces to forge a path for migration, and as a result migrate slower than trailing cells. Critically, resistive pressures are tunable by controlling the curvature of the liquid interface, which regulates motility. By granting cells autonomy in determining their confinement, and tuning environmental resistance, interfacial deformations are made to match those of surrounding cells in vivo during interstitial neutrophil migration in a larval zebrafish model. We discover that, in this context, neutrophils employ a bleb-based mechanism of force generation to deform a barrier exerting cell-scale confining pressures. Significance StatementImmune cells sense physical forces provided by surrounding cellular tissues to regulate their motility. Here, we introduce the use of liquid-liquid interfaces to model forces exerted by surrounding cells during interstitial motility in vivo. Neutrophils interacting with the interface employ a bleb-based mechanism of force generation to induce interfacial deformation. This work furthers our understanding of the mechanisms employed by immune cells to traverse through deformable barriers akin to cells in the body, and introduces a pioneering technology enabling the study of cell interaction with soft materials.

bioengineering↗

bioRxiv2023

Keratinocyte dynamics modulate the spatial organization of redox signaling during sensory neuron regeneration

Epithelial damage leads to early reactive oxygen species (ROS) signaling, which regulates sensory neuron regeneration and tissue repair. How the initial type of tissue injury influences early damage signaling and regenerative growth of sensory axons remains unclear. Previously we reported that thermal injury triggers distinct early tissue responses in larval zebrafish. Here, we found that thermal but not mechanical injury impairs sensory axon regeneration and function. Real-time imaging revealed an immediate tissue response to thermal injury characterized by the rapid Arp2/3-dependent migration of keratinocytes, which was associated with tissue-scale ROS production and sustained sensory axon damage. Isotonic treatment was sufficient to limit keratinocyte movement, spatially restrict ROS production and rescue sensory neuron function. These results suggest that early keratinocyte dynamics regulate the spatial and temporal pattern of long-term signaling in the wound microenvironment during tissue repair.

cell biology↗

bioRxiv2022

A Structural Connectivity Atlas of Limbic Brainstem Nuclei

BackgroundUnderstanding the structural connectivity of key brainstem nuclei with limbic cortical regions is essential to the development of therapeutic neuromodulation for depression, chronic pain, addiction, anxiety and movement disorders. Several brainstem nuclei have been identified as the primary central nervous system (CNS) source of important monoaminergic ascending fibers including the noradrenergic locus coeruleus, serotonergic dorsal raphe nucleus, and dopaminergic ventral tegmental area. However, due to practical challenges to their study, there is limited data regarding their in vivo anatomic connectivity in humans. ObjectiveTo evaluate the structural connectivity of the following brainstem nuclei with limbic cortical areas: locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius. Additionally, to develop a group average atlas of these limbic brainstem structures to facilitate future analyses. MethodsEach nucleus was manually masked from 197 Human Connectome Project (HCP) structural MRI images using FSL software. Probabilistic tractography was performed using FSLs FMRIB Diffusion Toolbox. Connectivity with limbic cortical regions was calculated and compared between brainstem nuclei. Results were aggregated to produce a freely available MNI structural atlas of limbic brainstem structures. ResultsA general trend was observed for a high probability of connectivity to the amygdala, hippocampus and DLPFC with relatively lower connectivity to the orbitofrontal cortex, nucleus accumbens, hippocampus and insula. The locus coeruleus and nucleus tractus solitarius demonstrated significantly greater connectivity to the DLPFC than amygdala while the periaqueductal grey, dorsal raphe nucleus, and ventral tegmental area did not demonstrate a significant difference between these two structures. ConclusionMonoaminergic and other modulatory nuclei in the brainstem project widely to cortical limbic regions. We describe the structural connectivity across the several key brainstem nuclei theorized to influence emotion, reward, and cognitive functions. An increased understanding of the anatomic basis of the brainstems role in emotion and other reward-related processing will support targeted neuromodulatary therapies aimed at alleviating the symptoms of neuropsychiatric disorders. HighlightsO_LIThe brainstem plays a key role in the processing of emotional stimuli and is intricately linked with the limbic system. C_LIO_LIAnatomic data for these connections is limited in humans C_LIO_LIWe describe the structural connectivity of five brain stem nuclei (locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius) in relation to limbic circuits C_LIO_LIOur results present a comprehensive delineation of the brainstem-limbic structural connectivity of these nuclei and are compiled into a freely available tractographic atlas C_LIO_LIApplications include future targeting of these structures for neuropsychiatric conditions C_LI

neuroscience↗

bioRxiv2021

Molecular insights into RNA recognition and gene regulation by the TRIM-NHL protein Mei-P26

The TRIM-NHL protein Meiotic P26 (Mei-P26) acts as a regulator of cell fate in Drosophila. Its activity is critical for ovarian germline stem cell maintenance, differentiation of oocytes and spermatogenesis. Mei-P26 functions as a post-transcriptional regulator of gene expression, however, the molecular details of how its NHL domain selectively recognizes and regulates its mRNA targets have remained elusive. Here, we present the crystal structure of the Mei-P26 NHL domain at 1.6 [A] resolution and identify key amino acids that confer substrate specificity and distinguish Mei-P26 from closely related TRIM-NHL proteins. Furthermore, we identify mRNA targets of Mei-P26 in cultured Drosophila cells and show that Mei-P26 can act as either a repressor or activator of gene expression on different RNA targets. Our work reveals the molecular basis of RNA recognition by Mei-P26 and the fundamental functional differences between otherwise very similar TRIM-NHL proteins.

biochemistry↗

bioRxiv2021

A functional network target for tic reduction during thalamic stimulation for Tourette Syndrome

BackgroundDeep brain stimulation (DBS) of the medial thalamus is an evolving therapy for severe, treatment-refractory Tourette syndrome (TS). It remains unanswered which functionally connected networks need to be modulated to obtain optimal treatment results. MethodsWe assessed treatment response of 15 patients with TS untergoing thalamic DBS six and twelve months postoperatively using the Yale Global Tic Severity Scale (YGTSS) tic score. For each time point, functional connectivity maps seeding from stimulation sites were calculated based on a normative functional connectome derived from 1000 healthy subjects. Resulting maps were analyzed in a voxel-wise mixed model for repeated measurements to identify patterns of connectivity associated with tic reduction. ResultsConnectivity of stimulation to the medial frontal cortex, bilateral insulae and sensorimotor cortex was associated with tic reduction. Connectivity with the temporal lobe, cerebellum, ventral striatum and orbitofrontal cortex was negatively associated. The overall connectivity pattern was robust to leave-one-out cross-validation, explaining 25 % of outcome variance (R = 0.500; p = 0.005). ConclusionsWe delineated a functional connectivity profile seeding from stimulation sites associated with TS-DBS outcome. This pattern comprised areas linked to the processing of premonitory urges and tic execution, thereby extending our current understanding of effective neuromodulation for TS.

neuroscience↗

bioRxiv2021

Mapping the subcortical connectivity of the human default mode network

The default mode network (DMN) mediates self-awareness and introspection, core components of human consciousness. Therapies to restore consciousness in patients with severe brain injuries have historically targeted subcortical sites in the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia, with the goal of reactivating cortical DMN nodes. However, the subcortical connectivity of the DMN has not been fully mapped and optimal subcortical targets for therapeutic neuromodulation of consciousness have not been identified. In this work, we created a comprehensive map of DMN subcortical connectivity by combining high-resolution functional and structural datasets with advanced signal processing methods. We analyzed 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy volunteers acquired in the Human Connectome Project. The rs-fMRI blood-oxygen-level-dependent (BOLD) data were temporally synchronized across subjects using the BrainSync algorithm. Cortical and subcortical DMN nodes were jointly analyzed and identified at the group level by applying a novel Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method to the synchronized dataset. The subcortical connectivity map was then overlaid on a 7 Tesla 100 micron ex vivo MRI dataset for neuroanatomic analysis using automated segmentation of nuclei within the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia. We further compared the NASCAR subcortical connectivity map with its counterpart generated from canonical seed-based correlation analyses. The NASCAR method revealed that BOLD signal in the central lateral nucleus of the thalamus and ventral tegmental area of the midbrain is strongly correlated with that of the DMN. In an exploratory analysis, additional subcortical sites in the median and dorsal raphe, lateral hypothalamus, and caudate nuclei were correlated with the cortical DMN. We also found that the putamen and globus pallidus are negatively correlated (i.e., anti-correlated) with the DMN, providing rs-fMRI evidence for the mesocircuit hypothesis of human consciousness, whereby a striatopallidal feedback system modulates anterior forebrain function via disinhibition of the central thalamus. Seed-based analyses yielded similar subcortical DMN connectivity, but the NASCAR result showed stronger contrast and better spatial alignment with dopamine immunostaining data. The DMN subcortical connectivity map identified here advances understanding of the subcortical regions that contribute to human consciousness and can be used to inform the selection of therapeutic targets in clinical trials for patients with disorders of consciousness.

neuroscience↗

bioRxiv2021

Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinsons disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement kinematics. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS. Significance StatementNeurotechnology will revolutionize the treatment of neurological and psychiatric patients, promising novel treatment avenues for previously intractable brain disorders. However, optimal surgical and computational approaches and their interactions with neurological disorders are unknown. How can recent advances in machine learning and connectomics aid the precision and performance of invasive brain signal decoding strategies? Do the brain disorders treated with such approaches have impact on decoding performance? We propose a real time compatible advanced machine learning pipeline for invasively recorded brain signals in Parkinsons disease (PD) patients. We report optimal movement decoding strategies with respect to signal source, model architecture and connectomic fingerprint and demonstrate that PD pathophysiology significantly and negatively impacts movement decoding. Our study has broad impacts for the development of smart brain implants for the treatment of PD and other brain disorders.

neuroscience↗

bioRxiv2020

Neural signatures of pathological hyperdirect pathway activity in Parkinson's disease

Parkinsons disease (PD) is characterised by the emergence of pathological patterns of oscillatory synchronisation across the cortico-basal-ganglia circuit. The relationship between anatomical connectivity and oscillatory synchronisation within this system remains poorly understood. We address this by integrating evidence from invasive electrophysiology, magnetoencephalography, tractography and computational modelling in patients. Coupling between supplementary motor area (SMA) and subthalamic nucleus (STN) within the high beta frequency (21-30 Hz) range correlated with fibre tract densities between these two structures. Additionally within the STN, non-linear waveform features suggestive of cortical synchronisation correlated with cortico-STN fibre densities. Finally, computational modelling revealed that exaggerated hyperdirect cortical inputs to the STN in the upper beta frequency range can provoke the generation of widespread pathological synchrony at lower beta (13-20 Hz) frequencies. These observations reveal a spectral signature of the hyperdirect pathway at high beta frequencies and provide evidence for its pathophysiological role in oscillatory network dysfunction in PD. One sentence summarySignatures of the hyperdirect pathway and its likely role in pathological network disruption in Parkinsons disease are identified.

neuroscience↗

bioRxiv2020

Deep Brain Stimulation: Imaging on a group level

Deep Brain Stimulation (DBS) is an established treatment option for movement disorders and is investigated to treat a growing number of other brain disorders. It has been shown that DBS effects are highly dependent on exact electrode placement, which is especially important when probing novel indications or stereotactic targets. Thus, considering precise electrode placement is crucial when investigating efficacy of DBS targets. To measure clinical improvement as a function of electrode placement, neuroscientific methodology and specialized software tools are needed. Such tools should have the goal to make electrode placement comparable across patients and DBS centers, and include statistical analysis options to validate and define optimal targets. Moreover, to allow for comparability across different research sites, these need to be performed within an algorithmically and anatomically standardized and openly available group space. With the publication of Lead-DBS software in 2014, an open-source tool was introduced that allowed for precise electrode reconstructions based on pre- and postoperative neuroimaging data. Here, we introduce Lead Group, implemented within the Lead-DBS environment and specifically designed to meet aforementioned demands. In the present article, we showcase the various processing streams of Lead Group in a retrospective cohort of 51 patients suffering from Parkinsons disease, who were implanted with DBS electrodes to the subthalamic nucleus (STN). Specifically, we demonstrate various ways to visualize placement of all electrodes in the group and map clinical improvement values to subcortical space. We do so by using active coordinates and volumes of tissue activated, showing converging evidence of an optimal DBS target in the dorsolateral STN. Second, we relate DBS outcome to the impact of each electrode on local structures by measuring overlap of stimulation volumes with the STN. Finally, we explore the software functions for connectomic mapping, which may be used to relate DBS outcomes to connectivity estimates with remote brain areas. We isolate a specific fiber bundle - which structurally resembles the hyperdirect pathway - that is associated with good clinical outcome in the cohort. The manuscript is accompanied by a walkthrough tutorial through which users are able to reproduce all main results presented in the present manuscript. All data and code needed to reproduce results are openly available. HighlightsO_LIWe present a novel toolbox to carry out DBS imaging analyses on a group-level C_LIO_LIGroup electrodes are visualized in 2D and 3D and related to clinical regressors C_LIO_LIA favorable target and connectivity profiles for the treatment of PD are validated C_LI

neuroscience↗

bioRxiv2019

Left prefrontal connectivity links subthalamic stimulation with depressive symptoms in Parkinson’s disease

ObjectiveSubthalamic nucleus deep brain stimulation (STN-DBS) in Parkinsons Disease (PD) not only stimulates focal target structures but also affects distributed brain networks. The impact this network modulation has on non-motor DBS effects is not well characterized. By focusing on the affective domain, we systematically investigate the impact of electrode placement and associated structural connectivity on changes in depressive symptoms following STN-DBS which have been reported to improve, worsen or remain unchanged. MethodsDepressive symptoms before and after STN-DBS surgery were documented in 116 PD patients from three DBS centers (Berlin, Queensland, Cologne). Based on individual electrode reconstructions, the volumes of tissue activated (VTA) were estimated and combined with normative connectome data to identify structural connections passing through VTAs. Berlin and Queensland cohorts formed a training and cross-validation dataset used to identify structural connectivity explaining change in depressive symptoms. The Cologne data served as test-set for which depressive symptom change was predicted. ResultsStructural connectivity was linked to depressive symptom change under STN-DBS. An optimal connectivity map trained on the Berlin cohort could predict changes in depressive symptoms in Queensland patients and vice versa. Furthermore, the joint training-set map predicted changes in depressive symptoms in the independent test-set. Worsening of depressive symptoms was associated with left prefrontal connectivity. InterpretationFibers linking the STN electrode with left prefrontal areas predicted worsening of depressive symptoms. Our results suggest that for the left STN-DBS lead, placement impacting fibers to left prefrontal areas should be avoided to maximize improvement of depressive symptoms.

neuroscience↗

bioRxiv2019

Directional DBS Leads Show Large Deviations from their Intended Implantation Orientation

ObjectiveLead orientation is a new degree of freedom with directional deep brain stimulation (DBS) leads. We investigated how prevalent deviations from the intended implantation direction are in a large patient cohort.\n\nMethodsThe Directional Orientation Detection (DiODe) algorithm to determine lead orientation from postoperative CT scans was implemented into the open-source Lead-DBS toolbox. Lead orientation was analysed in 100 consecutive patients (198 leads). Different anatomical targets and intraoperative setups were compared.\n\nResultsDeviations of up to 90{degrees} from the intended implantation direction were observed. Deviations of more than 30{degrees} were seen in 42 % of the leads and deviations of more than 60{degrees} in about 11 % of the leads. Deviations were independent from the neuroanatomical target and the stereotactic frame but increased depending on which microdrive was used.\n\nDiscussionOur results indicate that large deviations from the intended implantation direction are a common phenomenon in directional leads. Postoperative determination of lead orientation is thus mandatory for investigating directional DBS.\n\nFull DisclosuresTill A. Dembek reports speaker honoraria from Medtronic and Boston Scientific.\n\nMauritius Hoevels has nothing to disclose.\n\nAlexandra Hellerbach has nothing to disclose.\n\nAndreas Horn reports speaker honoraria from Medtronic.\n\nJan Niklas Petry-Schmelzer reports travel grants from Boston Scientific.\n\nJan Borggrefe has nothing to disclose.\n\nJochen Wirths reports travel grants from Boston Scientific.\n\nHaidar Salimi-Dafsari reports honoraria from Medtronic and Boston Scientific.\n\nMichael T. Barbe reports grants from Medtronic and Boston Scientific.\n\nVeerle Visser-Vandewalle is a member of the advisory boards and reports consultancies for Medtronic, Boston Scientific and St Jude Medical. She received a grant from SAPIENS Steering Brain Stimulation. Harald Treuer has nothing to disclose.

neuroscience↗

bioRxiv2019

Toward a unified connectomic target for deep brain stimulation in obsessive-compulsive disorder

Multiple surgical targets have been proposed for treating obsessive-compulsive disorder (OCD) with deep brain stimulation (DBS). However, different targets may modulate the same neural network responsible for clinical improvement. Here we analyzed data from four cohorts of OCD patients (N = 50) that underwent DBS to the anterior limb of the internal capsule (ALIC), the nucleus accumbens (NAcc) or the subthalamic nucleus (STN). Fiber tracts that were predominantly connected to electrodes in good or poor DBS responders were isolated from a normative structural connectome and assigned a predictive value. Strikingly, the same fiber bundle was related to treatment response when independently analyzing two large training cohorts that targeted either ALIC or STN. This discriminative tract is a subsection of the ALIC and connects frontal regions (such as the dorsal anterior cingulate, dACC, and ventral prefrontal, vlPFC, cortices to the STN). When informing the tract solely based on one cohort (e.g. ALIC), clinical improvements in the other (e.g. STN) could be significantly predicted, and vice versa. Finally, clinical improvements of eight patients from a third center with electrodes in the NAcc and six patients from a fourth center in which electrodes had been implanted in both STN and ALIC were significantly predicted based on this novel tract-based DBS target. Results suggest a functional role of a limbic hyperdirect pathway that projects from dACC and vlPFC to anteriomedial STN. Obsessive-compulsive symptoms seem to be tractable by modulating the specific bundle isolated here. Our results show that connectivity-derived improvement models can inform clinical improvement across DBS targets, surgeons and centers. The identified tract is now three-dimensionally defined in stereotactic standard space and will be made openly available.

neuroscience↗

bioRxiv2019

Connectivity profile of thalamic deep brain stimulation to effectively treat essential tremor

Essential tremor is the most prevalent movement disorder and is often refractory to medical treatment. Deep brain stimulation offers a therapeutic approach that can efficiently control tremor symptoms. Several deep brain stimulation targets (ventral intermediate nucleus, zona incerta, posterior subthalamic area) have been discussed for tremor treatment. Effective deep brain stimulation therapy for tremor critically involves optimal targeting to modulate the tremor network. This could potentially become more robust and precise by using state-of-the-art brain connectivity measurements. In the current study, we utilized two normative brain connectomes (structural and functional) to show the pattern of effective deep brain stimulation electrode connectivity in 36 essential tremor patients. Our structural and functional connectivity models were significantly predictive of post-operative tremor improvement in out-of-sample data (p < 0.001 for both structural and functional leave-one-out cross-validation). Additionally, we segregated the somatotopic brain network based on head and hand tremor scores. These resulted in segregations that mapped onto the well-known somatotopic maps of both motor cortex and cerebellum. Crucially, this shows that slightly distinct networks need to be modulated to ameliorate head vs. hand tremor and that those networks could be identified based on somatotopic zones in motor cortex and cerebellum.\n\nFinally, we propose a multi-modal connectomic deep brain stimulation sweet spot that may serve as a reference to enhance clinical care, in the future. This spot resided in the posterior subthalamic area, encroaching on the inferior borders of ventral intermediate nucleus and sensory thalamus. Our results underscore the importance of integrating brain connectivity in optimizing deep brain stimulation targeting for essential tremor.

neuroscience↗

bioRxiv2019

Modulating the human functional connectome using deep brain stimulation

Neuroimaging has seen a paradigm shift from a formal description of local activity patterns toward studying distributed brain networks. The recently defined framework of the human connectome allows to globally analyse parts of the brain and their interconnections. Deep brain stimulation (DBS) is an invasive therapy for patients with severe movement disorders aiming to retune abnormal brain network activity by local high frequency stimulation of the basal ganglia. Beyond clinical utility, DBS represents a powerful research platform to study functional connectomics and the modulation of distributed brain networks in the human brain. We acquired resting-state functional MRI in twenty Parkinsons disease (PD) patients with subthalamic DBS switched ON and OFF. An age-matched control cohort of fifteen subjects was acquired from an open data repository. DBS lead placement in the subthalamic nucleus (STN) was localized using a state-of-the art pipeline that involved brain shift correction, multispectral image registration and use of a precise subcortical atlas. Based on a realistic 3D model of the electrode and surrounding anatomy, the amount of local impact of DBS was estimated using a finite element method approach. On a global level, average connectivity increases and decreases throughout the brain were estimated by contrasting ON and OFF DBS scans on a voxel-wise graph comprising eight thousand nodes. Local impact of DBS on the sensorimotor STN explained half the variance in global connectivity increases within the sensorimotor network (R = 0.711, p < 0.001). Moreover, local impact of DBS on the motor STN could explain the degree of how much voxel-wise average brain connectivity normalized toward healthy controls (R = 0.713, p < 0.001). Finally, a network based statistics analysis revealed that DBS attenuated specific couplings that are known to be pathological in PD. Namely, coupling between motor thalamus and sensorimotor cortex was increased and striatal coupling with cerebellum, external pallidum and STN was decreased by DBS. Our results show that rs-fMRI may be acquired in DBS ON and OFF conditions on clinical MRI hardware and that data is useful to gain additional insight into how DBS modulates the functional connectome of the human brain. We demonstrate that effective DBS increases overall connectivity in the motor network, normalizes the network profile toward healthy controls and specifically strengthens thalamo-cortical connectivity while reducing striatal control over basal ganglia and cerebellar structures.

neuroscience↗

bioRxiv2018

Lead-DBS v2: Toward a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patients preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice.\n\nThis work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

neuroscience↗