Scherberger, Hansjörg

[["dc.bibliographiccitation.artnumber","e15719"],["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Schaffelhofer, Stefan"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2016-10-20T12:02:12Z"],["dc.date.accessioned","2021-10-27T13:12:53Z"],["dc.date.available","2016-10-20T12:02:12Z"],["dc.date.available","2021-10-27T13:12:53Z"],["dc.date.issued","2016-08-15"],["dc.description.abstract","The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks."],["dc.identifier.doi","10.7554/eLife.15719"],["dc.identifier.fs","622743"],["dc.identifier.gro","3151420"],["dc.identifier.pmid","27525488"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13789"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91731"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.euproject","NEBIAS"],["dc.relation.issn","2050-084X"],["dc.relation.orgunit","Deutsches Primatenzentrum"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","17985"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Intveld, Rijk W."],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2019-07-09T11:51:08Z"],["dc.date.available","2019-07-09T11:51:08Z"],["dc.date.issued","2018"],["dc.description.abstract","Considerable progress has been made over the last decades in characterizing the neural coding of hand shape, but grasp force has been largely ignored. We trained two macaque monkeys (Macaca mulatta) on a delayed grasping task where grip type and grip force were instructed. Neural population activity was recorded from areas relevant for grasp planning and execution: the anterior intraparietal area (AIP), F5 of the ventral premotor cortex, and the hand area of the primary motor cortex (M1). Grasp force was strongly encoded by neural populations of all three areas, thereby demonstrating for the first time the coding of grasp force in single- and multi-units of AIP. Neural coding of intended grasp force was most strongly represented in area F5. In addition to tuning analysis, a dimensionality reduction method revealed low-dimensional responses to grip type and grip force. Additionally, this method revealed a high correlation between latent variables of the neural population representing grasp force and the corresponding latent variables of electromyographic forearm muscle activity. Our results therefore suggest an important role of the cortical areas AIP, F5, and M1 in coding grasp force during movement execution as well as of F5 for coding intended grasp force."],["dc.identifier.doi","10.1038/s41598-018-35488-z"],["dc.identifier.pmid","30573765"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16056"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59882"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","570"],["dc.title","Neural coding of intended and executed grasp force in macaque areas AIP, F5, and M1"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","056016"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Menz, Veera Katharina"],["dc.contributor.author","Schaffelhofer, Stefan"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2017-09-07T11:53:53Z"],["dc.date.available","2017-09-07T11:53:53Z"],["dc.date.issued","2015"],["dc.description.abstract","Objective. In the last decade, multiple brain areas have been investigated with respect to their decoding capability of continuous arm or hand movements. So far, these studies have mainly focused on motor or premotor areas like M1 and F5. However, there is accumulating evidence that anterior intraparietal area (AIP) in the parietal cortex also contains information about continuous movement. Approach. In this study, we decoded 27 degrees of freedom representing complete hand and arm kinematics during a delayed grasping task from simultaneously recorded activity in areas M1, F5, and AIP of two macaque monkeys (Macaca mulatta). Main results. We found that all three areas provided decoding performances that lay significantly above chance. In particular, M1 yielded highest decoding accuracy followed by F5 and AIP. Furthermore, we provide support for the notion that AIP does not only code categorical visual features of objects to be grasped, but also contains a substantial amount of temporal kinematic information. Significance. This fact could be utilized in future developments of neural interfaces restoring hand and arm movements."],["dc.identifier.doi","10.1088/1741-2560/12/5/056016"],["dc.identifier.gro","3151408"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12572"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8206"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1741-2560"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Representation of continuous hand and arm movements in macaque areas M1, F5, and AIP: a comparative decoding study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0142679"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.lastpage","19"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Lehmann, Sebastian J."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2017-09-07T11:53:54Z"],["dc.date.available","2017-09-07T11:53:54Z"],["dc.date.issued","2015"],["dc.description.abstract","The execution of reach-to-grasp movements in order to interact with our environment is an important subset of the human movement repertoire. To coordinate such goal-directed movements, information about the relative spatial position of target and effector (in this case the hand) has to be continuously integrated and processed. Recently, we reported the existence of spatial representations in spiking-activity of the cortical fronto-parietal grasp network (Lehmann & Scherberger 2013), and in particular in the anterior intraparietal cortex (AIP). To further investigate the nature of these spatial representations, we explored in two rhesus monkeys (Macaca mulatta) how different frequency bands of the local field potential (LFP) in AIP are modulated by grip type, target position, and gaze position, during the planning and execution of reach-to-grasp movements. We systematically varied grasp type, spatial target, and gaze position and found that both spatial and grasp information were encoded in a variety of frequency bands (1–13Hz, 13–30Hz, 30–60Hz, and 60–100Hz, respectively). Whereas the representation of grasp type strongly increased towards and during movement execution, spatial information was represented throughout the task. Both spatial and grasp type representations could be readily decoded from all frequency bands. The fact that grasp type and spatial (reach) information was found not only in spiking activity, but also in various LFP frequency bands of AIP, might significantly contribute to the development of LFP-based neural interfaces for the control of upper limb prostheses."],["dc.identifier.doi","10.1371/journal.pone.0142679"],["dc.identifier.gro","3151416"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12555"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8215"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Spatial Representations in Local Field Potential Activity of Primate Anterior Intraparietal Cortex (AIP)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2020-12-10T18:10:09Z"],["dc.date.available","2020-12-10T18:10:09Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1038/s41598-018-20051-7"],["dc.identifier.eissn","2045-2322"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15415"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73862"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Population coding of grasp and laterality-related information in the macaque fronto-parietal network"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1005175"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLOS Computational Biology"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Michaels, Jonathan A."],["dc.contributor.author","Dann, Benjamin"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2017-09-07T11:54:32Z"],["dc.date.available","2017-09-07T11:54:32Z"],["dc.date.issued","2016"],["dc.description.abstract","Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity."],["dc.identifier.doi","10.1371/journal.pcbi.1005175"],["dc.identifier.gro","3151431"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13940"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8233"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","210"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","IEEE Transactions on Neural Systems and Rehabilitation Engineering"],["dc.bibliographiccitation.lastpage","220"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Schaffelhofer, S."],["dc.contributor.author","Sartori, M."],["dc.contributor.author","Scherberger, H."],["dc.contributor.author","Farina, D."],["dc.date.accessioned","2015-06-15T12:57:15Z"],["dc.date.accessioned","2021-10-27T13:21:01Z"],["dc.date.available","2015-06-15T12:57:15Z"],["dc.date.available","2021-10-27T13:21:01Z"],["dc.date.issued","2015-03-01"],["dc.description.abstract","Reach-to-grasp tasks have become popular paradigms for exploring the neural origin of hand and arm movements. This is typically investigated by correlating limb kinematic with electrophysiological signals from intracortical recordings. However, it has never been investigated whether reach and grasp movements could be well expressed in the muscle domain and whether this could bring improvements with respect to current joint domain-based task representations. In this study, we trained two macaque monkeys to grasp 50 different objects, which resulted in a high variability of hand configurations. A generic musculoskeletal model of the human upper extremity was scaled and morphed to match the specific anatomy of each individual animal. The primate-specific model was used to perform 3-D reach-to-grasp simulations driven by experimental upper limb kinematics derived from electromagnetic sensors. Simulations enabled extracting joint angles from 27 degrees of freedom and the instantaneous length of 50 musculotendon units. Results demonstrated both a more compact representation and a higher decoding capacity of grasping tasks when movements were expressed in the muscle kinematics domain than when expressed in the joint kinematics domain. Accessing musculoskeletal variables might improve our understanding of cortical hand-grasping areas coding, with implications in the development of prosthetics hands."],["dc.identifier.doi","10.1109/tnsre.2014.2364776"],["dc.identifier.gro","3151429"],["dc.identifier.pmid","25350935"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11890"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91988"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.euproject","DEMOVE"],["dc.relation.issn","1558-0210"],["dc.relation.issn","1534-4320"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Musculoskeletal representation of a large repertoire of hand grasping actions in primates."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Wildlife Biology"],["dc.bibliographiccitation.volume","2019"],["dc.contributor.author","Buil, Jeroen M. M."],["dc.contributor.author","Peckre, Louise R."],["dc.contributor.author","Dörge, Matthias"],["dc.contributor.author","Fichtel, Claudia"],["dc.contributor.author","Kappeler, Peter M."],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2020-12-10T18:43:54Z"],["dc.date.available","2020-12-10T18:43:54Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.2981/wlb.00581"],["dc.identifier.issn","0909-6396"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17239"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78262"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Remotely releasable collar mechanism for medium-sized mammals: an affordable technology to avoid multiple captures"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e15278"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.lastpage","24"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Schaffelhofer, Stefan"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2016-09-19T06:12:43Z"],["dc.date.accessioned","2021-10-27T13:11:25Z"],["dc.date.available","2016-09-19T06:12:43Z"],["dc.date.available","2021-10-27T13:11:25Z"],["dc.date.issued","2016"],["dc.description.abstract","Grasping requires translating object geometries into appropriate hand shapes. How the brain computes these transformations is currently unclear. We investigated three key areas of the macaque cortical grasping circuit with microelectrode arrays and found cooperative but anatomically separated visual and motor processes. The parietal area AIP operated primarily in a visual mode. Its neuronal population revealed a specialization for shape processing, even for abstract geometries, and processed object features ultimately important for grasping. Premotor area F5 acted as a hub that shared the visual coding of AIP only temporarily and switched to highly dominant motor signals towards movement planning and execution. We visualize these non-discrete premotor signals that drive the primary motor cortex M1 to reflect the movement of the grasping hand. Our results reveal visual and motor features encoded in the grasping circuit and their communication to achieve transformation for grasping."],["dc.identifier.doi","10.7554/eLife.15278"],["dc.identifier.fs","622742"],["dc.identifier.gro","3151436"],["dc.identifier.pmid","27458796"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13678"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91594"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.euproject","NEBIAS"],["dc.relation.issn","2050-084X"],["dc.relation.orgunit","Fakultät für Biologie und Psychologie"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","hand grasping; motor cortex; neuroscience; parallel recording; parietal cortex; premotor cortex; rhesus macaque; sensorimotor transformation"],["dc.title","Object vision to hand action in macaque parietal, premotor, and motor cortices"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","026025"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Schaffelhofer, Stefan"],["dc.contributor.author","Scherberger, Hansjörg"],["dc.date.accessioned","2017-09-07T11:53:56Z"],["dc.date.available","2017-09-07T11:53:56Z"],["dc.date.issued","2012"],["dc.description.abstract","The investigation of grasping movements in cortical motor areas depends heavily on the measurement of hand kinematics. Currently used methods for small primates need either a large number of sensors or provide insufficient accuracy. Here, we present both a novel glove based on electromagnetic tracking sensors that can operate at a rate of 100 Hz and a new modeling method that allows to monitor 27 degrees of freedom (DOF) of the hand and arm using only seven sensors. A rhesus macaque was trained to wear the glove while performing precision and power grips during a delayed grasping task in the dark without noticeable hindrance. During five recording sessions all 27 joint angles and their positions could be tracked reliably. Furthermore, the field generator did not interfere with electrophysiological recordings below 1 kHz and did not affect single-cell separation. Measurements with the glove proved to be accurate during static and dynamic testing (mean absolute error below 2° and 3°, respectively). This makes the glove a suitable solution for characterizing electrophysiological signals with respect to hand grasping and in particular for brain–machine interface applications."],["dc.identifier.doi","10.1088/1741-2560/9/2/026025"],["dc.identifier.gro","3151428"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8402"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8228"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1741-2560"],["dc.rights","CC BY-NC-SA 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-sa/3.0"],["dc.title","A new method of accurate hand- and arm-tracking for small primates"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]