04323nas a2200877 4500008004100000022001400041245008800055210006900143260001200212300001200224490000800236520184100244653001202085653001802097653001002115653001402125653002202139653000902161653000902170653001102179653001102190653002002201653001102221653003102232653001702263653001002280100002002290700002502310700001902335700002202354700002002376700002302396700002202419700002202441700002302463700002602486700001602512700002202528700002202550700002102572700001702593700002002610700002002630700002202650700002202672700001902694700003002713700002302743700002202766700002502788700001302813700002102826700002702847700002302874700002002897700002302917700002002940700001902960700001902979700002302998700002103021700001703042700002103059700002203080700002303102700002303125700001803148700002203166700002103188700002403209700002303233700002403256700002003280700002503300856012003325 2023 eng d a1476-468700aA somato-cognitive action network alternates with effector regions in motor cortex.0 asomatocognitive action network alternates with effector regions  c05/2023 a351-3590 v6173 a

Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations, despite evidence for concentric functional zones and maps of complex actions. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action and physiological control, arousal, errors and pain. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions and connectivity to internal organs such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.

10aAnimals10aBrain Mapping10aChild10aCognition10aDatasets as Topic10aFoot10aHand10aHumans10aInfant10aInfant, Newborn10aMacaca10aMagnetic Resonance Imaging10aMotor Cortex10aMouth1 aGordon, Evan, M1 aChauvin, Roselyne, J1 aVan, Andrew, N1 aRajesh, Aishwarya1 aNielsen, Ashley1 aNewbold, Dillan, J1 aLynch, Charles, J1 aSeider, Nicole, A1 aKrimmel, Samuel, R1 aScheidter, Kristen, M1 aMonk, Julia1 aMiller, Ryland, L1 aMetoki, Athanasia1 aMontez, David, F1 aZheng, Annie1 aElbau, Immanuel1 aMadison, Thomas1 aNishino, Tomoyuki1 aMyers, Michael, J1 aKaplan, Sydney1 aD'Andrea, Carolina, Badke1 aDemeter, Damion, V1 aFeigelis, Matthew1 aRamirez, Julian, S B1 aXu, Ting1 aBarch, Deanna, M1 aSmyser, Christopher, D1 aRogers, Cynthia, E1 aZimmermann, Jan1 aBotteron, Kelly, N1 aPruett, John, R1 aWillie, Jon, T1 aBrunner, Peter1 aShimony, Joshua, S1 aKay, Benjamin, P1 aMarek, Scott1 aNorris, Scott, A1 aGratton, Caterina1 aSylvester, Chad, M1 aPower, Jonathan, D1 aListon, Conor1 aGreene, Deanna, J1 aRoland, Jarod, L1 aPetersen, Steven, E1 aRaichle, Marcus, E1 aLaumann, Timothy, O1 aFair, Damien, A1 aDosenbach, Nico, U F uhttps://www.neurotechcenter.org/publications/2023/somato-cognitive-action-network-alternates-effector-regions-motor02457nas a2200349 4500008004100000022001400041245012600055210006900181260001200250300001200262490000700274520141700281653001501698653001001713653001801723653002601741653002101767653001301788653002401801653000901825653001101834653001801845653001901863653003101882653002301913653004101936100002001977700002301997700002002020700001902040856004802059 2009 eng d a1879-278200aMapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.0 aMapping broadband electrocorticographic recordings to twodimensi c11/2009 a1257-700 v223 a

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

10aAlgorithms10aBrain10aBrain Mapping10aElectrodes, Implanted10aElectrodiagnosis10aEpilepsy10aFeasibility Studies10aHand10aHumans10aLinear Models10aMotor Activity10aNeural Networks (Computer)10aNonlinear Dynamics10aSignal Processing, Computer-Assisted1 aGunduz, Aysegul1 aSanchez, Justin, C1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1964798105892nas a2200361 4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482 2008 eng d a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals  c01/2008 a63-810 v1673 a

Electrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.

10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1758250704121nas a2200457 4500008004100000022001400041245010800055210006900163260001200232300001100244490000700255520290000262653001503162653001003177653002103187653001203208653001803220653001003238653002403248653002703272653001103299653000903310653001103319653000903330653001603339653001703355653001303372653001203385653002203397653002803419653001103447653002803458653001303486100002303499700002603522700001903548700001603567700001303583700001903596856004803615 2008 eng d a1524-462800aUnique cortical physiology associated with ipsilateral hand movements and neuroprosthetic implications.0 aUnique cortical physiology associated with ipsilateral hand move c12/2008 a3351-90 v393 a

BACKGROUND AND PURPOSE: 

Brain computer interfaces (BCIs) offer little direct benefit to patients with hemispheric stroke because current platforms rely on signals derived from the contralateral motor cortex (the same region injured by the stroke). For BCIs to assist hemiparetic patients, the implant must use unaffected cortex ipsilateral to the affected limb. This requires the identification of distinct electrophysiological features from the motor cortex associated with ipsilateral hand movements.

METHODS: 

In this study we studied 6 patients undergoing temporary placement of intracranial electrode arrays. Electrocorticographic (ECoG) signals were recorded while the subjects engaged in specific ipsilateral or contralateral hand motor tasks. Spectral changes were identified with regards to frequency, location, and timing.

RESULTS: 

Ipsilateral hand movements were associated with electrophysiological changes that occur in lower frequency spectra, at distinct anatomic locations, and earlier than changes associated with contralateral hand movements. In a subset of 3 patients, features specific to ipsilateral and contralateral hand movements were used to control a cursor on a screen in real time. In ipsilateral derived control this was optimal with lower frequency spectra.

CONCLUSIONS: 

There are distinctive cortical electrophysiological features associated with ipsilateral movements which can be used for device control. These findings have implications for patients with hemispheric stroke because they offer a potential methodology for which a single hemisphere can be used to enhance the function of a stroke induced hemiparesis.

10aAdolescent10aAdult10aArtificial Limbs10aBionics10aBrain Mapping10aChild10aDominance, Cerebral10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aMovement10aParesis10aProsthesis Design10aPsychomotor Performance10aStroke10aUser-Computer Interface10aVolition1 aWisneski, Kimberly1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aSmyth, Matt1 aMoran, D1 aLeuthardt, E C uhttp://www.ncbi.nlm.nih.gov/pubmed/1892745604391nas a2200409 4500008004100000022001400041245010700055210006900162260001200231300002900243490000700272520323000279653001003509653002203519653001803541653002503559653002603584653002703610653001103637653000903648653001103657653000903668653001603677653001703693653001703710653004103727653001103768100001903779700002003798700002603818700001903844700002003863700002003883700001303903700001703916856004803933 2007 eng d a1524-404000aElectrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex.0 aElectrocorticographic Frequency Alteration Mapping A Clinical Te c04/2007 a260-70; discussion 270-10 v603 a

OBJECTIVE: 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS: 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS: 

The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

10aAdult10aBiological Clocks10aBrain Mapping10aElectric Stimulation10aElectrodes, Implanted10aElectroencephalography10aFemale10aHand10aHumans10aMale10aMiddle Aged10aMotor Cortex10aOscillometry10aSignal Processing, Computer-Assisted10aTongue1 aLeuthardt, E C1 aMiller, John, W1 aAnderson, Nicholas, R1 aSchalk, Gerwin1 aDowling, Joshua1 aMiller, John, W1 aMoran, D1 aOjemann, J G uhttp://www.ncbi.nlm.nih.gov/pubmed/1741516203540nas a2200457 4500008004100000022001400041245004900055210004100104260001200145300001100157490000700168520234400175653001002519653001502529653001402544653001002558653002702568653002702595653002102622653001302643653001102656653000902667653000902676653001902685653001102704653003102715653002702746653000902773653001302782653003302795653004102828653002802869100002302897700001902920700002102939700002002960700001802980700002102998700001503019856004803034 2007 eng d a1053-811900aAn MEG-based brain-computer interface (BCI).0 aMEGbased braincomputer interface BCI c07/2007 a581-930 v363 a

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography(EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

10aAdult10aAlgorithms10aArtifacts10aBrain10aElectroencephalography10aElectromagnetic Fields10aElectromyography10aFeedback10aFemale10aFoot10aHand10aHead Movements10aHumans10aMagnetic Resonance Imaging10aMagnetoencephalography10aMale10aMovement10aPrincipal Component Analysis10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aMellinger, Jürgen1 aSchalk, Gerwin1 aBraun, Christoph1 aPreissl, Hubert1 aRosenstiel, W1 aBirbaumer, Niels1 aKübler, A uhttp://www.ncbi.nlm.nih.gov/pubmed/17475511