@article {4459, title = {A somato-cognitive action network alternates with effector regions in motor cortex.}, journal = {Nature}, volume = {617}, year = {2023}, month = {05/2023}, pages = {351-359}, abstract = {

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.

}, keywords = {Animals, Brain Mapping, Child, Cognition, Datasets as Topic, Foot, Hand, Humans, Infant, Infant, Newborn, Macaca, Magnetic Resonance Imaging, Motor Cortex, Mouth}, issn = {1476-4687}, doi = {10.1038/s41586-023-05964-2}, author = {Gordon, Evan M and Chauvin, Roselyne J and Van, Andrew N and Rajesh, Aishwarya and Nielsen, Ashley and Newbold, Dillan J and Lynch, Charles J and Seider, Nicole A and Krimmel, Samuel R and Scheidter, Kristen M and Monk, Julia and Miller, Ryland L and Metoki, Athanasia and Montez, David F and Zheng, Annie and Elbau, Immanuel and Madison, Thomas and Nishino, Tomoyuki and Myers, Michael J and Kaplan, Sydney and Badke D{\textquoteright}Andrea, Carolina and Demeter, Damion V and Feigelis, Matthew and Ramirez, Julian S B and Xu, Ting and Barch, Deanna M and Smyser, Christopher D and Rogers, Cynthia E and Zimmermann, Jan and Botteron, Kelly N and Pruett, John R and Willie, Jon T and Brunner, Peter and Shimony, Joshua S and Kay, Benjamin P and Marek, Scott and Norris, Scott A and Gratton, Caterina and Sylvester, Chad M and Power, Jonathan D and Liston, Conor and Greene, Deanna J and Roland, Jarod L and Petersen, Steven E and Raichle, Marcus E and Laumann, Timothy O and Fair, Damien A and Dosenbach, Nico U F} } @article {2181, title = {An MEG-based brain-computer interface (BCI).}, journal = {Neuroimage}, volume = {36}, year = {2007}, month = {07/2007}, pages = {581-93}, abstract = {

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.

}, keywords = {Adult, Algorithms, Artifacts, Brain, Electroencephalography, Electromagnetic Fields, Electromyography, Feedback, Female, Foot, Hand, Head Movements, Humans, Magnetic Resonance Imaging, Magnetoencephalography, Male, Movement, Principal Component Analysis, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1053-8119}, doi = {10.1016/j.neuroimage.2007.03.019}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17475511}, author = {Mellinger, J{\"u}rgen and Gerwin Schalk and Christoph Braun and Preissl, Hubert and Rosenstiel, W. and Niels Birbaumer and K{\"u}bler, A.} }