Mapping different parts of the brain and determining how they correspond to thoughts, actions, and other neural functions is a central area of inquiry in neuroscience, but while previous studies using fMRI scans and EEG have allowed researchers to rough out brain areas connected with different types of neural activities, they have not allowed for mapping the activity of individual neurons.
Now in a paper publishing March 26 in the journal Cell, investigators report that they have used microelectrode arrays implanted in the brains of two people to map out motor functions down to the level of the single nerve cell.
The study revealed that an area believed to control only one body part actually operates across a wide range of motor functions. It also demonstrated how different neurons coordinate with each other.
“This research shows for the first time that an area of the brain previously thought to be connected only to the arm and hand has information about the entire body,” says first author Frank Willett, a postdoctoral fellow in the Neural Prosthetics Translational Laboratory at Stanford University and the Howard Hughes Medical Institute.
“We also found that this area has a shared neural code that links all the body parts together.”
The study, a collaboration between neuroscientists at Stanford and Brown University, is part of BrainGate2, a multisite pilot clinical trial focused on developing and testing medical devices to restore communication and independence in people affected by neurological conditions like paralysis and locked-in syndrome.
A major focus of the Stanford team has been developing ways to restore the ability of these people to communicate through brain-computer interfaces (BCIs).
The new study involved two participants who have chronic tetraplegia–partial or total loss of function in all four limbs.
One of them has a high-level spinal cord injury and the other has amyotrophic lateral sclerosis. Both have electrodes implanted in the so-called hand knob area of the motor cortex of their brains.
This area–named in part for its knoblike shape–was previously thought to control movement in the hands and arms only.
The investigators used the electrodes to measure the action potentials in single neurons when the participants were asked to attempt to do certain tasks–for example, lifting a finger or turning an ankle.
The researchers looked at how the microarrays in the brain were activated. They were surprised to find that the hand knob area was activated not only by movements in the hand and arm, but also in the leg, face, and other parts of the body.
“Another thing we looked at in this study was matching movements of the arms and legs,” Willett says, “for example, moving your wrist up or moving your ankle up. We would have expected the resulting patterns of neural activity in motor cortex to be different, because they are a completely different set of muscles.
We actually found that they were much more similar than we would have expected.” These findings reveal an unexpected link between all four limbs in motor cortex that might help the brain to transfer skills learned with one limb to another one.
The researchers looked at how the microarrays in the brain were activated.
They were surprised to find that the hand knob area was activated not only by movements in the hand and arm, but also in the leg, face, and other parts of the body. domain.
Willett says that the new findings have important implications for the development of BCIs to help people who are paralyzed to move again.
“We used to think that to control different parts of the body, we would need to put implants in many areas spread out across the brain,” he notes. “It’s exciting, because now we can explore controlling movements throughout the whole body with an implant in only one area.”
One important potential application for BCIs is allowing people who are paralyzed or have locked-in syndrome to communicate by controlling a computer mouse or other device.
“It may be that we can connect different body movements to different types of computer clicks,” Willett says.
“We hope we can leverage these different signals more accurately to enable someone who can’t talk to use a computer, since neural signals from different body parts are easier for a BCI to tease apart than those from the arm or hand alone.”
Funding: This work was supported by the Office of Research and Development, Rehabilitation R and D Service, Department of Veterans Affairs, the Executive Committee on Research of Massachusetts General Hospital, NIDCD, NINDS, Larry and Pamela Garlick, Samuel and Betsy Reeves, the Wu Tsai Neuroscience Institute at Stanford, the Simons Foundation Collaboration on the Global Brain, the Office of Naval Research, and the Howard Hughes Medical Institute.
The caudal SMA proper is anatomically connected with brain regions of the motor execution network, including the primary motor cortex (M1), spinal cord, basal ganglia, and cerebellum, and involved in motor execution (Muakkassa and Strick, 1979; Dum and Strick, 1991, 2005; Galea and Darian-Smith, 1994; He et al., 1995; Maier et al., 2002).
The rostral preSMA mainly connects with the prefrontal cortex (PFC; Luppino et al., 1993; Lu et al., 1994; Wang et al., 2005) and involves higher-level processing, such as motor control and attention (Nakata et al., 2008; Boehler et al., 2010; Krüger et al., 2013; Cummine et al., 2017; Obeso et al., 2017).
The resting-state functional connectivity (rsFC) patterns of the SMA subregions have been investigated in healthy subjects (Zhang et al., 2011). Similar to the anatomical connection patterns, the SMA proper and preSMA, respectively, showed rsFC with the sensorimotor and prefrontal areas, although both subregions were connected to the insula.
The subcortical infarction frequently impairs the internal capsule, corona radiata, and basal ganglia, which have direct or indirect anatomical fibers with other cortical regions (Alexander et al., 1986; Dum and Strick, 1991).
The lesions interrupt the integrity of fibers that pass through them and subsequently affect the directly or indirectly connected distal cortical areas (Grefkes and Fink, 2014). Previous studies have explored that the focal subcortical lesions can trigger remote effects on the function of brain networks after stroke (Dancause, 2006; Grefkes et al., 2008; Dubovik et al., 2012; Grefkes and Fink, 2014), which may account for various behavioral deficits, such as motor deficit (Wang et al., 2010), aphasia (de Boissezon et al., 2005; Choi et al., 2007), spatial neglect (He et al., 2007), cognitive impairment (Stebbins et al., 2008; Gottesman and Hillis, 2010), and so on.
Motor deficit is the most common symptom in stroke, and motor recovery has been associated with functional reorganization of the motor network (Duncan et al., 2000). All previous studies on the reorganization of the SMA after stroke have treated the SMA as a whole and reported increased activation in the SMA (Tombari et al., 2004; Jaillard et al., 2005; Tang et al., 2015; Chen et al., 2018) and increased effective connectivity of the SMA in chronic stroke patients when they perform motor or motor imagery tasks (Mintzopoulos et al., 2009; Sharma et al., 2009; Rehme et al., 2011).
However, the rsFC alterations of the SMA after stroke have never been studied at the level of the subregion. Considering the different connectivity patterns and functions of the SMA subregions and the importance of the SMA in motor recovery after stroke, we hypothesized that the rsFCs of the SMA subregions may show different reorganization patterns in stroke patients.
Recently, the SMA has been consistently parcellated into the SMA proper and preSMA based on different MRI modalities (Johansen-Berg et al., 2004; Kim et al., 2010; Zhang et al., 2011). This parcellation framework is generally carried out using unsupervised clustering methods according to the similarity in either anatomical or functional connectivity profiles between voxels.
In contrast to the traditional parcellation methods that are mainly based on local information (such as gyrification landmark, cytoarchitectonics, and activations, etc.), the connectivity-based clustering methods can parcellate the brain areas into several subregions that have similar connectivity profiles within each subregion, while much different across them.
Thus, this strategy is more preferable for a connectivity-related study in contrast to traditional subregions based on local information (Fan et al., 2016). These methods have been applied to human and animal in vivo brain studies and demonstrate high reliability and accuracy (Wang et al., 2015, 2019; Schaefer et al., 2018).
In the present study, we automatically parcellated the SMA based on the functional connectivity profiles and hypothesized that the rsFC of the SMA proper and preSMA may demonstrate different reorganization patterns in chronic stroke.
This is the first study to investigate rsFC changes of the SMA after stroke at the level of the subregion. In stroke patients, the SMA proper showed increased rsFCs with brain regions of the motor execution network, whereas the preSMA showed increased rsFC with the motor control network.
These findings suggest that the two SMA subregions exhibit completely different functional reorganization patterns within the motor network. Additionally, we found that both the SMA proper and the preSMA showed decreased rsFC with brain areas involved in cognitive control, which may relate to the impaired cognitive function in stroke patients.
Consistent with previous studies (Johansen-Berg et al., 2004; Kim et al., 2010; Zhang et al., 2011), we have parcellated the SMA into anterior (preSMA) and posterior (SMA proper) clusters based on the rsFC patterns. The SMA proper functionally connected with brain areas that belonged to the motor execution network, which supports its function in motor execution (Maier et al., 2002; Krieghoff et al., 2009; Chouinard and Paus, 2010; Kim et al., 2010; Zhang et al., 2011).
The preSMA functionally connected with the frontal, parietal, and insular areas that are closely related to cognitive control, including the control of complex motor behaviors (Kim et al., 2010; Zhang et al., 2011). The significant rsFC between the SMA proper and the bilateral FICs suggests that the SMA proper is also involved in motor control.
The increased activation in the SMA was found to contribute to motor recovery after stroke (Chollet et al., 1991; Jaillard et al., 2005). This hypothesis is validated by the findings that the initially decreased effective connectivity of the SMA (Grefkes et al., 2008; Rehme et al., 2011) was finally increased at the chronic stage of stroke (Mintzopoulos et al., 2009; Rehme et al., 2011; Zhang et al., 2016; Chen et al., 2018).
The relationship between the rsFCs of the SMA and motor recovery in stroke patients is further validated in a longitudinal study. This study revealed that the rsFCs between the ipsilesional primary motor cortex and the contralesional SMA at onset was positively correlated with motor recovery at 6 months after stroke (Park et al., 2011).
Although previous studies have revealed the functional connectivity changes of the SMA after stroke (Grefkes et al., 2008; Mintzopoulos et al., 2009; Park et al., 2011; Rehme et al., 2011), there is no study that focused on the rsFC changes of the SMA at the level of the subregion.
In the present study, we revealed different patterns in increased rsFCs between the SMA subregions. The SMA proper showed increased rsFC with both the primary and secondary motor areas, whereas the preSMA only exhibited increased rsFC with the secondary motor areas. The discrepancy may be a reflection of the differences in the rsFC patterns and functions of these two SMA subregions.
As discussed above, the SMA subregions, especially the preSMA, are implicated in a motor control network that controls more complex motor behaviors. During the performance of a complex motor task, the sensory systems identify information about the performance (including errors and conflicts) and environment.
The information from different sensory modalities is converged to the FICs to direct attention to pertinent stimuli as changing conditions, internal or external homeostatic demands, and context (Seeley et al., 2007); and then the FICs (especially the right FIC) initiate the activity of the brain regions associated with cognitive control (Seeley et al., 2007).
These brain areas typically include the anterior cingulate cortex, SMA (especially the preSMA), and prefrontal areas where the errors are corrected and the conflicts are resolved. Finally, the correct control signals send to the motor control system.
The process is recycled repeatedly to ensure the smooth execution of a complex motor task. Our findings of decreased rsFCs of the SMA subregions with the FICs and DLPFC in well-recovered stroke patients may suggest the dysfunction of the motor control network. The connectivity deficits within the cognitive control network may result in the deficits even in well-recovered stroke patients when they are performing complex motor tasks.
This hypothesis is supported by the findings that well-recovered stroke patients also exhibited functional deficits in fine or complex motor tasks (Gerloff et al., 2006; Ustinova et al., 2006; Lotze et al., 2011; Verbraak et al., 2012). Our findings are consistent with previous rsFC studies that revealed decreased rsFC (Yin et al., 2012) or resting-state effective connectivity (Inman et al., 2012) of the motor-related areas with prefrontal and parietal brain areas associated with cognitive control.
Besides, our research group have found the disconnection between the cerebellar subregion crus II with the cognitive control frontoparietal network, which may explain the deficits in cognitive control function (Li et al., 2013).
Although our findings suggest rsFC deficits within the motor control network even in well-recovered stroke patients, future studies should be performed on the relationship between the rsFC deficit and the fine cognitive dysfunction in a more direct way.