A Speech Neuroprosthesis in the Frontal Lobe and Hippocampus: Decoding High-Frequency Activity into Phonemes.

BACKGROUND AND OBJECTIVES: Loss of speech due to injury or disease is devastating. Here, we report a novel speech neuroprosthesis that artificially articulates building blocks of speech based on high-frequency activity in brain areas never harnessed for a neuroprosthesis before: anterior cingulate and orbitofrontal cortices, and hippocampus. METHODS: A 37-year-old male neurosurgical epilepsy patient with intact speech, implanted with depth electrodes for clinical reasons only, silently controlled the neuroprosthesis almost immediately and in a natural way to voluntarily produce 2 vowel sounds. RESULTS: During the first set of trials, the participant made the neuroprosthesis produce the different vowel sounds artificially with 85% accuracy. In the following trials, performance improved consistently, which may be attributed to neuroplasticity. We show that a neuroprosthesis trained on overt speech data may be controlled silently. CONCLUSION: This may open the way for a novel strategy of neuroprosthesis implantation at earlier disease stages (eg, amyotrophic lateral sclerosis), while speech is intact, for improved training that still allows silent control at later stages. The results demonstrate clinical feasibility of direct decoding of high-frequency activity that includes spiking activity in the aforementioned areas for silent production of phonemes that may serve as a part of a neuroprosthesis for replacing lost speech control pathways.

Machine learning decoding of single neurons in the thalamus for speech brain-machine interfaces.

Objective. Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding.Approach. We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space.Main results. Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other.Significance. Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation.

Neuronal Encoding of Speech Features in the Human Thalamus in Parkinson's Disease and Essential Tremor Patients.

BACKGROUND AND OBJECTIVES: The human thalamus is known, from stimulation studies and functional imaging, to participate in high-level language tasks. The goal of this study is to find whether and how speech features, in particular, vowel phonemes, are encoded in the neuronal activity of the thalamus, and specifically of the left ventralis intermediate nucleus (Vim), during speech production, perception, and imagery. METHODS: In this cross-sectional study, we intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients with Parkinson's disease (PD) (n = 4) or essential tremor (n = 4) undergoing implantation of deep brain stimulation (n = 3) or radiofrequency lesioning (n = 5) while patients articulated the five monophthongal vowel sounds. RESULTS: In this article, we report that single neurons in the left Vim encode individual vowel phonemes mainly during speech production but also during perception and imagery. They mainly use one of two encoding schemes: broad or sharp tuning, with a similar percentage of units each. Sinusoidal tuning has been demonstrated in almost half of the broadly tuned units. Patients with PD had a lower percentage of speech-related units in each aspect of speech (production, perception, and imagery), a significantly lower percentage of broadly tuned units, and significantly lower median firing rates during speech production and perception, but significantly higher rates during imagery, than patients with essential tremor. CONCLUSION: The results suggest that the left Vim uses mixed encoding schemes for speech features. Our findings explain, at the single neuron level, why deep brain stimulation and radiofrequency lesioning of the left Vim are likely to cause speech side effects. Moreover, they may indicate that speech-related units in the left Vim of patients with PD may be degraded even in the subclinical phase.

Reduced neural feedback signaling despite robust neuron and gamma auditory responses during human sleep.

During sleep, sensory stimuli rarely trigger a behavioral response or conscious perception. However, it remains unclear whether sleep inhibits specific aspects of sensory processing, such as feedforward or feedback signaling. Here, we presented auditory stimuli (for example, click-trains, words, music) during wakefulness and sleep in patients with epilepsy, while recording neuronal spiking, microwire local field potentials, intracranial electroencephalogram and polysomnography. Auditory stimuli induced robust and selective spiking and high-gamma (80-200 Hz) power responses across the lateral temporal lobe during both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Sleep only moderately attenuated response magnitudes, mainly affecting late responses beyond early auditory cortex and entrainment to rapid click-trains in NREM sleep. By contrast, auditory-induced alpha-beta (10-30 Hz) desynchronization (that is, decreased power), prevalent in wakefulness, was strongly reduced in sleep. Thus, extensive auditory responses persist during sleep whereas alpha-beta power decrease, likely reflecting neural feedback processes, is deficient. More broadly, our findings suggest that feedback signaling is key to conscious sensory processing.

Machine learning algorithm for decoding multiple subthalamic spike trains for speech brain-machine interfaces.

Objective. The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces (BMI).Approach. We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns.Main results. In an extensive comparison of algorithms, our sparse decoder ('SpaDe'), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery.Significance. Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards BMIs for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ('locked-in') patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech BMI.

Impaired Timing of Speech-Related Neurons in the Subthalamic Nucleus of Parkinson Disease Patients Suffering Speech Disorders.

BACKGROUND: Our previous study found degradation to subthalamic neuronal encoding of speech features in Parkinson disease (PD) patients suffering from speech disorders. OBJECTIVE: To find how timing of speech-related neuronal firing changes in PD patients with speech disorders compared to PD patients without speech disorders. METHODS: During the implantation of deep brain stimulator (DBS), we recorded the activity of single neurons in the subthalamic nucleus (STN) of 18 neurosurgical patients with PD while they articulated, listened to, or imagined articulation of 5 vowel sounds, each following a beep. We compared subthalamic activity of PD patients with (n = 10) vs without speech disorders. RESULTS: In this comparison, patients with speech disorders had longer reaction times and shorter lengths of articulation. Their speech-related neuronal activity preceding speech onset (planning) was delayed relative to the beep, but the time between this activity and the emission of speech sound was similar. Notwithstanding, speech-related neuronal activity following the onset of speech (feedback) was delayed when computed relative to the onset. Only in these patients was the time lag of planning neurons significantly correlated with the reaction time. Neuronal activity in patients with speech disorders was delayed during imagined articulation of vowel sounds but earlier during speech perception. CONCLUSION: Our findings indicate that longer reaction times in patients with speech disorders are due to STN or earlier activity of the speech control network. This is a first step in locating the source(s) of PD delays within this network and is therefore of utmost importance for future treatment of speech disorders.

Adopting MR-guided stereotactic laser ablations for epileptic lesions: initial clinical experience and lessons learned.

OBJECTIVE: MR-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive technique for ablating brain lesions under real-time MRI feedback and control of the ablation process. The Medtronic Visualase system was recently approved for use in Europe and Israel. We report our initial technical experience using the system in the first 16 cases in which the system was used to ablate focal epileptogenic lesions. METHODS: We included all consecutive patients with intractable epilepsy who underwent MRgLITT procedures between 2018 and 2020. We reviewed medical charts and imaging studies of patients. Post-ablation MRIs were used to calculate ablation volumes. RESULTS: Seventeen MRgLITT procedures were performed in 16 patients. One cooling catheter/laser fiber assemblies were placed per patient. Indications for surgery were intractable epilepsy due to TLE (n = 7), suspected low-grade glioma (n = 4), radiological cortical dysplasia (n = 1), hypothalamic hamartoma (n = 1), and MR-negative foci (n = 3). Ablations were made using 30 to 70% of the maximal energy of the Visualase system. We used serial ablations as needed along the tract of the catheter by pulling back the optic fiber; the length of the lesion ranged between 7.4 and 38.1 mm. Ablation volume ranged between 0.27 and 6.78 mm3. Immediate post-ablation MRI demonstrated good ablation of the epileptic lesion in 16/17 cases. In one case with mesial temporal sclerosis, no ablation was performed due to suboptimal position of the catheter. That patient was successfully reoperated at a later date. Mean follow-up was 14.9 months (± 11.6 months). Eleven patients had follow-up longer than 12 months. Good seizure control (Engel I, A) was achieved in 7/11 patients (63%) and 1/11 (9%) had significant improvement in seizure frequency (Angle IIIa). Three patients (27%) did not experience improvement in their seizure frequency (Engel IV, B), and one of these patients died during the follow-up period from sudden unexpected death of epilepsy (SUDEP). No immediate or delayed neurological complications were documented in any of the cases during the follow-up period. CONCLUSIONS: MRgLITT is a promising technique and can be used safely as an alternative to open resection in both lesional and non-lesional intractable epilepsy cases. In our local series, the success rate of epilepsy surgery was comparable to recent publications.

Generalizing neural signal-to-text brain-computer interfaces.

Objective:Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. The goal of this study is to evaluate the BCI performance of a robust speech decoding system that translates neural signals evoked by speech to a textual output. While previous studies have approached this problem by using neural signals to choose from a limited set of possible words, we employ a more general model that can type any word from a large corpus of English text.Approach:In this study, we create an end-to-end BCI that translates neural signals associated with overt speech into text output. Our decoding system first isolates frequency bands in the input depth-electrode signal encapsulating differential information regarding production of various phonemic classes. These bands form a feature set that then feeds into a Long Short-Term Memory (LSTM) model which discerns at each time point probability distributions across all phonemes uttered by a subject. Finally, a particle filtering algorithm temporally smooths these probabilities by incorporating prior knowledge of the English language to output text corresponding to the decoded word. The generalizability of our decoder is driven by the lack of a vocabulary constraint on this output word.Main result:This method was evaluated using a dataset of 6 neurosurgical patients implanted with intra-cranial depth electrodes to identify seizure foci for potential surgical treatment of epilepsy. We averaged 32% word accuracy and on the phoneme-level obtained 46% precision, 51% recall and 73.32% average phoneme error rate while also achieving significant increases in speed when compared to several other BCI approaches.Significance:Our study employs a more general neural signal-to-text model which could facilitate communication by patients in everyday environments.

Anesthesia-induced loss of consciousness disrupts auditory responses beyond primary cortex.

Despite its ubiquitous use in medicine, and extensive knowledge of its molecular and cellular effects, how anesthesia induces loss of consciousness (LOC) and affects sensory processing remains poorly understood. Specifically, it is unclear whether anesthesia primarily disrupts thalamocortical relay or intercortical signaling. Here we recorded intracranial electroencephalogram (iEEG), local field potentials (LFPs), and single-unit activity in patients during wakefulness and light anesthesia. Propofol infusion was gradually increased while auditory stimuli were presented and patients responded to a target stimulus until they became unresponsive. We found widespread iEEG responses in association cortices during wakefulness, which were attenuated and restricted to auditory regions upon LOC. Neuronal spiking and LFP responses in primary auditory cortex (PAC) persisted after LOC, while responses in higher-order auditory regions were variable, with neuronal spiking largely attenuated. Gamma power induced by word stimuli increased after LOC while its frequency profile slowed, thus differing from local spiking activity. In summary, anesthesia-induced LOC disrupts auditory processing in association cortices while relatively sparing responses in PAC, opening new avenues for future research into mechanisms of LOC and the design of anesthetic monitoring devices.

Degradation of Neuronal Encoding of Speech in the Subthalamic Nucleus in Parkinson's Disease.

BACKGROUND: Most of the patients with Parkinson's disease suffer from speech disorders characterized mainly by dysarthria and hypophonia. OBJECTIVE: To understand the deterioration of speech in the course of Parkinson's disease. METHODS: We intraoperatively recorded single neuron activity in the subthalamic nucleus of 18 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator while patients articulated 5 vowel sounds. RESULTS: Here, we report that single subthalamic neurons encode individual vowel phonemes and employ 1 of 2 encoding schemes: broad or sharp tuning. Broadly tuned units respond to all examined phonemes, each with a different firing rate, whereas sharply tuned ones are specific to 1 to 2 phonemes. We then show that in comparison with patients without speech deficits, the spiking activity in patients with speech disorders was lower during speech production, overt or imagined, but not during perception. However, patients with speech disorders employed a larger percentage of the neurons for the aforementioned tasks. Whereas the lower firing rates affect mainly sharply tuned units, the extra units used a broad tuning encoding scheme. CONCLUSION: Our findings suggest mechanisms of neuronal degradation due to Parkinsonian speech disorders and their possible compensation. As impairment in sharply tuned units may be compensated by broadly tuned ones, the proposed compensation model appears to be suboptimal, lending support to the persistence of speech disorders in the course of the disease.

Pace of movement: the role of single neurons in the subthalamic nucleus.

OBJECTIVEThe ability to modulate the pace of movement is a critical factor in the smooth operation of the motor system. The authors recently described distinct and overlapping representations of movement kinematics in the subthalamic nucleus (STN), but it is still unclear how movement pace is modulated according to the demands of the task at the neuronal level in this area. The goal of this study was to clarify how different movement paces are being controlled by neurons in the STN.METHODSThe authors performed direct recording of the electrical activity of single neurons in the STN of neurosurgical patients with Parkinson's disease undergoing implantation of a deep brain stimulator under local anesthesia while the patients performed repetitive foot and hand movements intraoperatively at multiple paces.RESULTSA change was observed in the neuronal population controlling the movement for each pace. The mechanism for switching between these controlling populations differs for hand and foot movements.CONCLUSIONSThese findings suggest that disparate schemes are utilized in the STN for neuronal recruitment for motor control of the upper and lower extremities. The results indicate a distributed model of motor control within the STN, where the active neuronal population changes when modifying the task condition and pace.

Subthalamic Neurons Encode Both Single- and Multi-Limb Movements in Parkinson's Disease Patients.

The subthalamic nucleus (STN) is the main target for neurosurgical treatment of motor signs of Parkinson's disease (PD). Despite the therapeutic effect on both upper and lower extremities, its role in motor control and coordination and its changes in Parkinson's disease are not fully clear. We intraoperatively recorded single unit activity in ten patients with PD who performed repetitive feet or hand movements while undergoing implantation of a deep brain stimulator. We found both distinct and overlapping representations of upper and lower extremity movement kinematics in subthalamic units and observed evidence for re-routing to a multi-limb representation that participates in limb coordination. The well-known subthalamic somatotopy showed a large overlap of feet and hand representations in the PD patients. This overlap and excessive amounts of kinematics or coordination units may reflect pathophysiology or compensatory mechanisms. Our findings thus explain, at the single neuron level, the important subthalamic role in motor control and coordination and indicate the effect of PD on the neuronal representation of movement.

Exploring human epileptic activity at the single-neuron level.

Today, localization of the seizure focus heavily relies on EEG monitoring (scalp or intracranial). However, current technology enables much finer resolutions. The activity of hundreds of single neurons in the human brain can now be simultaneously explored before, during, and after a seizure or in association with an interictal discharge. This technology opens up new horizons to understanding epilepsy at a completely new level. This review therefore begins with a brief description of the basis of the technology, the microelectrodes, and the setup for their implantation in patients with epilepsy. Using these electrodes, recent studies provide novel insights into both the time domain and firing patterns of epileptic activity of single neurons. In the time domain, seizure-related activity may occur even minutes before seizure onset (in its current, EEG-based definition). Seizure-related neuronal interactions exhibit complex heterogeneous dynamics. In the seizure-onset zone, changes in firing patterns correlate with cell loss; in the penumbra, neurons maintain their spike stereotypy during a seizure. Hence, investigation of the extracellular electrical activity is expected to provide a better understanding of the mechanisms underlying the disease; it may, in the future, serve for a more accurate localization of the seizure focus; and it may also be employed to predict the occurrence of seizures prior to their behavioral manifestation in order to administer automatic therapeutic interventions.

Can a Body-Fixed Sensor Reduce Heisenberg's Uncertainty When It Comes to the Evaluation of Mobility? Effects of Aging and Fall Risk on Transitions in Daily Living.

BACKGROUND: Functional performance-based tests like the Timed Up and Go test (TUG) and its subtasks have been associated with fall risk, future disability, nursing home admission, and other poor outcomes in older adults. However, a single measurement in the laboratory may not fully reflect the subject's condition and everyday performance. To begin to validate an approach based on long-term, continuous monitoring, we investigated the sit-to-walk and walk-to-sit transitions performed spontaneously and naturally during daily living. METHODS: Thirty young adults, 38 older adults, and 33 elderly (idiopathic) fallers were studied. After evaluating mobility and functional performance in the laboratory, participants wore an accelerometer on their lower back for 3 days. We analyzed the sit-to-walk and walk-to-sit transitions using temporal and distribution-related features. Machine learning algorithms assessed the feature set's ability to discriminate between the different cohorts. RESULTS: 5,027 transitions were analyzed. Significant differences were observed between the young and older adults (p < .044) and between the fallers and older adults (p < .032). Machine learning algorithms classified the young and older adult with an accuracy of about 98% and the fallers and the older adults at 88%, which was better than the results achieved using traditional laboratory assessments (~72%). CONCLUSIONS: Features extracted from the multiple transitions recorded during daily living apparently reflect changes associated with aging and fall risk. Long-term monitoring of temporal features and their distribution may be helpful to provide a more complete and accurate assessment of the effects of aging and fall risk on daily function and mobility.

Preconscious prediction of a driver's decision using intracranial recordings.

While driving, we make numerous conscious decisions such as route and turn direction selection. Although drivers are held responsible, the neural processes that govern such decisions are not clear. We recorded intracranial EEG signals from six patients engaged in a computer-based driving simulator. Patients decided which way to turn (left/right) and subsequently reported the time of the decision. We show that power modulations of gamma band oscillations (30-100 Hz) preceding the reported time of decision (up to 5.5 sec) allow prediction of decision content with high accuracy (up to 82.4%) on a trial-by-trial basis, irrespective of subsequent motor output. Moreover, these modulations exhibited a spatiotemporal gradient, differentiating left/right decisions earliest in premotor cortices and later in more anterior and lateral regions. Our results suggest a preconscious role for the premotor cortices in early stages of decision-making, which permits foreseeing and perhaps modifying the content of real-life human choices before they are consciously made.

Cognitive-motor brain-machine interfaces.

Brain-machine interfaces (BMIs) open new horizons for the treatment of paralyzed persons, giving hope for the artificial restoration of lost physiological functions. Whereas BMI development has mainly focused on motor rehabilitation, recent studies have suggested that higher cognitive functions can also be deciphered from brain activity, bypassing low level planning and execution functions, and replacing them by computer-controlled effectors. This review describes the new generation of cognitive-motor BMIs, focusing on three BMI types: By outlining recent progress in developing these BMI types, we aim to provide a unified view of contemporary research towards the replacement of behavioral outputs of cognitive processes by direct interaction with the brain.

Sparse decoding of multiple spike trains for brain-machine interfaces.

Brain-machine interfaces (BMIs) rely on decoding neuronal activity from a large number of electrodes. The implantation procedures, however, do not guarantee that all recorded units encode task-relevant information: selection of task-relevant neurons is critical to performance but is typically performed based on heuristics. Here, we describe an algorithm for decoding/classification of volitional actions from multiple spike trains, which automatically selects the relevant neurons. The method is based on sparse decomposition of the high-dimensional neuronal feature space, projecting it onto a low-dimensional space of codes serving as unique class labels. The new method is tested against a range of existing methods using simulations and recordings of the activity of 1592 neurons in 23 neurosurgical patients who performed motor or speech tasks. The parameter estimation algorithm is orders of magnitude faster than existing methods and achieves significantly higher accuracies for both simulations and human data, rendering sparse decoding highly attractive for BMIs.

Structured neuronal encoding and decoding of human speech features.

Human speech sounds are produced through a coordinated movement of structures along the vocal tract. Here we show highly structured neuronal encoding of vowel articulation. In medial-frontal neurons, we observe highly specific tuning to individual vowels, whereas superior temporal gyrus neurons have nonspecific, sinusoidally modulated tuning (analogous to motor cortical directional tuning). At the neuronal population level, a decoding analysis reveals that the underlying structure of vowel encoding reflects the anatomical basis of articulatory movements. This structured encoding enables accurate decoding of volitional speech segments and could be applied in the development of brain-machine interfaces for restoring speech in paralysed individuals.

Visuomotor coordination and motor representation by human temporal lobe neurons.

The division of cortical visual processing into distinct dorsal and ventral streams is a key concept in primate neuroscience [Goodale, M. A., & Milner, A. D. Separate visual pathways for perception and action. Trends in Neurosciences, 15, 20-25, 1992; Steele, G., Weller, R., & Cusick, C. Cortical connections of the caudal subdivision of the dorsolateral area (V4) in monkeys. Journal of Comparative Neurology, 306, 495-520, 1991]. The ventral stream is usually characterized as a "What" pathway, whereas the dorsal stream is implied in mediating spatial perception ("Where") and visually guided actions ("How"). A subpathway emerging from the dorsal stream and projecting to the medial-temporal lobe has been identified [Kravitz, D. J., Saleem, K. S., Baker, C. I., & Mishkin, M. A new neural framework for visuospatial processing. Nature Reviews Neuroscience, 12, 217-230, 2011; Cavada, C., & Goldman-Raiuc, P. S. Posterior parietal cortex in rhesus monkey: I. Parcellation of areas based on distinctive limbic and sensory cortico-cortical connections. Journal of Comparative Neurology, 287, 393-421, 1989]. The current article studies the coordination of visual information typically associated with the dorsal stream ("Where"), with planned movements, focusing on the temporal lobe. We recorded extracellular activity from 565 cells in the human medial-temporal and frontal lobes while 13 patients performed cued hand movements with visual feedback (visuomotor task), without feedback (motor task), or observed visual feedback without motor movement (visual-only task). We discovered two different neural populations in the human medial-temporal lobe. One consists of motor-like neurons representing hand position, speed or acceleration during the motor task but not during the visuomotor or visual tasks. The other is specific to the parahippocampal gyrus (an area known to process visual motion [Gur, M., & Snodderly, D. M. Direction selectivity in V1 of alert monkeys: Evidence for parallel pathways for motion processing. Journal of Physiology, 585, 383-400, 2007; Sato, N., & Nakamura, K. Visual response properties of neurons in the parahippocampal cortex of monkeys. Journal of Neurophysiology, 90, 876-886, 2003]) and encodes speed, acceleration, or direction of hand movements, but only during the visuomotor task: neither during visual-only nor during motor tasks. These findings suggest a functional basis for the anatomical subpathway between the dorsal stream and the medial-temporal lobe. Similar to the recent expansion of the motor control process into the sensory cortex [Matyas, F., Sreenivasan, V., Marbach, F., Wacongne, C., Barsy, B., Mateo, C., et al. Motor control by sensory cortex. Science, 330, 1240-1243, 2010], our findings render the human medial-temporal lobe an important junction in the process of planning and execution of motor acts whether internally or externally (visually) driven. Thus, the medial-temporal lobe might serve as an integration node between the two processing streams. Our findings thus shed new light on the brain mechanisms underlying visuomotor coordination which is a crucial capacity for everyday survival, whether it is identifying and picking up food, sliding a key into a lock, driving a vehicle, or escaping a predator.