Unilateral, 3D Arm Movement Kinematics Are Encoded in Ipsilateral Human Cortex.

There is increasing evidence that the hemisphere ipsilateral to a moving limb plays a role in planning and executing movements. However, the exact relationship between cortical activity and ipsilateral limb movements is uncertain. We sought to determine whether 3D arm movement kinematics (speed, velocity, and position) could be decoded from cortical signals recorded from the hemisphere ipsilateral to the moving limb. By having invasively monitored patients perform unilateral reaches with each arm, we also compared the encoding of contralateral and ipsilateral limb kinematics from a single cortical hemisphere. In four motor-intact human patients (three male, one female) implanted with electrocorticography electrodes for localization of their epileptic foci, we decoded 3D movement kinematics of both arms with accuracies above chance. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar, enabling cross-prediction of kinematics between arms. These results clarify our understanding that the ipsilateral hemisphere robustly contributes to motor execution and supports that the information of complex movements is more bihemispherically represented in humans than has been previously understood.SIGNIFICANCE STATEMENT Although limb movements are traditionally understood to be driven by the cortical hemisphere contralateral to a moving limb, movement-related neural activity has also been found in the ipsilateral hemisphere. This study provides the first demonstration that 3D arm movement kinematics can be decoded from human electrocorticographic signals ipsilateral to the moving limb. Surprisingly, the spatial and spectral encoding of contralateral and ipsilateral limb kinematics was similar. The finding that specific kinematics are encoded in the ipsilateral hemisphere demonstrates that the ipsilateral hemisphere contributes to the execution of unilateral limb movements, improving our understanding of motor control. Additionally, the bihemisheric representation of voluntary movements has implications for the development of neuroprosthetic systems for reaching and for neurorehabilitation strategies following cortical injuries.

Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image- level intracranial hemorrhage (ICH) using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level RSNA dataset and fine-tuned on a local dataset using attention-based bidirectional long-short-term memory networks. This local training dataset included 10,699 noncontrast head CT scans from 7469 patients with ICH study-level labels extracted from radiology reports. Model performance was compared with that of two senior neuroradiologists on 100 random test scans using the McNemar test, and its generalizability was evaluated on an external independent dataset. Results The model achieved a positive predictive value (PPV) of 85.7% (95% CI: [84.0%, 87.4%]) and an AUC of 0.96 (95% CI: [0.96, 0.97]) on the held-out local test set (n = 7243, 3721 female) and 89.3% (95% CI: [87.8%, 90.7%]) and 0.96 (95% CI: [0.96, 0.97]), respectively, on the external test set (n = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster (P < .05) diagnostic time of 5.04 seconds per scan (versus 86 seconds and 22.2 seconds for the two neuroradiologists, respectively). The model's attention weights and heatmaps visually aligned with neuroradiologists' interpretations. Conclusion The proposed model demonstrated high generalizability and high PPVs, offering a valuable tool for expedited ICH detection and prioritization while reducing false-positive interruptions in radiologists' workflows. ©RSNA, 2024.

Resting state network estimation in individual subjects.

Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e.g., task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.

Electrophysiological Sequelae of Hemispherotomy in Ipsilateral Human Cortex.

Objectives: Hemispheric disconnection has been used as a treatment of medically refractory epilepsy and evolved from anatomic hemispherectomy to functional hemispherectomies to hemispherotomies. The hemispherotomy procedure involves disconnection of an entire hemisphere with limited tissue resection and is reserved for medically-refractory epilepsy due to diffuse hemispheric disease. Although it is thought to be effective by preventing seizures from spreading to the contralateral hemisphere, the electrophysiological effects of a hemispherotomy on the ipsilateral hemisphere remain poorly defined. The objective of this study was to evaluate the effects of hemispherotomy on the electrophysiologic dynamics in peri-stroke and dysplastic cortex. Methods: Intraoperative electrocorticography (ECoG) was recorded from ipsilateral cortex in 5 human subjects with refractory epilepsy before and after hemispherotomy. Power spectral density, mutual information, and phase-amplitude coupling were measured from the ECoG signals. Results: Epilepsy was a result of remote perinatal stroke in three of the subjects. In two of the subjects, seizures were a consequence of dysplastic tissue: one with hemimegalencephaly and the second with Rasmussen's encephalitis. Hemispherotomy reduced broad-band power spectral density in peri-stroke cortex. Meanwhile, hemispherotomy increased power in the low and high frequency bands for dysplastic cortex. Functional connectivity was increased in lower frequency bands in peri-stroke tissue but not affected in dysplastic tissue after hemispherotomy. Finally, hemispherotomy reduced band-specific phase-amplitude coupling in peristroke cortex but not dysplastic cortex. Significance: Disconnecting deep subcortical connections to peri-stroke cortex via a hemispherotomy attenuates power of oscillations and impairs the transfer of information from large-scale distributed brain networks to the local cortex. Hence, hemispherotomy reduces heterogeneity between neighboring cortex while impairing phase-amplitude coupling. In contrast, dysfunctional networks in dysplastic cortex lack the normal connectivity with distant networks. Therefore hemispherotomy does not produce the same effects.

Decoding three-dimensional reaching movements using electrocorticographic signals in humans.

OBJECTIVE: Electrocorticography (ECoG) signals have emerged as a potential control signal for brain-computer interface (BCI) applications due to balancing signal quality and implant invasiveness. While there have been numerous demonstrations in which ECoG signals were used to decode motor movements and to develop BCI systems, the extent of information that can be decoded has been uncertain. Therefore, we sought to determine if ECoG signals could be used to decode kinematics (speed, velocity, and position) of arm movements in 3D space. APPROACH: To investigate this, we designed a 3D center-out reaching task that was performed by five epileptic patients undergoing temporary placement of ECoG arrays. We used the ECoG signals within a hierarchical partial-least squares (PLS) regression model to perform offline prediction of hand speed, velocity, and position. MAIN RESULTS: The hierarchical PLS regression model enabled us to predict hand speed, velocity, and position during 3D reaching movements from held-out test sets with accuracies above chance in each patient with mean correlation coefficients between 0.31 and 0.80 for speed, 0.27 and 0.54 for velocity, and 0.22 and 0.57 for position. While beta band power changes were the most significant features within the model used to classify movement and rest, the local motor potential and high gamma band power changes, were the most important features in the prediction of kinematic parameters. SIGNIFICANCE: We believe that this study represents the first demonstration that truly three-dimensional movements can be predicted from ECoG recordings in human patients. Furthermore, this prediction underscores the potential to develop BCI systems with multiple degrees of freedom in human patients using ECoG.

Characterization of the effects of the human dura on macro- and micro-electrocorticographic recordings.

OBJECTIVE: Electrocorticography (ECoG) electrodes implanted on the surface of the brain have recently emerged as a potential signal platform for brain-computer interface (BCI) systems. While clinical ECoG electrodes are currently implanted beneath the dura, epidural electrodes could reduce the invasiveness and the potential impact of a surgical site infection. Subdural electrodes, on the other hand, while slightly more invasive, may have better signals for BCI application. Because of this balance between risk and benefit between the two electrode positions, the effect of the dura on signal quality must be determined in order to define the optimal implementation for an ECoG BCI system. APPROACH: This study utilized simultaneously acquired baseline recordings from epidural and subdural ECoG electrodes while patients rested. Both macro-scale (2 mm diameter electrodes with 1 cm inter-electrode distance, one patient) and micro-scale (75 µm diameter electrodes with 1 mm inter-electrode distance, four patients) ECoG electrodes were tested. Signal characteristics were evaluated to determine differences in the spectral amplitude and noise floor. Furthermore, the experimental results were compared to theoretical effects produced by placing epidural and subdural ECoG contacts of different sizes within a finite element model. MAIN RESULTS: The analysis demonstrated that for micro-scale electrodes, subdural contacts have significantly higher spectral amplitudes and reach the noise floor at a higher frequency than epidural contacts. For macro-scale electrodes, while there are statistical differences, these differences are small in amplitude and likely do not represent differences relevant to the ability of the signals to be used in a BCI system. CONCLUSIONS: Our findings demonstrate an important trade-off that should be considered in developing a chronic BCI system. While implanting electrodes under the dura is more invasive, it is associated with increased signal quality when recording from micro-scale electrodes with very small sizes and spacing. If recording from larger electrodes, such as traditionally used clinically, the signal quality of epidural recordings is similar to that of subdural recordings.

Using the electrocorticographic speech network to control a brain-computer interface in humans.

Electrocorticography (ECoG) has emerged as a new signal platform for brain-computer interface (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for BCI operation and that these signals can be acquired from a cortical array that is small and minimally invasive.