Does Ipsilateral Remapping Following Hand Loss Impact Motor Control of the Intact Hand?

What happens once a cortical territory becomes functionally redundant? We studied changes in brain function and behavior for the remaining hand in humans (male and female) with either a missing hand from birth (one-handers) or due to amputation. Previous studies reported that amputees, but not one-handers, show increased ipsilateral activity in the somatosensory territory of the missing hand (i.e., remapping). We used a complex finger task to explore whether this observed remapping in amputees involves recruiting more neural resources to support the intact hand to meet greater motor control demands. Using basic fMRI analysis, we found that only amputees had more ipsilateral activity when motor demand increased; however, this did not match any noticeable improvement in their behavioral task performance. More advanced multivariate fMRI analyses showed that amputees had stronger and more typical representation-relative to controls' contralateral hand representation-compared with one-handers. This suggests that in amputees, both hand areas work together more collaboratively, potentially reflecting the intact hand's efference copy. One-handers struggled to learn difficult finger configurations, but this did not translate to differences in univariate or multivariate activity relative to controls. Additional white matter analysis provided conclusive evidence that the structural connectivity between the two hand areas did not vary across groups. Together, our results suggest that enhanced activity in the missing hand territory may not reflect intact hand function. Instead, we suggest that plasticity is more restricted than generally assumed and may depend on the availability of homologous pathways acquired early in life.

Intact finger representation within primary sensorimotor cortex of musician's dystonia.

Musician's dystonia presents with a persistent deterioration of motor control during musical performance. A predominant hypothesis has been that this is underpinned by maladaptive neural changes to the somatotopic organization of finger representations within primary somatosensory cortex. Here, we tested this hypothesis by investigating the finger-specific activity patterns in the primary somatosensory and motor cortex using functional MRI and multivariate pattern analysis in nine musicians with dystonia and nine healthy musicians. A purpose-built keyboard device allowed characterization of activity patterns elicited during passive extension and active finger presses of individual fingers. We analysed the data using both traditional spatial analysis and state-of-the art multivariate analyses. Our analysis reveals that digit representations in musicians were poorly captured by spatial analyses. An optimized spatial metric found clear somatotopy but no difference in the spatial geometry between fingers with dystonia. Representational similarity analysis was confirmed as a more reliable technique than all spatial metrics evaluated. Significantly, the dissimilarity architecture was equivalent for musicians with and without dystonia. No expansion or spatial shift of digit representation maps were found in the symptomatic group. Our results therefore indicate that the neural representation of generic finger maps in primary sensorimotor cortex is intact in musician's dystonia. These results speak against the idea that task-specific dystonia is associated with a distorted hand somatotopy and lend weight to an alternative hypothesis that task-specific dystonia is due to a higher-order disruption of skill encoding. Such a formulation can better explain the task-specific deficit and offers alternative inroads for therapeutic interventions.

Deep Multi-Scale Features Fusion for Effective Violence Detection and Control Charts Visualization.

The study of automated video surveillance systems study using computer vision techniques is a hot research topic and has been deployed in many real-world CCTV environments. The main focus of the current systems is higher accuracy, while the assistance of surveillance experts in effective data analysis and instant decision making using efficient computer vision algorithms need researchers' attentions. In this research, to the best of our knowledge, we are the first to introduce a process control technique: control charts for surveillance video data analysis. The control charts concept is merged with a novel deep learning-based violence detection framework. Different from the existing methods, the proposed technique considers the importance of spatial information, as well as temporal representations of the input video data, to detect human violence. The spatial information are fused with the temporal dimension of the deep learning model using a multi-scale strategy to ensure that the temporal information are properly assisted by the spatial representations at multi-levels. The proposed frameworks' results are kept in the history-maintaining module of the control charts to validate the level of risks involved in the live input surveillance video. The detailed experimental results over the existing datasets and the real-world video data demonstrate that the proposed approach is a prominent solution towards automated surveillance with the pre- and post-analyses of violent events.

No evidence for motor-recovery-related cortical connectivity changes after stroke using resting-state fMRI.

It has been proposed that a form of cortical reorganization (changes in functional connectivity between brain areas) can be assessed with resting-state (rs) functional MRI (fMRI). Here, we report a longitudinal data set collected from 19 patients with subcortical stroke and 11 controls. Patients were imaged up to five times over 1 year. We found no evidence, using rs-fMRI, for longitudinal poststroke cortical connectivity changes despite substantial behavioral recovery. These results could be construed as questioning the value of resting-state imaging. Here, we argue instead that they are consistent with other emerging reasons to challenge the idea of motor-recovery-related cortical reorganization poststroke when conceived of as changes in connectivity between cortical areas.NEW & NOTEWORTHY We investigated longitudinal changes in functional connectivity after stroke. Despite substantial motor recovery, we found no differences in functional connectivity patterns between patients and controls, nor any changes over time. Assuming that rs-fMRI is an adequate method to capture connectivity changes between cortical regions after brain injury, these results provide reason to doubt that changes in cortico-cortical connectivity are the relevant mechanism for promoting motor recovery.

Structure of Population Activity in Primary Motor Cortex for Single Finger Flexion and Extension.

How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1.SIGNIFICANCE STATEMENT The primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

Obtaining and maintaining cortical hand representation as evidenced from acquired and congenital handlessness.

A key question in neuroscience is how cortical organisation relates to experience. Previously we showed that amputees experiencing highly vivid phantom sensations maintain cortical representation of their missing hand (Kikkert et al., 2016). Here, we examined the role of sensory hand experience on persistent hand representation by studying individuals with acquired and congenital hand loss. We used representational similarity analysis in primary somatosensory and motor cortex during missing and intact hand movements. We found that key aspects of acquired amputees' missing hand representation persisted, despite varying vividness of phantom sensations. In contrast, missing hand representation of congenital one-handers, who do not experience phantom sensations, was significantly reduced. Across acquired amputees, individuals' reported motor control over their phantom hand positively correlated with the extent to which their somatosensory hand representation was normally organised. We conclude that once cortical organisation is formed, it is remarkably persistent, despite long-term attenuation of peripheral signals.

Ipsilateral finger representations in the sensorimotor cortex are driven by active movement processes, not passive sensory input.

Hand and finger movements are mostly controlled through crossed corticospinal projections from the contralateral hemisphere. During unimanual movements, activity in the contralateral hemisphere is increased while the ipsilateral hemisphere is suppressed below resting baseline. Despite this suppression, unimanual movements can be decoded from ipsilateral activity alone. This indicates that ipsilateral activity patterns represent parameters of ongoing movement, but the origin and functional relevance of these representations is unclear. In this study, we asked whether ipsilateral representations are caused by active movement or whether they are driven by sensory input. Participants alternated between performing single finger presses and having fingers passively stimulated while we recorded brain activity using high-field (7T) functional imaging. We contrasted active and passive finger representations in sensorimotor areas of ipsilateral and contralateral hemispheres. Finger representations in the contralateral hemisphere were equally strong under passive and active conditions, highlighting the importance of sensory information in feedback control. In contrast, ipsilateral finger representations in the sensorimotor cortex were stronger during active presses. Furthermore, the spatial distribution of finger representations differed between hemispheres: the contralateral hemisphere showed the strongest finger representations in Brodmann areas 3a and 3b, whereas the ipsilateral hemisphere exhibited stronger representations in premotor and parietal areas. Altogether, our results suggest that finger representations in the two hemispheres have different origins: contralateral representations are driven by both active movement and sensory stimulation, whereas ipsilateral representations are mainly engaged during active movement. NEW & NOTEWORTHY Movements of the human body are mostly controlled by contralateral cortical regions. The function of ipsilateral activity during movements remains elusive. Using high-field neuroimaging, we investigated how human contralateral and ipsilateral hemispheres represent active and passive finger presses. We found that representations in contralateral sensorimotor cortex are equally strong during both conditions. Ipsilateral representations were mostly present during active movement, suggesting that sensorimotor areas do not receive direct sensory input from the ipsilateral hand.

Evidence for a subcortical origin of mirror movements after stroke: a longitudinal study.

Following a stroke, mirror movements are unintended movements that appear in the non-paretic hand when the paretic hand voluntarily moves. Mirror movements have previously been linked to overactivation of sensorimotor areas in the non-lesioned hemisphere. In this study, we hypothesized that mirror movements might instead have a subcortical origin, and are the by-product of subcortical motor pathways upregulating their contributions to the paretic hand. To test this idea, we first characterized the time course of mirroring in 53 first-time stroke patients, and compared it to the time course of activities in sensorimotor areas of the lesioned and non-lesioned hemispheres (measured using functional MRI). Mirroring in the non-paretic hand was exaggerated early after stroke (Week 2), but progressively diminished over the year with a time course that parallelled individuation deficits in the paretic hand. We found no evidence of cortical overactivation that could explain the time course changes in behaviour, contrary to the cortical model of mirroring. Consistent with a subcortical origin of mirroring, we predicted that subcortical contributions should broadly recruit fingers in the non-paretic hand, reflecting the limited capacity of subcortical pathways in providing individuated finger control. We therefore characterized finger recruitment patterns in the non-paretic hand during mirroring. During mirroring, non-paretic fingers were broadly recruited, with mirrored forces in homologous fingers being only slightly larger (1.76 times) than those in non-homologous fingers. Throughout recovery, the pattern of finger recruitment during mirroring for patients looked like a scaled version of the corresponding control mirroring pattern, suggesting that the system that is responsible for mirroring in controls is upregulated after stroke. Together, our results suggest that post-stroke mirror movements in the non-paretic hand, like enslaved movements in the paretic hand, are caused by the upregulation of a bilaterally organized subcortical system.

Separable systems for recovery of finger strength and control after stroke.

Impaired hand function after stroke is a major cause of long-term disability. We developed a novel paradigm that quantifies two critical aspects of hand function, strength, and independent control of fingers (individuation), and also removes any obligatory dependence between them. Hand recovery was tracked in 54 patients with hemiparesis over the first year after stroke. Most recovery of strength and individuation occurred within the first 3 mo. A novel time-invariant recovery function was identified: recovery of strength and individuation were tightly correlated up to a strength level of ~60% of estimated premorbid strength; beyond this threshold, strength improvement was not accompanied by further improvement in individuation. Any additional improvement in individuation was attributable instead to a second process that superimposed on the recovery function. We conclude that two separate systems are responsible for poststroke hand recovery: one contributes almost all of strength and some individuation; the other contributes additional individuation.NEW & NOTEWORTHY We tracked recovery of the hand over a 1-yr period after stroke in a large cohort of patients, using a novel paradigm that enabled independent measurement of finger strength and control. Most recovery of strength and control occurs in the first 3 mo after stroke. We found that two separable systems are responsible for motor recovery of hand: one contributes strength and some dexterity, whereas a second contributes additional dexterity.

Reliability of dissimilarity measures for multi-voxel pattern analysis.

Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries.

Hand use predicts the structure of representations in sensorimotor cortex.

Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex.

Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation.

The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists.

MRT letter: visual attention driven framework for hysteroscopy video abstraction.

Diagnostic hysteroscopy is a popular method for investigating the regions in the female reproductive system. The videos generated by hysteroscopy sessions of patients are recurrently archived in medical libraries. Gynecologists often need to browse these libraries in search of similar cases or for reviewing old videos of a patient. Diagnostic hysteroscopy videos contain a lot of information with abundant redundancy. Key frame extraction-based video summarization can be used to reduce this huge amount of data. Moreover, key frames can be used for browsing and indexing of hysteroscopy videos. In this article, a domain specific visual attention driven framework for summarization of hysteroscopy videos is proposed. The visual attention model is materialized by computing saliency based on color, texture, and motion. The experimental results, in comparison with other techniques, demonstrate the efficacy of the proposed framework.

Closed-loop response properties of a visual interneuron involved in fly optomotor control.

Due to methodological limitations neural function is mostly studied under open-loop conditions. Normally, however, nervous systems operate in closed-loop where sensory input is processed to generate behavioral outputs, which again change the sensory input. Here, we investigate the closed-loop responses of an identified visual interneuron, the blowfly H1-cell, that is part of a neural circuit involved in optomotor flight and gaze control. Those behaviors may be triggered by attitude changes during flight in turbulent air. The fly analyses the resulting retinal image shifts and performs compensatory body and head rotations to regain its default attitude. We developed a fly robot interface to study H1-cell responses in a 1 degree-of-freedom image stabilization task. Image shifts, induced by externally forced rotations, modulate the cell's spike rate that controls counter rotations of a mobile robot to minimize relative motion between the robot and its visual surroundings. A feedback controller closed the loop between neural activity and the rotation of the robot. Under these conditions we found the following H1-cell response properties: (i) the peak spike rate decreases when the mean image velocity is increased, (ii) the relationship between spike rate and image velocity depends on the standard deviation of the image velocities suggesting adaptive scaling of the cell's signaling range, and (iii) the cell's gain decreases linearly with increasing image accelerations. Our results reveal a remarkable qualitative similarity between the response dynamics of the H1-cell under closed-loop conditions with those obtained in previous open-loop experiments. Finally, we show that the adaptive scaling of the H1-cell's responses, while maximizing information on image velocity, decreases the cell's sensitivity to image accelerations. Understanding such trade-offs in biological vision systems may advance the design of smart vision sensors for autonomous robots.

A multivariate method to determine the dimensionality of neural representation from population activity.

How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept to approach this question: According to this model, the activation patterns across a neuronal population are composed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classifiers that use only the first d most important pattern dimensions. Using cross-validation, we can identify the classifier that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all fingers of a hand at different force levels and (ii) presses of different individual fingers at a single force level. As expected, the new method shows that the representation of force is low-dimensional; the neural activation for different force levels is scaled versions of each other. In comparison, individual finger presses are represented in a full, four-dimensional feature space. The approach can be used to determine an important characteristic of neuronal population codes without knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches.

Meat and fish freshness inspection system based on odor sensing.

We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.

An experimental platform to study the closed-loop performance of brain-machine interfaces.

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.