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.

Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis.

Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.

Cooperation Not Competition: Bihemispheric tDCS and fMRI Show Role for Ipsilateral Hemisphere in Motor Learning.

What is the role of ipsilateral motor and premotor areas in motor learning? One view is that ipsilateral activity suppresses contralateral motor cortex and, accordingly, that inhibiting ipsilateral regions can improve motor learning. Alternatively, the ipsilateral motor cortex may play an active role in the control and/or learning of unilateral hand movements. We approached this question by applying double-blind bihemispheric transcranial direct current stimulation (tDCS) over both contralateral and ipsilateral motor cortex in a between-group design during 4 d of unimanual explicit sequence training in human participants. Independently of whether the anode was placed over contralateral or ipsilateral motor cortex, bihemispheric stimulation yielded substantial performance gains relative to unihemispheric or sham stimulation. This performance advantage appeared to be supported by plastic changes in both hemispheres. First, we found that behavioral advantages generalized strongly to the untrained hand, suggesting that tDCS strengthened effector-independent representations. Second, functional imaging during speed-matched execution of trained sequences conducted 48 h after training revealed sustained, polarity-independent increases in activity in both motor cortices relative to the sham group. These results suggest a cooperative rather than competitive interaction of the two motor cortices during skill learning and suggest that bihemispheric brain stimulation during unimanual skill learning may be beneficial because it harnesses plasticity in the ipsilateral hemisphere.SIGNIFICANCE STATEMENT Many neurorehabilitation approaches are based on the idea that is beneficial to boost excitability in the contralateral hemisphere while attenuating that of the ipsilateral cortex to reduce interhemispheric inhibition. We observed that bihemispheric transcranial direct current stimulation (tDCS) with the excitatory anode either over contralateral or ipsilateral motor cortex facilitated motor learning nearly twice as strongly as unihemispheric tDCS. These increases in motor learning were accompanied by increases in fMRI activation in both motor cortices that outlasted the stimulation period, as well as increased generalization to the untrained hand. Collectively, our findings suggest a cooperative rather than a competitive role of the hemispheres and imply that it is most beneficial to harness plasticity in both hemispheres in neurorehabilitation of motor deficits.

Effector-independent motor sequence representations exist in extrinsic and intrinsic reference frames.

Many daily activities rely on the ability to produce meaningful sequences of movements. Motor sequences can be learned in an effector-specific fashion (such that benefits of training are restricted to the trained hand) or an effector-independent manner (meaning that learning also facilitates performance with the untrained hand). Effector-independent knowledge can be represented in extrinsic/world-centered or in intrinsic/body-centered coordinates. Here, we used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis to determine the distribution of intrinsic and extrinsic finger sequence representations across the human neocortex. Participants practiced four sequences with one hand for 4 d, and then performed these sequences during fMRI with both left and right hand. Between hands, these sequences were equivalent in extrinsic or intrinsic space, or were unrelated. In dorsal premotor cortex (PMd), we found that sequence-specific activity patterns correlated higher for extrinsic than for unrelated pairs, providing evidence for an extrinsic sequence representation. In contrast, primary sensory and motor cortices showed effector-independent representations in intrinsic space, with considerable overlap of the two reference frames in caudal PMd. These results suggest that effector-independent representations exist not only in world-centered, but also in body-centered coordinates, and that PMd may be involved in transforming sequential knowledge between the two. Moreover, although effector-independent sequence representations were found bilaterally, they were stronger in the hemisphere contralateral to the trained hand. This indicates that intermanual transfer relies on motor memories that are laid down during training in both hemispheres, but preferentially draws upon sequential knowledge represented in the trained hemisphere.

Bihemispheric transcranial direct current stimulation enhances effector-independent representations of motor synergy and sequence learning.

Complex manual tasks-everything from buttoning up a shirt to playing the piano-fundamentally involve two components: (1) generating specific patterns of muscle activity (here, termed "synergies"); and (2) stringing these into purposeful sequences. Although transcranial direct current stimulation (tDCS) of the primary motor cortex (M1) has been found to increase the learning of motor sequences, it is unknown whether it can similarly facilitate motor synergy learning. Here, we determined the effects of tDCS on the learning of motor synergies using a novel hand configuration task that required the production of difficult muscular activation patterns. Bihemispheric tDCS was applied to M1 of healthy, right-handed human participants during 4 d of repetitive left-hand configuration training in a double-blind design. tDCS augmented synergy learning, leading subsequently to faster and more synchronized execution. This effect persisted for at least 4 weeks after training. Qualitatively similar tDCS-associated improvements occurred during training of finger sequences in a separate subject cohort. We additionally determined whether tDCS only improved the acquisition of motor memories for specific synergies/sequences or whether it also facilitated more general parts of the motor representations, which could be transferred to novel movements. Critically, we observed that tDCS effects generalized to untrained hand configurations and untrained finger sequences (i.e., were nonspecific), as well as to the untrained hand (i.e., were effector-independent). Hence, bihemispheric tDCS may be a promising adjunct to neurorehabilitative training regimes, in which broad transfer to everyday tasks is highly desirable.

Two distinct ipsilateral cortical representations for individuated finger movements.

Movements of the upper limb are controlled mostly through the contralateral hemisphere. Although overall activity changes in the ipsilateral motor cortex have been reported, their functional significance remains unclear. Using human functional imaging, we analyzed neural finger representations by studying differences in fine-grained activation patterns for single isometric finger presses. We demonstrate that cortical motor areas encode ipsilateral movements in 2 fundamentally different ways. During unimanual ipsilateral finger presses, primary sensory and motor cortices show, underneath global suppression, finger-specific activity patterns that are nearly identical to those elicited by contralateral mirror-symmetric action. This component vanishes when both motor cortices are functionally engaged during bimanual actions. We suggest that the ipsilateral representation present during unimanual presses arises because otherwise functionally idle circuits are driven by input from the opposite hemisphere. A second type of representation becomes evident in caudal premotor and anterior parietal cortices during bimanual actions. In these regions, ipsilateral actions are represented as nonlinear modulation of activity patterns related to contralateral actions, an encoding scheme that may provide the neural substrate for coordinating bimanual movements. We conclude that ipsilateral cortical representations change their informational content and functional role, depending on the behavioral context.

Boundaries of task-specificity: bimanual finger dexterity is reduced in musician's dystonia.

Task-specific dystonia leads to loss of sensorimotor control for a particular motor skill. Although focal in nature, it is hugely disabling and can terminate professional careers in musicians. Biomarkers for underlying mechanism and severity are much needed. In this study, we designed a keyboard device that measured the forces generated at all fingertips during individual finger presses. By reliably quantifying overflow to other fingers in the instructed (enslaving) and contralateral hand (mirroring) we explored whether this task could differentiate between musicians with and without dystonia. 20 right-handed professional musicians (11 with dystonia) generated isometric flexion forces with the instructed finger to match 25%, 50% or 75% of maximal voluntary contraction for that finger. Enslaving was estimated as a linear slope of the forces applied across all instructed/uninstructed finger combinations. Musicians with dystonia had a small but robust loss of finger dexterity. There was increased enslaving and mirroring, primarily during use of the symptomatic hand (enslaving p = 0.003; mirroring p = 0.016), and to a lesser extent with the asymptomatic hand (enslaving p = 0.052; mirroring p = 0.062). Increased enslaving and mirroring were seen across all combinations of finger pairs. In addition, enslaving was exaggerated across symptomatic fingers when more than one finger was clinically affected. Task-specific dystonia therefore appears to express along a gradient, most severe in the affected skill with subtle and general motor control dysfunction in the background. Recognition of this provides a more nuanced understanding of the sensorimotor control deficits at play and can inform therapeutic options for this highly disabling disorder.

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.

Comparing the similarity and spatial structure of neural representations: a pattern-component model.

In recent years there has been growing interest in multivariate analyses of neuroimaging data, which can be used to detect distributed patterns of activity that encode an experimental factor of interest. In this setting, it has become common practice to study the correlations between patterns to make inferences about the way a brain region represents stimuli or tasks (known as representational similarity analysis). Although it would be of great interest to compare these correlations from different regions, direct comparisons are currently not possible. This is because sample correlations are strongly influenced by voxel-selection, fMRI noise, and nonspecific activation patterns, all of which can differ widely between regions. Here, we present a multivariate modeling framework in which the measured patterns are decomposed into their constituent parts. The model is based on a standard linear mixed model, in which pattern components are considered to be randomly distributed over voxels. The model allows one to estimate the true correlations of the underlying neuronal pattern components, thereby enabling comparisons between different regions or individuals. The pattern estimates also allow us to make inferences about the spatial structure of different response components. Thus, the new model provides a theoretical and analytical framework to study the structure of distributed neural representations.

A comparison of volume-based and surface-based multi-voxel pattern analysis.

For functional magnetic resonance imaging (fMRI), multi-voxel pattern analysis (MVPA) has been shown to be a sensitive method to detect areas that encode certain stimulus dimensions. By moving a searchlight through the volume of the brain, one can continuously map the information content about the experimental conditions of interest to the brain. Traditionally, the searchlight is defined as a volume sphere that does not take into account the anatomy of the cortical surface. Here we present a method that uses a cortical surface reconstruction to guide voxel selection for information mapping. This approach differs in two important aspects from a volume-based searchlight definition. First, it uses only voxels that are classified as grey matter based on an anatomical scan. Second, it uses a surface-based geodesic distance metric to define neighbourhoods of voxels, and does not select voxels across a sulcus. We study here the influence of these two factors onto classification accuracy and onto the spatial specificity of the resulting information map. In our example data set, participants pressed one of four fingers while undergoing fMRI. We used MVPA to identify regions in which local fMRI patterns can successfully discriminate which finger was moved. We show that surface-based information mapping is a more sensitive measure of local information content, and provides better spatial selectivity. This makes surface-based information mapping a useful technique for a data-driven analysis of information representation in the cerebral cortex.

Integration of sensory and motor representations of single fingers in the human cerebellum.

The cerebellum is thought to play a key role in the integration of sensory and motor events. Little is known, however, about how sensory and motor maps in the cerebellum superimpose. In the present study we investigated the relationship between these two maps for the representation of single fingers. Participants made isometric key presses with individual fingers or received vibratory tactile stimulation to the fingertips while undergoing high-resolution functional magnetic resonance imaging (fMRI). Using multivariate analysis, we have demonstrated that the ipsilateral lobule V and VIII show patterns of activity that encode, within the same region, both which finger pressed and which finger was stimulated. The individual finger-specific activation patches are smaller than 3 mm and only show a weak somatotopic organization. To study the superposition of sensory and motor maps, we correlated the finger-specific patterns across the two conditions. In the neocortex, sensory stimulation of one digit led to activation of the same patches as force production by the same digit; in the cerebellum, these activation patches were organized in an uncorrelated manner. This suggests that, in the cerebellum, a movement of a particular finger is paired with a range of possible sensory outcomes. In summary, our results indicate a small and fractured representation of single digits in the cerebellum and suggest a fundamental difference in how the cerebellum and the neocortex integrate sensory and motor events.

Domain Adaptation-Based Deep Learning for Automated Tumor Cell (TC) Scoring and Survival Analysis on PD-L1 Stained Tissue Images.

We report the ability of two deep learning-based decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR = 0.539, p = 0.004 and second system: HR = 0.525, p = 0.003) compared to TC scoring by pathologists (HR = 0.574, p = 0.01).

Advances in functional imaging of the human cerebellum.

PURPOSE OF REVIEW: A quarter century of functional neuroimaging has provided a number of insights into the function of the human cerebellum. However, progress has been relatively slow, partly because cerebellar imaging poses a number of unique challenges for functional magnetic resonance imaging (fMRI). This review provides a guide to problems and recent solutions in the design, analysis and interpretation of neuroimaging studies of the human cerebellum. RECENT FINDINGS: One major problem in the interpretation of functional imaging studies is that it is still unclear what type of neural activity is reflected in the cerebellar blood-oxygenation-level-dependent signal. We summarize recent work that has provided partly contradictory insights. We then highlight some technical challenges, specifically the susceptibility to physiological artifacts, and recently developed techniques to account for them. Furthermore, the small size and functional heterogeneity of the cerebellum poses a challenge for normalization and atlas methods, which demands different analysis techniques than those used in the neocortex. Finally, we highlight some novel results assessing anatomical and functional connectivity with the neocortex. SUMMARY: Although these results clearly show the limitations of current approaches, they also show the potential of anatomical and functional MRI for the study of the human cerebellum.

Automated image analysis of NSCLC biopsies to predict response to anti-PD-L1 therapy.

BACKGROUND: Immune checkpoint therapies (ICTs) targeting the programmed cell death-1 (PD1)/programmed cell death ligand-1 (PD-L1) pathway have improved outcomes for patients with non-small cell lung cancer (NSCLC), particularly those with high PD-L1 expression. However, the predictive value of manual PD-L1 scoring is imperfect and alternative measures are needed. We report an automated image analysis solution to determine the predictive and prognostic values of the product of PD-L1+ cell and CD8+ tumor infiltrating lymphocyte (TIL) densities (CD8xPD-L1 signature) in baseline tumor biopsies. METHODS: Archival or fresh tumor biopsies were analyzed for PD-L1 and CD8 expression by immunohistochemistry. Samples were collected from 163 patients in Study 1108/NCT01693562, a Phase 1/2 trial to evaluate durvalumab across multiple tumor types, including NSCLC, and a separate cohort of 199 non-ICT- patients. Digital images were automatically scored for PD-L1+ and CD8+ cell densities using customized algorithms applied with Developer XD™ 2.7 software. RESULTS: For patients who received durvalumab, median overall survival (OS) was 21.0 months for CD8xPD-L1 signature-positive patients and 7.8 months for signature-negative patients (p = 0.00002). The CD8xPD-L1 signature provided greater stratification of OS than high densities of CD8+ cells, high densities of PD-L1+ cells, or manually assessed tumor cell PD-L1 expression ≥25%. The CD8xPD-L1 signature did not stratify OS in non-ICT patients, although a high density of CD8+ cells was associated with higher median OS (high: 67 months; low: 39.5 months, p = 0.0009) in this group. CONCLUSIONS: An automated CD8xPD-L1 signature may help to identify NSCLC patients with improved response to durvalumab therapy. Our data also support the prognostic value of CD8+ TILS in NSCLC patients who do not receive ICT. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01693562 . Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure).

Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis.

BACKGROUND: Immuno-oncology and cancer immunotherapies are areas of intense research. The numbers and locations of CD8+ tumor-infiltrating lymphocytes (TILs) are important measures of the immune response to cancer with prognostic, pharmacodynamic, and predictive potential. We describe the development, validation, and application of advanced image analysis methods to characterize multiple immunohistochemistry-derived CD8 parameters in clinical and nonclinical tumor tissues. METHODS: Commercial resection tumors from nine cancer types, and paired screening/on-drug biopsies of non-small-cell lung carcinoma (NSCLC) patients enrolled in a phase 1/2 clinical trial investigating the PD-L1 antibody therapy durvalumab (NCT01693562), were immunostained for CD8. Additional NCT01693562 samples were immunostained with a CD8/PD-L1 dual immunohistochemistry assay. Whole-slide scanning was performed, tumor regions were annotated by a pathologist, and images were analyzed with customized algorithms using Definiens Developer XD software. Validation of image analysis data used cell-by-cell comparison to pathologist scoring across a range of CD8+ TIL densities of all nine cancers, relying primarily on 95% confidence in having at least moderate agreement regarding Lin concordance correlation coefficient (CCC = 0.88-0.99, CCC_lower = 0.65-0.96). RESULTS: We found substantial variability in CD8+ TILs between individual patients and across the nine types of human cancer. Diffuse large B-cell lymphoma had several-fold more CD8+ TILs than some other cancers. TIL densities were significantly higher in the invasive margin versus tumor center for carcinomas of head and neck, kidney and pancreas, and NSCLC; the reverse was true only for prostate cancer. In paired patient biopsies, there were significantly increased CD8+ TILs 6 weeks after onset of durvalumab therapy (mean of 365 cells/mm2 over baseline; P = 0.009), consistent with immune activation. Image analysis accurately enumerated CD8+ TILs in PD-L1+ regions of lung tumors using the dual assay and also measured elongate CD8+ lymphocytes which constituted a fraction of overall TILs. CONCLUSIONS: Validated image analysis accurately enumerates CD8+ TILs, permitting comparisons of CD8 parameters among tumor regions, individual patients, and cancer types. It also enables the more complex digital solutions needed to better understand cancer immunity, like analysis of multiplex immunohistochemistry and spatial evaluation of the various components comprising the tumor microenvironment. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT01693562 . Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure).

Skill learning strengthens cortical representations of motor sequences.

Motor-skill learning can be accompanied by both increases and decreases in brain activity. Increases may indicate neural recruitment, while decreases may imply that a region became unimportant or developed a more efficient representation of the skill. These overlapping mechanisms make interpreting learning-related changes of spatially averaged activity difficult. Here we show that motor-skill acquisition is associated with the emergence of highly distinguishable activity patterns for trained movement sequences, in the absence of average activity increases. During functional magnetic resonance imaging, participants produced either four trained or four untrained finger sequences. Using multivariate pattern analysis, both untrained and trained sequences could be discriminated in primary and secondary motor areas. However, trained sequences were classified more reliably, especially in the supplementary motor area. Our results indicate skill learning leads to the development of specialized neuronal circuits, which allow the execution of fast and accurate sequential movements without average increases in brain activity. DOI:http://dx.doi.org/10.7554/eLife.00801.001.