The Growing Self Organizing Map (GSOM) unsupervised machine learning algorithm was used to generate a topology-preserving two-dimensional mapping of the pooled data and then separate it into clusters. Signature profiles of somatosensory impairment across two modalities  and three modalities  were characterized for both hands. Distinct impairment subgroups were identified. The influence of background and clinical variables was also modelled. The study provided evidence of the utility of unsupervised cluster analysis that can profile stroke survivor signatures of somatosensory impairment, which may inform improved diagnosis and characterization of impairment patterns.Altered somatosensory function is common among stroke survivors, yet is often poorly characterized. Methods of profiling somatosensation that illustrate the variability in impairment within and across different modalities remain limited. We aimed to characterize post-stroke somatosensation profiles (“fingerprints”) of the upper limb using an unsupervised machine learning cluster analysis to capture hidden relationships between measures of touch, proprioception, and haptic object recognition. Raw data were pooled from six studies where multiple quantitative measures of upper limb somatosensation were collected from stroke survivors ( Altered somatosensation is common after stroke ,2,3,4. IOur ability to perceive, recognize, and correctly manipulate objects requires a complex integration of multiple modalities of sensation, including touch, proprioception, and haptic object recognition. However, currently, our ability to profile somatosensory function across multiple modalities, types of information processing, and severity of impairment is limited. When analysing the influences of somatosensation on outcome and functional abilities after stroke, items indexing sensation are often hidden in a global measure of impairment such as the NIH Stroke Scale , combineWe propose the creation of somatosensory profiles that capture how impairments in different modalities of somatosensation can co-occur within an individual and whether the observed pattern of impairment is potentially representative of a phenotype, signature, or “fingerprint” of somatosensory impairment within a population. Generating profiles has relevance not only in better understanding patterns of somatosensory impairment within and across modalities, but also their impact on function, as different signatures of impairment could differentially impact dexterity and co-ordination of the upper limb, arm use, and/or functional activities. To allow us to characterize the potential patterns or relationships across multiple modalities of somatosensation in a sample of stroke survivors, we applied an unsupervised machine learning approach  to charaThe co-existence and integration of somatosensory information is complex, involving the simultaneous detection and weighting of relevant stimuli, as well as gating of irrelevant stimuli so that meaningful information can be extracted to inform different cognitive processes, actions, and functional tasks. For example, discriminating where our limbs are in space relative to each other via our proprioceptive senses is essential for the execution of coordinated goal-directed movement ,24. FurtThe relationship between touch and proprioception is made more complex in the context of stroke, where severity of impairment can differ across modalities, and potentially influence each other. These relationships are difficult to unpack for several reasons. First, in clinical research, the specific nature and severity of modalities are often not detailed on an individual level. If multiple modalities of impairment are reported, it is often presented as group averages which fail to capture more nuanced patterns of variability across individuals e.g., ,3,4). Se,4. Se3,4Machine learning-based analysis approaches provide an opportunity to explore relationships between variables beyond standard statistical approaches, and to summarize profiles of somatosensory impairment/capacity. Using unsupervised machine learning algorithms, we can use a data-driven approach to reveal previously unseen patterns of relationships in multivariate data sets ,30. The Our aim was to generate profiles of somatosensory impairment across three different modalities of upper limb sensation: touch, proprioception, and haptic object recognition. Using unsupervised learning methods and quantitative measures of somatosensation within individuals, we sought to systematically identify within a population of individuals whether scores on a set of outcomes map to distinct phenotypes of somatosensory impairment and capacity. This was achieved using pooled raw data from studies of individuals with stroke with known somatosensory impairment. We included assessment scores from both limbs to capture impairments that may also be present in the ipsilesional hand ,33. HandData were pooled from six studies where survivors of stroke were tested using several quantitative measures of somatosensation. Most were intervention studies where the effectiveness of sensory discrimination training for the recovery of somatosensation in the upper limb was investigated. The studies were: (1) Discriminative validity study ,21; (2) To assess for relationships across multiple modalities of somatosensation on an individual subject level, only studies that included at least two of the following quantitative, standardized assessments of upper limb somatosensation were eligible for inclusion: TDT (Tactile Discrimination Test (TDT ), Wrist Sample size: In cluster analysis, the goal is typically to group similar data points into clusters based on a similarity measure or distance metric. We used GSOM to generate a topology-preserving mapping of the data, which was then separated into clusters using K-means. It is an unsupervised learning technique and does not involve hypothesis testing or the need to estimate effect sizes. Therefore, power calculation is not a standard requirement when performing cluster analysis for recognizing patterns . NeverthParticipant inclusion and exclusion criteria and testing conditions were similar across all studies. Participants were recruited from hospitals, rehabilitation centres, and the community, and spanned subacute to chronic phases of recovery. Most were recruited from metropolitan sites in Melbourne, Victoria, Australia. Common inclusion criteria were participants with ischaemic or haemorrhagic stroke, all participants were medically stable, had adequate comprehension of simple instructions, and were able to provide informed consent. Participants presented with impaired touch discrimination, limb position sense, and/or tactile object recognition identified clinically and by standardized tests, with the exception of those from the Discriminative Validity ,21 and NThe TDT is a quantitative assessment of the ability to discriminate precisely defined surface textures (grids) through the sense of touch, using one’s preferred finger tip (either index or middle), with vision occluded . It is aThe WPST is a quantitative assessment of an individual’s perceived position of their wrist in the absence of vision . The exaThe fTORT is a 14-item quantitative assessment of an individual’s ability to recognize everyday objects through touch. Detailed procedures are presented in Carey et al. . In brien = 203) to investigate the binary relationship between these two modalities in a larger sample. The second included participants with complete TDT, WPST, and fTORT data sets for both hands (n = 141) to further profile how touch, proprioception, and haptic object recognition cluster. The selected sample size for each analysis varied according to completeness of the available data for the variables to be analysed.The total pooled sample included in the study consisted of 207 participants with stroke and somatosensory impairment. The dataset consisted of three discrete measures  × 2 limbs  for each participant, which were collected at a single time point. In addition, some categorical variables related to key demographic and background clinical information were available .Data pre-processing is an essential step in data analysis since it involves cleaning, transforming, and preparing the data before it is used to train a machine learning model. The goal of data pre-processing is to make the data usable for the clustering algorithm by removing any irrelevant or redundant information, handling missing values, and ensuring that the data is in a suitable format for the chosen algorithm.Since neither data imputation nor data interpolation yields correct participant clinical scores, we therefore deleted data rows with missing values for clinical scores considered for specific analysis . TreatinThe pre-processed dataset had clinical scores of different scales. For example, TDT scores range from 0 to 100, WPST’s range is 0–40, and fTORT scores range from 0 to 42 see . Since mFeature engineering of categorical features involved the transformation into a suitable format for the machine learning algorithm. Categorical variables such as background clinical information  were transformed using one-hot encoding . One-hotA self-organizing map (SOM) is a type of artificial neural network (ANN) used for unsupervised learning-based clustering. A SOM projects high-dimensional input data onto a lower-dimensional map, where similar input patterns are clustered together. The map is self-organizing in that it adapts to represent the structure of the input data over time. The GSOM  is an exGSOM was selected as the clustering method for this study since GSOM does not require a pre-defined network architecture which is difficult to identify in an exploratory study such as this study. The GSOM is also flexible and adaptable for small datasets where the data distribution may need to be better defined. This enables unbiased exploration of data patterns and does not require pre-defined labels.In this study, we used GSOM’s spread factor (SF) hyperparameter value 0.9. The spread factor parameter, which can take values between 0 and 1, controls the node growth in the GSOM allowing the generation of maps with small or large number of nodes. Higher spread factors (larger maps) lead to uncovering more detailed data patterns (including non-prominent sub groupings), while lower spread factors  yield high-level data patterns (only the most prominent groupings).The GSOM results in a two-dimensional grid of nodes, where each node represents a cluster of similar data patterns. The nodes spatially close to each other on the map are thought to represent clusters. Hence, we could even examine the map and identify areas where the nodes are densely packed, as these areas typically represent clusters of similar patterns.t-tests. Furthermore, two-sample t-tests were used to confirm the distinctiveness of the clusters statistically.However, in this study, we used another algorithm (K-means)  to derivA summary of participant demographic data and background clinical information is reported in The following sections describe GSOM-based clustering to generate a profile of somatosensory impairment across different modalities of upper limb sensation: touch, proprioception, and haptic object recognition. Cluster generation was carried out to help answer the following main questions: (a) How do survivors of stroke cluster according to TDT and WPST scores for the contralesional hand, ipsilesional hand, and both hands? (b) How do survivors of stroke cluster according to TDT, WPST, and fTORT scores for the contralesional hand, ipsilesional hand, and both? (c) Does the hand affected and hand dominance impact the clusters? We examined how survivors of stroke cluster according to TDT and WPST scores for contralesional hand, ipsilesional hand, and both hands. The following figures display the result profiles from the clustering experiments across two modalities: TDT and WPST for contralesional hand ; ipsilesn = 204). Altered somatosensation was profiled across TDT and WPST for the contralesional hand and three profiles of individual level data patterns were observed  presented with no to mild touch impairment and relatively mild to no proprioception impairment for the contralesional hand. Participants of Profile 2 (49%) exhibited relatively severe touch impairment and no to mild proprioception impairment for the contralesional hand. Profile 3 (26%) had participants with moderate to extreme impairment in both touch and proprioception. n = 203). Somatosensory impairment was profiled across TDT and WPST for the ipsilesional hand, and three profiles having weak cluster structure were observed at individual-level data patterns  typically ranged from mild to moderate impairment in both touch and proprioception. Profile 2 participants (40%) mostly had no impairment in both modalities. Profile 3 (35%) exhibited no proprioception impairment mostly for the ipsilesional hand, while touch sensation varied from no to mild impairment levels. n = 203). Somatosensory impairment was profiled across TDT and WPST for both contralesional and ipsilesional hands and three profiles having weak cluster structure were observed at individual level data patterns (Profile 1 consisted of participants (40%) with no to mild touch impairment (high variance) and no to mild proprioception impairment in the contralesional hand. Their ipsilesional hand exhibited no to mild touch and proprioception impairment. Profile 2 (26%) ranged from moderate to severe touch impairment (high variance) and no to mild proprioception impairment for the contralesional hand, with mild impairment in touch for the ipsilesional hand. Profile 3 (34%) included participants with a range of moderate to severe contralesional impairment for touch and proprioception (high variance) and no to mild impairment in ipsilesional hand in both touch and proprioception.As a further step, we examined how somatosensory impairment based on the TDT, WPST, and fTORT is grouped into clusters, for contralesional, ipsilesional, and both hands, and how the two variables “handedness” and “is the affected hand the dominant hand” impact the clusters. The following figures display the result profiles from the clustering experiments across three modalities: TDT, WPST, and fTORT for contralesional hand ; and ipsn = 144). These participants were from the SENSe, In Touch, CoNNECT, and SENSe CONNECT studies, all sensory intervention studies. Three profiles were observed through the cluster analysis. After deleting records with missing values, 144 participants were included in the experiment , no to mild proprioception, and mild to moderate haptic object recognition impairment (high variance). Profile 3 consisted of 25% of participants, showing relatively moderate impairment across all modalities. n = 141) and observed four major profiles. We profiled somatosensory impairment across TDT, WPST, and fTORT for ipsilesional hand  showed mild touch impairment in the ipsilesional hand, but no proprioception and/or haptic object recognition impairment. Profile 2 (21%) had varying levels of mild touch impairment, but no proprioception impairment, with a range from no to mild haptic object recognition impairment. Profile 3 (27%) showed no impairment in touch, proprioception or haptic object recognition for the ipsilesional hand. Profile 4 (29%) had no to mild touch and haptic object recognition impairment, with varying levels from no to moderate proprioception impairment.Furthermore, we examined how somatosensory impairment based on the TDT, WPST, and fTORT is grouped into clusters, for both contralesional and ipsilesional hands, and how the two variables “handedness” and “is the affected hand the dominant hand” impact the clusters.The following figures display the result profiles from the clustering experiments across three modalities for both hands ; and when = 141). Four major profiles were identified. The summary of profiles is described in Somatosensory impairment was profiled across TDT, WPST, and fTORT for both contralesional and ipsilesional hands as the next step for participants with complete data sets for both hands (Profile 1 consisted of participants (25%) with no to mild impairment in all three modalities for the contralesional hand. Their ipsilesional hand showed no to mild impaired touch impairment, with no proprioception or haptic object recognition impairment. Profile 2 (26%) ranged from moderate to severe impairment in touch, proprioception, and haptic object recognition for the contralesional hand, with varying levels of no to mild touch impairment (high variance), and no impairment in proprioception or haptic object recognition for the ipsilesional hand. Profile 3 (20%) included participants with a range of moderate contralesional impairment (high variance) and no to mild impaired ipsilesional hands in all three modalities. Participants of Profile 4 (29%) had mild to severe impaired touch, with varying levels from no to moderate impaired proprioception and haptic object recognition for the contralesional hand. Their ipsilesional hand showed no to mild impairment across modalities. We included the following parameters in the above analysis of somatosensory assessment data from both hands: (i) dominant hand (DH), (ii) is dominant hand the affected hand (DAH). Three clusters emerged . ProfileProfile 1 (36%) comprised participants having mild to moderate impairment across the three modalities for the contralesional hand, and no/mild impairment for the ipsilesional hand for touch only. Ninety percent were right hand dominant, with 32% dominant hand affected. Profile 2 (38%) showed moderate contralesional impairment across modalities for the contralesional hand, with high variability. Again, the ipsilesional hand showed no/mild impairment for touch only. Ninety percent were right hand dominant, with 100% having the dominant hand affected. Participants of Profile 3 (26%) had severe impairment across all modalities for the contralesional hand, again with no/mild impairment for touch only in the ipsilesional hand. All were right hand dominant with only 8% dominant hand affected.Signature profiles of somatosensory impairment across modalities of touch discrimination, proprioception, and haptic object recognition were characterized for the contralesional hand and ipsilesional hand of survivors of stroke. Distinct profiles were characterized according to somatosensory modality, relative presence/severity of impairment, and spread/variability of scores. Two-modality (TDT and WPST) and three-modality  analyses, plus the addition of background clinical information, uncovered distinct profiles for contralesional, ipsilesional, and for combined hands. Three to four distinct profiles (clusters) were identified in each sub-analysis, with relatively similar frequencies of survivors belonging within a profile i.e., ranging from 25–49% per profile (when three profiles emerged), and 20–29% (for four emergent profiles).Characterization of somatosensory signatures using unsupervised learning-based cluster analysis has provided new insights into profiles of clinical impairment of sensation and residual capacity, after stroke. Clustering according to two somatosensory modalities (touch discrimination and wrist position sense) highlighted a separation in clusters (profiles) according to presence of impairment in one   emerged based primarily on severity of impairment for this sample who were recruited to sensory intervention trials. Profiles for the contralesional hand were characterized as severe impairment (Profile 1), mild impairment (Profile 2) or moderate impairment (Profile 3) across all modalities. Those with severe impairment in one modality tended to be severely impaired in others. Again, relatively mild ipsilesional hand impairment was evident for three of the four profiles for touch (Profiles 1 and 2), haptic object recognition (Profile 2) or proprioception (Profile 4). When both hands were considered, impairment ranged from no to severe impairment for the contralesional hand, with little or no ipsilesional hand impairment. Although not tested here, the severity-related profiling across modalities is likely impacted by injury to common neural pathways known to be part of a distributed network of brain regions that is responsible for processing somatosensory information ,49. TogeA feature of analyses of the three somatosensory modalities for the contralesional hand was that haptic object recognition impairment, measured with the fTORT, was consistently a distinguishing feature between profiles. Further, while there was evidence of distinction in presence and severity of impairment for the two modality analyses, the severity of impairment was more consistent across modalities for the three modality analyses when haptic object recognition was included. Haptic object recognition is dependent on the ability to integrate both touch and proprioceptive information and relies on cognitive resources such as working memory and attention to form a percept of the object informed by shape and texture . UnderpiWe also observed how accounting for hand dominance and whether the more affected hand was the dominant hand changed subgroups of impairment. With the addition of these two parameters, the four profiles   to captuOur current study has mapped distinct profiles of somatosensory impairment across multiple modalities at a single timepoint. While this provides new insights into how impairments co-occur for different clusters of individuals, it does not provide us with a robust indicator of recovery over time. Characterizing impairment profiles at an individual level opens the potential to better characterize sensory recovery trajectories, which is currently limited. In a small sample of nine acute stroke participants, Winward and colleagues  found thFuture work should map how clusters of impairment may change longitudinally along key timepoints of the stroke recovery continuum  and afteThis approach does not need to be restricted to profiling of somatosensory impairment. A strength of unsupervised clustering techniques is the capacity to explore potentially meaningful relationships with large multivariate data sets. With respect to stroke recovery, many large data repositories including national stroke registries have been developed to facilitate meta-analyses of large pooled samples of data to advance scientific discovery and stroke care ,63. StroThe somatosensory impairment profiles characterized were based on quantitative measures of somatosensory discrimination across tactile (TDT) and proprioceptive (WPST) modalities, and haptic object recognition. As such, the profiles that emerged were linked to these measures of impairment and to the upper limb. They were also defined in stroke populations that span subacute to rehabilitation and chronic phases, providing limits to the generalizability of our findings. Nevertheless, such impairment is common in these populations ,2,3,4,5,Our current study focused on somatosensory variables and creating a sensory signature with a relatively restricted number of variables. The GSOM methodology is capable of handling large volumes of data , but thiTo our knowledge this is the first study to apply an unsupervised machine learning approach to profile somatosensory impairments post-stroke. Signature profiles were created using data from touch, proprioception, and/or haptic object recognition modalities; contralesional and/or ipsilesional hands; and hand dominance. Through the inclusion of quantitative assessments for both the affected  and less-affected  hand of stroke survivors, we identified distinct profiles (or “fingerprints”) of somatosensation that differed with respect to patterns of impairment presence and severity. A noteworthy finding is that individuals with severe impairment across multiple modalities in the affected hand can be further divided into two distinct subgroups with and without impairment in the less-affected  hand. These two subtypes of severe impairment may have different challenges with functional tasks which could be an important consideration for tailoring rehabilitation therapies. The feasibility and value of using unsupervised learning-based cluster analysis to profile signatures of somatosensory impairment after stroke was demonstrated and may inform improved characterization of both somatosensory impairment and capacity for targeted rehabilitation and recovery.