Structural and Semantic Speech Graph Analysis of Dream Reports in Congenitally and Late Blind Individuals

Structural and Semantic Speech Graph Analysis of Dream Reports in Congenitally and Late Blind Individuals | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Structural and Semantic Speech Graph Analysis of Dream Reports in Congenitally and Late Blind Individuals Kausar Raheel, Nazanin Biabani, Olga Ivanenko, Qi Rui See, Rita Bertani, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6761771/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Visual input is thought to influence spatial cognition and language. While blind individuals often rely on egocentric spatial representations, it remains unclear whether and how visual deprivation affects dream-related language. This study applied speech graph analysis (SGA) to investigate linguistic differences in dream reports from congenitally blind (CB), late blind (LB), and sighted control (SC) individuals. Methods: We retrospectively analyzed 333 dream reports from an open-access database (DreamBank): 118 from CB, 75 from LB, and 140 from SC individuals. Graph-theoretical metrics of structural and semantic speech organization were extracted using validated NLP and SGA pipelines, including recurrence (L2/L3), connectivity (LSC), and lexical diversity (nodes). Results: Compared to SC, both CB and LB groups showed significantly reduced lexical diversity and increased long-range recurrence (LSC), suggesting greater linguistic cohesiveness. LB reports showed a specific increase in short-range recurrence cycles (L2, L3), not observed in CB. Spectral analysis supported these group differences, indicating altered graph-wide connectivity properties in blind groups. Conclusions: Blind individuals demonstrate distinct structural and semantic features in dream-related language, consistent with more egocentric narrative construction. These findings support a potential role of sensory experience in shaping cognitive-linguistic encoding. Further prospective studies are needed to explore underlying neural mechanisms and developmental trajectories. Biological sciences/Neuroscience Health sciences/Neurology Blindness Dream Recall Speech Graph Analysis Cognitive Mapping Natural Language Processing Figures Figure 1 Figure 2 Figure 3 1 Introduction The structure of human thought is profoundly shaped by the sensory modalities through which we experience the world 1 – 4 . Of these, vision is arguably the most dominant, providing rich spatial, temporal, and contextual cues that scaffold cognitive development from early life onward. It has long been theorized that the loss or absence of vision prompts the brain to reorganize its functional architecture, with downstream effects on language, memory, and spatial cognition 1 , 2 , 5 – 8 . Yet, the extent to which this sensory reconfiguration penetrates the generative processes of internal experiences, such as dreams, remains largely unexplored. Dreams represent a unique window into endogenous cognition: spontaneous, internally driven simulations that integrate perceptual, emotional, and autobiographical elements 9 . Unlike externally constrained waking cognition, dreams are self-generated and decoupled from environmental inputs, offering an ecologically valid opportunity to probe the architecture of mental representation in the absence of external sensory cues 9 – 11 . For individuals with blindness, particularly those blind from birth, dream content may offer a useful perspective on how non-visual modalities contribute to experiential encoding and the organisation of narrative. However, most previous work on the dreams of blind individuals has focused on phenomenology, or content analysis, rather than the underlying cognitive-linguistic structure through which these dreams are expressed 12 – 18 . Blindness has been associated with distinct patterns of spatial representation 19 . Research in both behavioural and neuroimaging domains suggests that blind individuals rely more heavily on egocentric reference frames, those anchored to the self, compared to allocentric reference frames, which are centered on external landmarks or geometric layouts. For instance, congenitally blind individuals describe spatial environments more sequentially and emphasize the path of movement rather than landmarks or environmental layout 1 . This egocentric dominance is thought to result from compensatory recruitment of non-visual modalities, such as proprioception and audition, and the repurposing of visual cortical regions for non-visual processing 20 . At the neural level, these differences in spatial cognition have been linked to distinct activation patterns in parietal and medial temporal regions 2 . Notably, recent work has shown altered entorhinal grid-cell coding geometry in congenitally blind individuals, with reduced evidence for the characteristic hexagonal symmetry typically associated with allocentric cognitive maps. Instead, a 90° symmetry pattern has been observed, potentially reflecting a shift towards more discrete, self-referenced spatial coding 2 . These changes correlate with enhanced activation in the parietal cortex during navigation tasks, suggesting a neuroplastic reweighting of spatial computation in the absence of visual input 2 . Despite this growing body of evidence, little is known about how such spatial cognitive adaptations affect language, particularly the structure of spontaneous language, as might emerge in the context of dream recall. Language is not merely a communication tool but a representational system that encodes and reflects underlying cognitive processes 3 . In this regard, dreams may offer a valuable platform for examining spontaneous narrative organization in a controlled yet naturalistic setting. Dreams are internally structured yet free from immediate social or sensory constraints, and thus, may provide an ideal context for studying how the brain generates and sequences complex experiences in the absence of visual scaffolding 11 . Recent advances in computational linguistics and graph theory have enabled a new class of analytic tools that can quantify the structural and semantic properties of language with high granularity. Speech graph analysis (SGA) is one such method, in which linguistic elements, typically words or lemmas, are modeled as nodes, and their sequential or semantic relationships as edges 21 . This framework enables the quantification of features such as lexical diversity (e.g. number of unique nodes), recurrence (e.g. cycles of two or three nodes), and global cohesion (e.g. clustering coefficients, strongly connected components) 22 . SGA has been validated as a sensitive tool in psychiatric research, distinguishing between typical and disordered thought in conditions such as schizophrenia 23 and Alzheimer’s disease 24 . In the context of dream reports, SGA provides a language-invariant and objective measure of how experiences are mentally encoded and subsequently narrated 25 , 26 . Preliminary evidence has shown that dream speech organization can reflect underlying neurophysiological states, including sleep stage, cognitive maturity, and psychopathology 26 . However, to our knowledge, no prior study has applied these methods to examine how blindness, whether congenital or acquired, affects the structure of dream language. Doing so offers a unique opportunity to investigate how altered perceptual scaffolding may impact spontaneous cognitive and linguistic expression. In the present exploratory study, we set out to address this gap by applying both structural and semantic speech graph analysis to a large corpus of dream reports obtained from congenitally blind, late blind, and sighted individuals. Our primary aim was to test whether blindness is associated with specific alterations in the linguistic topology of dreams, and whether these alterations differ between those with congenital and late-onset visual deprivation. By analyzing both word-level transitions and deeper semantic dependencies, we sought to construct a multidimensional linguistic profile of dream narratives across sensory backgrounds. We hypothesized that dream reports from blind individuals would show reduced lexical diversity and increased internal cohesion, consistent with the hypothesized dominance of egocentric spatial and narrative frameworks. We further anticipated that these features would be more pronounced in the congenitally blind, who lack any visual experience and therefore rely exclusively on non-visual modalities throughout development. Additionally, we explored whether the presence of short-range recurrence patterns, previously associated with cognitive immaturity and neurodevelopmental conditions 26 , would differentiate between late blind and congenitally blind participants, potentially indexing differences in neuroplastic adaptation. Our aim was to shed light on the intersection between sensory experience, spatial cognition, and linguistic representation, using dreams as a model system for endogenous cognitive architecture. 2. Methods and Materials This retrospective, cross-sectional study investigated the structural and semantic organization of dream-related language in individuals with differing visual experiences. The central aim was to determine whether congenital or acquired blindness is associated with measurable alterations in the linguistic structure of dream narratives, and whether these differences reflect underlying cognitive adaptations. To address this, we applied a multi-stage computational pipeline comprising natural language processing (NLP), speech graph analysis (SGA), and spectral graph theory to a carefully curated set of dream reports. The dataset was compiled from the DreamBank 27 corpus ( https://www.dreambank.net/ ), a large, open-access repository of anonymized dream narratives obtained under informed consent. Four dream series were selected for analysis: two comprising blind participants (both congenitally and late blind), and two consisting of age- and sex-matched sighted controls. Specifically, the blind cohort was drawn from the “Blind Dreamers (Female)” and “Blind Dreamers (Male)” series, while the control cohort was selected from the “Hall/VdC Norms: Female” and “Hall/VdC Norms: Male” series 25 , 28 . Inclusion criteria required that dream reports contain between 50 and 300 words, in order to standardize narrative length and avoid excessive sparsity or verbosity in graph representation. Dreams from individuals with partial vision or light perception were excluded from the congenitally blind group 13 . In total, 333 dream reports were included: 118 from congenitally blind individuals, 75 from late blind individuals, and 140 from sighted controls. Supplementary Tables 1 and 2 provide demographic and sample distribution details. Each dream report underwent minimal preprocessing, limited to correction of typographic inconsistencies, and was then subjected to two parallel analytic streams: one based on structural word adjacency and the other on semantic dependencies. For structural analysis, each report was treated as a directed sequence of words, with individual words constituting nodes and adjacent word-pairs forming directed edges. Speech graphs were generated using the publicly available SpeechGraphs Java software ( http://neuro.ufrn.br/softwares/speechgraphs ), which calculates a suite of 14 topological attributes 21 . These attributes capture general properties (e.g. number of nodes and edges), measures of graph connectivity (e.g. largest connected component, largest strongly connected component, average total degree [ATD]), recurrence patterns (e.g. repeated edges [RE], parallel edges [PE], and short loops of one, two, or three nodes [L1, L2 or L3]), and global graph topology (e.g. density, clustering coefficient, average shortest path length, and graph diameter). All graph metrics were normalized by the number of nodes to control for narrative length (see Table 1 ). A full list of speech graph attributes and definitions is provided in Table 2 . Table 2 Normalised descriptive statistics for the SGA of structural and semantic graphs (corrected per number of nodes). Structural Graphs Semantic Graphs Sighted Controls (n = 140) SGA Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Edges 1.505 0.016 1.500 1.364 1.618 1.475 1.536 1.693 0.020 1.683 1.528 1.838 1.653 1.733 RE 0.100 0.005 0.088 0.055 0.139 0.090 0.111 0.082 0.006 0.067 0.040 0.108 0.071 0.094 PE 0.112 0.006 0.104 0.065 0.153 0.101 0.123 0.087 0.006 0.069 0.043 0.117 0.076 0.099 L1 0.001 0.000 0.000 0.000 0.000 0.000 0.001 0.005 0.001 0.000 0.000 0.009 0.003 0.007 L2 0.012 0.001 0.012 0.000 0.019 0.010 0.014 0.008 0.001 0.000 0.000 0.014 0.006 0.010 L3 0.038 0.003 0.035 0.016 0.055 0.033 0.043 0.008 0.001 0.000 0.000 0.013 0.006 0.011 LCC 1.000 0.000 1.000 1.000 1.000 0.999 1.000 0.993 0.001 1.000 0.987 1.000 0.992 0.995 LSC 0.789 0.009 0.808 0.750 0.863 0.771 0.806 0.042 0.004 0.027 0.016 0.050 0.036 0.049 ATD 0.041 0.001 0.039 0.034 0.046 0.040 0.044 0.050 0.001 0.048 0.042 0.055 0.048 0.053 Density 0.001 0.000 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.001 0.001 Diam (LCC) 0.146 0.005 0.134 0.102 0.172 0.136 0.155 0.115 0.003 0.112 0.092 0.137 0.110 0.121 ASP (LCC) 0.058 0.002 0.055 0.043 0.069 0.055 0.061 0.054 0.001 0.052 0.043 0.064 0.051 0.056 CC 0.001 0.000 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.001 0.001 Late Blind (n = 75) SGA Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Edges 1.626 0.027 1.586 1.497 1.722 1.575 1.679 1.894 0.037 1.857 1.690 2.045 1.823 1.966 RE 0.154 0.011 0.146 0.077 0.209 0.133 0.177 0.135 0.009 0.130 0.074 0.197 0.116 0.153 PE 0.172 0.012 0.162 0.097 0.215 0.150 0.195 0.153 0.010 0.146 0.090 0.214 0.134 0.172 L1 0.001 0.001 0.000 0.000 0.000 0.000 0.003 0.014 0.002 0.014 0.000 0.021 0.010 0.018 L2 0.018 0.002 0.016 0.009 0.027 0.015 0.021 0.035 0.005 0.020 0.005 0.053 0.026 0.044 L3 0.051 0.004 0.044 0.027 0.072 0.044 0.058 0.062 0.014 0.022 0.000 0.086 0.039 0.092 LCC 0.995 0.003 1.000 1.000 1.000 0.989 1.000 0.987 0.003 0.992 0.983 1.000 0.981 0.991 LSC 0.867 0.012 0.897 0.838 0.932 0.842 0.890 0.107 0.010 0.088 0.047 0.152 0.089 0.126 ATD 0.048 0.002 0.046 0.039 0.054 0.045 0.051 0.061 0.002 0.059 0.052 0.067 0.058 0.064 Density 0.001 0.000 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.001 0.001 Diam (LCC) 0.143 0.007 0.135 0.101 0.163 0.130 0.157 0.117 0.004 0.114 0.089 0.134 0.109 0.125 ASP (LCC) 0.060 0.003 0.057 0.044 0.070 0.055 0.066 0.056 0.002 0.054 0.042 0.066 0.052 0.060 CC 0.001 0.000 0.001 0.001 0.001 0.001 0.001 0.002 0.000 0.002 0.001 0.002 0.002 0.002 Congenitally Blind (n = 118) SGA Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Mean SE Mdn Q1 Q3 95% BCI lower 95% BCI upper Edges 1.642 0.022 1.621 1.443 1.828 1.600 1.686 1.954 0.032 1.882 1.682 2.222 1.890 2.017 RE 0.158 0.009 0.141 0.083 0.225 0.140 0.176 0.148 0.008 0.134 0.083 0.219 0.132 0.163 PE 0.173 0.010 0.159 0.094 0.242 0.155 0.193 0.161 0.008 0.147 0.091 0.227 0.145 0.178 L1 0.001 0.000 0.000 0.000 0.000 0.000 0.002 0.014 0.001 0.013 0.000 0.022 0.012 0.017 L2 0.016 0.001 0.015 0.000 0.023 0.013 0.018 0.030 0.003 0.022 0.000 0.037 0.024 0.037 L3 0.044 0.003 0.038 0.020 0.060 0.038 0.051 0.047 0.006 0.023 0.000 0.063 0.036 0.059 LCC 0.998 0.001 1.000 1.000 1.000 0.995 1.000 0.989 0.002 0.996 0.982 1.000 0.986 0.991 LSC 0.879 0.009 0.902 0.855 0.936 0.860 0.896 0.109 0.008 0.090 0.039 0.167 0.095 0.124 ATD 0.047 0.001 0.046 0.038 0.055 0.045 0.049 0.061 0.001 0.058 0.053 0.070 0.059 0.064 Density 0.001 0.000 0.001 0.000 0.001 0.001 0.001 0.001 0.000 0.001 0.001 0.001 0.001 0.001 Diam (LCC) 0.141 0.006 0.128 0.099 0.173 0.130 0.153 0.115 0.004 0.110 0.088 0.136 0.109 0.122 ASP (LCC) 0.059 0.002 0.053 0.042 0.072 0.054 0.063 0.054 0.002 0.054 0.042 0.064 0.051 0.057 CC 0.001 0.000 0.001 0.001 0.001 0.001 0.001 0.002 0.000 0.002 0.001 0.002 0.002 0.002 Abbreviations : ATD ; Average total distance, ASP ; Average shortest path, CC ; Clustering Coefficient, Diam; diameter, LCC ; Largest Connected Component, LSC ; Largest strongly connected component, L1 ; Cycles of one node, L2 ; Cycles of two nodes, L3 ; Cycles of three nodes, Mdn; median, PE ; parallel edges, RE ; repeated edges, SD ; Standard Deviation, SE ; Standard Error, SGA ; Speech Graph Attributes, Q1 ; first quartile, Q3; third quartile, 95% BCI lower; 95% bootstrap confidence interval lower, 95% BCI upper; 95% bootstrap confidence interval upper. To further account for verbosity and analyze language at a finer granularity, a sliding window approach was employed 21 . Dream texts were segmented into overlapping windows of 30 words with a one-word step size, enabling localized assessments of graph properties across each narrative. This technique facilitated examination of microstructural variation in linguistic organization within and across participant groups. For semantic (or more correctly, syntactic) analysis, dream texts were processed using the Stanford CoreNLP ( https://stanfordnlp.github.io/CoreNLP/demo.html ) toolkit 29 , 30 . This included tokenization, part-of-speech tagging, and lemmatisation, followed by syntactic dependency parsing to identify relational structures between words. Lemmas, base forms of words 31 , were used as graph nodes, and syntactic dependencies served as directed edges. Custom Python scripts incorporating BeautifulSoup 32 , NumPy 33 , Pandas 34 , and NetworkX libraries were developed to transform the CoreNLP outputs into semantic graphs. These graphs were constructed as directed and unweighted, with non-linguistic elements (e.g. punctuation, root nodes) and ambiguous self-loops removed. Unlike the structural graphs, which were analyzed using a sliding window, semantic graphs were constructed from entire dream reports to preserve the integrity of syntactic dependency chains. To capture the holistic organization of linguistic networks, both structural and semantic graphs were further subjected to spectral graph analysis 35 . This approach uses the eigenvalues of the graph Laplacian matrix to characterize global connectivity and structural regularity 36 – 38 . Disconnected graphs were excluded from this analysis. For each graph, we computed the spectral radius (the largest eigenvalue), the spectral gap (the difference between the largest and second largest eigenvalues), algebraic connectivity (the second-smallest eigenvalue, also known as the Fiedler value), and the eigenratio (the ratio of the spectral radius to the Fiedler value). These metrics provide insight into graph-wide features such as cohesion, modularity, and synchronizability. Spectral analyses were performed using the NetworkX 39 and SciPy 40 libraries in Python, and all Laplacian matrices were normalized to minimize size-related artifacts. All statistical analyses were conducted using non-parametric methods, as the Shapiro-Wilk test indicated that many graph attributes did not conform to normal distributions. Group-level differences across the three conditions (congenitally blind, late blind, and sighted controls) were assessed using the Kruskal–Wallis test. Where significant main effects were observed, post hoc comparisons were performed using the Mann–Whitney U-test with Bonferroni correction for multiple comparisons. For metrics potentially influenced by word count, additional rank-based analyses of covariance were conducted, incorporating word count as a covariate. Effect sizes (η²) were calculated for all comparisons and interpreted using standard thresholds (0.01 = small, 0.06 = moderate, 0.14 = large). All statistical analyses were carried out in IBM SPSS Statistics (version 28.0.0) and Python (SciPy library) 40 . The study was conducted in accordance with institutional and international guidelines for secondary data analysis of anonymized data. All original data are openly accessible through DreamBank 27 . All data that support the findings of this study are available and open source at the DreamBank; the data analysed in this study were obtained from the open-access DreamBank repository ( https://www.dreambank.net/ ). The dataset consists of fully anonymised narratives collected with informed consent. As per UK regulations and institutional guidance ( www.kcl.ac.uk/research/support/ethics ), no additional ethical approval was required for this secondary analysis. 3. Results A total of 333 dream reports were analyzed: 118 from congenitally blind (CB), 75 from late blind (LB), and 140 from sighted controls (SC). Linguistic structure differed significantly between groups across both structural and semantic graph domains. Group-level statistical comparisons for structural and semantic graph features are summarized in Table 3 . Detailed group comparisons and descriptive statistics are presented in Supplementary Tables 3–19. Table 3 Three group comparison of normalised SGA of structural and semantic dream graphs. SGA Structural Graphs Semantic Graphs Total N DFH H η2 p H η2 p Edges 333 2 26.000 0.073 2.26E-06* 42.783 0.124 5.13E − 10 * RE 333 2 28.906 0.082 5.29E-07* 52.072 0.152 4.93E − 12 * PE 333 2 33.018 0.094 6.76E-08* 61.224 0.179 5.07E − 14 * L1 333 2 2.175 0.001 .337 39.358 0.113 2.84E − 09 * L2 333 2 10.545 0.026 .005* 59.740 0.175 1.07E − 13 * L3 333 2 8.199 0.019 .017* 53.544 0.156 2.36E − 12 * LCC 333 2 4.280 0.007 .118 6.944 0.015 0.031* LSC 333 2 76.366 0.225 2.61E-17* 81.887 0.242 1.65E − 18 * ATD 333 2 23.661 0.066 7.28E-06* 61.401 0.180 4.64E − 14 * Density 333 2 6.756 0.014 .034* 25.257 0.070 3.28E − 06 * Diam/ Diam-LCC 333 2 1.197 -0.002 .550 0.248 -0.005 0.883 ASP/ ASP-LCC 333 2 0.384 -0.005 .825 0.369 -0.005 0.832 CC 333 2 19.565 0.053 5.64E − 05 * 62.377 0.183 2.85E − 14 * 3.1 Structural Graph Analysis Across the entire dataset, CB and LB participants exhibited significantly reduced lexical diversity compared to SC, as reflected by a lower number of unique nodes per dream graph. The Kruskal–Wallis test revealed a significant group effect (H = 33.38, df = 2, p = 5.7 × 10⁻⁸, η² = 0.095). Post hoc comparisons confirmed lower node counts in both CB (U = 11304.5, z = 5.10, p_adj = 1.0 × 10⁻⁶) and LB (U = 7215.5, z = 4.52, p_adj = 1.9 × 10⁻⁵) relative to SC. In contrast, both blind groups showed greater internal connectivity and cohesiveness. Long-range recurrence, measured by the largest strongly connected component (LSC), was significantly increased in CB (mean ± SE = 0.879 ± 0.009) and LB (0.867 ± 0.012) compared to SC (0.789 ± 0.009; Kruskal–Wallis H = 76.37, p = 2.6 × 10⁻¹⁷, η² = 0.225). Pairwise Mann–Whitney tests yielded significant differences for CB vs. SC (U = 3420.0, z = − 8.11, p_adj = 1.6 × 10⁻¹⁵) and LB vs. SC (U = 2550.0, z = − 6.21, p_adj = 1.6 × 10⁻⁹). Late blind participants also showed a unique increase in short-range recurrence cycles, particularly in loops of two and three nodes. For L2 cycles, a significant group effect was observed (H = 10.55, p = 0.005, η² = 0.026), with LB showing higher recurrence than SC (U = 3944.0, z = − 3.00, p_adj = 0.007, η² = 0.042). Similarly, L3 cycles were elevated in LB (mean = 0.051 ± 0.004) relative to SC (0.038 ± 0.003; U = 3975.5, z = − 2.93, p_adj = 0.010, η² = 0.040), while CB did not differ significantly from controls in these metrics. Clustering coefficient (CC), a measure of local node interconnectivity, was also elevated in both blind groups compared to controls (Kruskal–Wallis H = 19.57, p = 5.6 × 10⁻⁵, η² = 0.053). Pairwise comparisons indicated significantly higher CC in CB vs. SC (U = 5910.5, z = − 3.94, p_adj = 2.5 × 10⁻⁴) and in LB vs. SC (U = 3764.0, z = − 3.42, p_adj = 0.002). Sliding-window analyses (30-word window, 1-word step) replicated these findings, revealing sustained differences in lexical diversity, recurrence, and cohesion (see Supplementary Tables 8–9). 3.2 Semantic (Syntactic) Graph Analysis Semantic dependency graphs yielded comparable patterns. Both CB and LB groups showed increased edge density and recurrence in dependency relationships compared to SC. Total normalized edge counts were significantly higher in CB (mean = 1.954 ± 0.032) and LB (1.894 ± 0.037) than SC (1.693 ± 0.020; H = 42.78, p = 5.1 × 10⁻¹⁰, η² = 0.124). Post hoc tests confirmed significant differences for CB vs. SC (U = 4661.5, z = − 6.03, p_adj = 5.1 × 10⁻⁹) and LB vs. SC (U = 3213.0, z = − 4.69, p_adj = 8.4 × 10⁻⁶). Short-range recurrence was similarly increased in LB across multiple measures, including repeated edges (RE: U = 3033.5, z = − 5.10, p_adj = 1.0 × 10⁻⁶, η² = 0.121), parallel edges (PE: U = 2700.5, z = − 5.86, p_adj = 1.3 × 10⁻⁸, η² = 0.160), and two-node cycles (L2: U = 2608.0, z = − 6.08, p_adj = 2.2 × 10⁻¹⁰, η² = 0.172). These effects were also observed in the CB group, but were slightly attenuated relative to LB (Supplementary Tables 10–13). Both blind groups showed significantly higher LSC values in semantic graphs, consistent with greater conceptual cohesion (H = 81.89, p = 1.7 × 10⁻¹⁸, η² = 0.242). Blind participants also exhibited higher semantic clustering coefficients than SC (mean CC: CB = 0.108, LB = 0.105, SC = 0.073; H = 62.38, p = 2.8 × 10⁻¹⁴). 3.3 Spectral Graph Measures Global structural properties assessed via spectral graph analysis further supported group-level distinctions. The spectral gap was significantly larger in CB than in SC (U = 6428.0, z = − 2.58, p_adj = 0.030), suggesting increased network connectivity and modular integrity in the blind group. Algebraic connectivity, another index of global robustness, was also higher in CB vs. SC (U = 6200.0, z = − 2.98, p_adj = 0.009). Similar trends were observed for semantic graphs, including elevated spectral radius and reduced eigenratio in blind individuals, indicating more cohesive and integrated linguistic networks (Supplementary Tables 14–19). Summarised, dream language from blind individuals, particularly those blind from birth, is characterized by decreased lexical diversity, increased local and global cohesion, and elevated structural recurrence. Late blind individuals further display distinctive short-range cyclic recurrence, potentially reflecting transitional stages of linguistic adaptation. These findings are consistent across structural and semantic graph domains and are reinforced by global network-level differences in spectral measures. 4. Discussion This pilot study demonstrates distinct differences in the structural and semantic organization of dream language in individuals with congenital and acquired (late) blindness (Fig. 3 a-e). Using a multi-tiered approach combining speech graph analysis, natural language processing, and spectral graph theory, we show that blind individuals, regardless of onset timing, exhibit reduced lexical diversity and increased linguistic cohesiveness during dream recall, relative to sighted controls. These differences were particularly marked in the congenitally blind group and were evident in both structural word adjacency and syntactic-semantic dependency graphs. An overview of observed linguistic trends across groups is presented in Table 4 . Notably, late blind participants exhibited unique patterns of short-range recurrence, potentially reflecting transitional features of adaptive reorganization. These preliminary findings suggest that visual experience may contribute to the neural organisation of endogenous cognitive narratives. For instance, the reduced lexical diversity (fewer unique nodes) in dream speech from blind individuals may reflect a more constrained repertoire of sensory-derived concepts, or a more sequential, path-oriented narrative strategy. This is consistent with prior work showing that blind speakers tend to focus on path and egocentric reference frames, emphasizing actions relative to the self rather than external landmarks 1 . In contrast, the increase in long-range structural cohesion (indexed by LSC and clustering coefficients) may suggest that blind individuals construct internally consistent, tightly interconnected narratives, perhaps indicative of more recursive, or self-referential linguistic structuring. These linguistic shifts appear to parallel known adaptations in spatial cognition and cortical organization 2 . Recent neuroimaging studies have shown that congenitally blind individuals exhibit altered entorhinal grid-cell geometry and enhanced recruitment of parietal regions during navigation, potentially suggestive of a shift from allocentric to egocentric spatial coding 2 . In keeping, the present findings may reflect a downstream manifestation of these neural adaptations in the structure of spontaneous language production. Thus, dream reports, being internally generated and unconstrained by immediate sensory input, may serve as a natural test case for examining how such neural reweighting manifests in cognitive and linguistic terms. Interestingly, while both blind groups showed similar trends in overall graph architecture, only the late blind cohort exhibited increased short-range recurrence (L2, L3 loops). These metrics have previously been associated with immature language development, lower educational attainment, and neurodevelopmental disorders such as attention-deficit and hyperactivity disorder (ADHD) 26 . In this context, the increase in short recurrent cycles in late blind individuals may indicate a partially reorganized, or incompletely stabilized, language encoding system following sensory loss. Whereas the congenitally blind develop within a consistently non-visual framework, those who lose vision later in life may have to adapt from a system originally calibrated to visual input, potentially leading to divergent pathways of neuroplastic reconfiguration. It is of note that such differences can be detected using graph-theoretical analysis, and this may suggest that in future SGA could offer a useful lens for exploring aspects of neurocognitive organization. Arguably, language graph analysis appears to offer a data-driven, language-invariant approach to characterising how experiences are internally structured and expressed 26 . Previous studies have applied this framework in clinical contexts, including schizophrenia, Alzheimer’s disease, and parasomnias 23 , 25 . The present findings tentatively extend this approach to the domain of sensory neurodiversity, suggesting that graph-based metrics may be sensitive to subtle variations in linguistic organisation associated with altered perceptual experience. Several limitations warrant consideration. First, this was a retrospective study based on secondary analysis of archival dream reports. While the DreamBank dataset is well-annotated and diverse, it was not originally assembled for the specific purpose of investigating sensory deprivation. As such, we could not control for potential confounding factors, including sleep quality, cognitive status, socioeconomic background, or the consistency of dream collection protocols. Second, although word count was addressed through both statistical adjustment and analytic design, we cannot rule out the influence of unmeasured individual differences in linguistic competence, such as verbal fluency, educational attainment, or preferred narrative style. Third, the dataset was derived from a relatively small number of individuals within each group, limiting generalisability and potentially amplifying idiosyncratic language patterns. Moreover, many of the dream reports, notably those from blind individuals, were collected several decades ago, raising the possibility that cultural, educational, or temporal factors may have shaped both content and expression in ways that are not fully accounted for. Finally, the cross-sectional nature of the data precludes any strong causal inference regarding the developmental impact of visual experience on cognitive and linguistic organisation. Nonetheless, these exploratory findings open several promising avenues for future research. Longitudinal studies could examine how language organization evolves in individuals who acquire blindness later in life and whether early interventions can modulate these trajectories. Neuroimaging work could complement the present findings by linking speech graph features with neural markers of spatial representation, memory consolidation, or sensory substitution. Importantly, the application of speech graph metrics to dream content, an unconstrained, internally generated domain, may offer a unique perspective on cognition in the absence of visual input, and may have broader relevance for understanding spontaneous thought processes in other populations, including those with autism, dementia, or trauma. In conclusion, we show that blindness is associated with distinct patterns of linguistic organisation during dream recall, reflecting both shared and differential adaptations in individuals with congenital and acquired vision loss. These findings underscore the close interdependence of perception, cognition, and language, and suggest that even the structure of internally generated narratives may be shaped, in part, by one’s sensory history. Declarations Data Availability Statement All data that support the findings of this study are available and open source at the DreamBank 27 . All methods were carried out in accordance with relevant guidelines and regulations. This study used human dream report data from the open-access DreamBank database (https://www.dreambank.net/), which collects and archives anonymized dream narratives under informed consent. As the analysis involved only retrospective, fully anonymized, publicly available data, additional institutional ethical approval was not required. Informed consent was obtained from all participants or their legal guardians at the time of original data collection. Acknowledgments We are indebted to our KCL colleagues, David Sherrin and Stephen Shemilt, whose generous help and care enabled this work. Special thanks is similarly owed to the DreamBank’s Adam Schneider and G. William Domhoff (Psychology Department, UC Santa Cruz, USA) for all their generous help in using the DreamBank. This research was funded in whole or in part by the Wellcome Trust [103952/Z/14/Z]. For open access, the author IR has applied for a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. This paper represents independent research in part funded by the NIHR Maudsley Biomedical Research Centre in South London and the Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or Department of Health and Social Care. Conflicts of Interest The authors declare no conflict of interest References Mamus, E., Speed, L.J., Rissman, L., Majid, A. & Ozyurek, A. Lack of Visual Experience Affects Multimodal Language Production: Evidence From Congenitally Blind and Sighted People. Cogn Sci 47 , e13228 (2023). Sigismondi, F., Xu, Y., Silvestri, M. & Bottini, R. Altered grid-like coding in early blind people. Nature Communications 15 , 3476 (2024). Fedorenko, E., Ivanova, A.A. & Regev, T.I. The language network as a natural kind within the broader landscape of the human brain. Nature Reviews Neuroscience 25 , 289-312 (2024). Malik-Moraleda, S. , et al. An investigation across 45 languages and 12 language families reveals a universal language network. Nature Neuroscience 25 , 1014-1019 (2022). Vecchi, T., Tinti, C. & Cornoldi, C. Spatial memory and integration processes in congenital blindness. NeuroReport 15 (2004). Cattaneo, Z. , et al. Imagery and spatial processes in blindness and visual impairment. Neuroscience & Biobehavioral Reviews 32 , 1346-1360 (2008). Sigismondi, F., Xu, Y., Silvestri, M. & Bottini, R. (2023). Kim, J.S., Aheimer, B., Montane Manrara, V. & Bedny, M. Shared understanding of color among sighted and blind adults. Proc Natl Acad Sci U S A 118 (2021). Nir, Y. & Tononi, G. Dreaming and the brain: from phenomenology to neurophysiology. Trends Cogn Sci 14 , 88-100 (2010). Christoff, K., Irving, Z.C., Fox, K.C., Spreng, R.N. & Andrews-Hanna, J.R. Mind-wandering as spontaneous thought: a dynamic framework. Nat Rev Neurosci 17 , 718-731 (2016). Fox, K.C., Nijeboer, S., Solomonova, E., Domhoff, G.W. & Christoff, K. Dreaming as mind wandering: evidence from functional neuroimaging and first-person content reports. Front Hum Neurosci 7 , 412 (2013). Gnoni, V. , et al. Nocturnal visual hallucinations in patients with disorders of arousal: a novel behavioral and EEG pattern. Croat Med J 63 , 438-447 (2022). Kang, J. , et al. Mental Imagery in Dreams of Congenitally Blind People. Brain Sci 13 (2023). Wasserman, D. , et al. Restricted truncal sagittal movements of rapid eye movement behaviour disorder. NPJ Parkinsons Dis 8 , 26 (2022). Ilic, K. , et al. Visuo-spatial imagery in dreams of congenitally and early blind: a systematic review. Front Integr Neurosci 17 , 1204129 (2023). Gnoni, V. , et al. Cotard's Parasomnia: Le Delire de Negation that occur during the Sleep-Wake Dissociation? J Clin Sleep Med (2020). Poole, N. & Rosenzweig, I. Dreaming of Godot. Lancet Neurol 19 , 801-802 (2020). Drakatos, P. , et al. Video polysomnographic findings in non-rapid eye movement parasomnia. J Sleep Res 28 , e12772 (2019). Cattaneo, Z. , et al. Imagery and spatial processes in blindness and visual impairment. Neurosci Biobehav Rev 32 , 1346-1360 (2008). Tinti, C., Adenzato, M., Tamietto, M. & Cornoldi, C. Visual experience is not necessary for efficient survey spatial cognition: evidence from blindness. Q J Exp Psychol (Hove) 59 , 1306-1328 (2006). Mota, N.B. , et al. Speech graphs provide a quantitative measure of thought disorder in psychosis. PloS one 7 (2012). Mota, N.B., Furtado, R., Maia, P.P., Copelli, M. & Ribeiro, S. Graph analysis of dream reports is especially informative about psychosis. Scientific reports 4 , 3691 (2014). Mota, N.B., Copelli, M. & Ribeiro, S. Thought disorder measured as random speech structure classifies negative symptoms and schizophrenia diagnosis 6 months in advance. NPJ Schizophr 3 , 18 (2017). Botezatu, M.R., Miller, E. & Kiselica, A.M. Limited connectedness of spontaneous speech may be a marker of dementia due to Alzheimer's disease. Front Aging Neurosci 15 , 1252614 (2023). See, Q.R. , et al. Dreaming Characteristics in Non-Rapid Eye Movement Parasomnia and Idiopathic Rapid Eye Movement Sleep Behaviour Disorder: Similarities and Differences. Nature and Science of Sleep Volume 16 , 263-277 (2024). Mota, N.B. , et al. Speech as a Graph: Developmental Perspectives on the Organization of Spoken Language. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 8 , 985-993 (2023). Schneider, A. & Domhoff, G.W. The Quantitative Study of Dreams. (2021). Hall, C.S. & Van De Castle, R.L. The content analysis of dreams (Appleton-Century-Crofts, East Norwalk, CT, US, 1966). Nikzad, A.H. , et al. Who does what to whom? graph representations of action-predication in speech relate to psychopathological dimensions of psychosis. Schizophrenia 8 , 58 (2022). Nettekoven, C.R. , et al. Semantic speech networks linked to formal thought disorder in early psychosis. Schizophrenia Bulletin 49 , S142-S152 (2023). Desai, R. & Riccardi, N. Cognitive Neuroscience of Language. (2021). Richardson, L. Beautiful soup documentation. (April, 2007). Harris, C.R. , et al. Array programming with NumPy. Nature 585 , 357-362 (2020). McKinney, W. Data structures for statistical computing in Python. in Proceedings of the 9th Python in Science Conference 51-56 (2010). Van Mieghem, P. Graph spectra for complex networks (Cambridge University Press, 2010). Estrada, E. Spectral theory of networks: From biomolecular to ecological systems. Analysis of complex networks: From biology to Linguistics , 55-83 (2009). de Haan, W. , et al. Disruption of functional brain networks in Alzheimer's disease: what can we learn from graph spectral analysis of resting-state magnetoencephalography? Brain connectivity 2 , 45-55 (2012). de Lange, S.C., de Reus, M.A. & van den Heuvel, M.P. The Laplacian spectrum of neural networks. Frontiers in computational neuroscience 7 , 189 (2014). Hagberg, A., Swart, P. & S Chult, D. Exploring network structure, dynamics, and function using NetworkX. (Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008). Virtanen, P. , et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods 17 , 261-272 (2020). Meghanathan, N. Spectral radius as a measure of variation in node degree for complex network graphs. in 2014 7th International Conference on u-and e-Service, Science and Technology 30-33 (IEEE, 2014). Wang, B., Tang, H., Zhou, T. & Xiu, Z. Optimizing synchronizability of networks. arXiv preprint cond-mat/0512079 (2005). Dörfler, F. & Bullo, F. Synchronization in complex networks of phase oscillators: A survey. Automatica 50 , 1539-1564 (2014). Hunter, J.D. Matplotlib: A 2D graphics environment. Computing in science & engineering 9 , 90-95 (2007). Table 1,4 Table 1,4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table14.docx SUPPLEMENT.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6761771","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468152515,"identity":"e912127d-6c03-414f-bfcf-d24f5e33a884","order_by":0,"name":"Kausar Raheel","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Kausar","middleName":"","lastName":"Raheel","suffix":""},{"id":468152520,"identity":"25cbc566-91ca-40e2-9c1e-23895999e822","order_by":1,"name":"Nazanin Biabani","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Nazanin","middleName":"","lastName":"Biabani","suffix":""},{"id":468152521,"identity":"20fb4db0-9a04-4b11-bea4-6f712b1e1899","order_by":2,"name":"Olga Ivanenko","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"","lastName":"Ivanenko","suffix":""},{"id":468152522,"identity":"6da4c6d4-9455-4a55-b71e-144c0e45d9db","order_by":3,"name":"Qi Rui See","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"Rui","lastName":"See","suffix":""},{"id":468152523,"identity":"d7475606-3f03-4212-be1f-14c9e5dabbf5","order_by":4,"name":"Rita Bertani","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Rita","middleName":"","lastName":"Bertani","suffix":""},{"id":468152524,"identity":"c0822323-7dfa-4dd2-bd86-ae848648044c","order_by":5,"name":"Valentina Gnoni","email":"","orcid":"","institution":"University of Bari Aldo Moro at Pia Fondazione “Card. 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Panico”","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Gnoni","suffix":""},{"id":468152525,"identity":"70ae1b1c-4500-4a5e-bdf7-79a2bd513126","order_by":6,"name":"David O’Regan","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"O’Regan","suffix":""},{"id":468152526,"identity":"20a4e279-5782-4ca7-91a7-9991dfd9e8be","order_by":7,"name":"Zoran Cvetkovic","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Zoran","middleName":"","lastName":"Cvetkovic","suffix":""},{"id":468152527,"identity":"1e1b2f66-1dfd-42c9-88eb-2d7cbe951b8f","order_by":8,"name":"Panagis Drakatos","email":"","orcid":"","institution":"Guy's and St Thomas’ NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Panagis","middleName":"","lastName":"Drakatos","suffix":""},{"id":468152528,"identity":"9a554b5b-be23-4468-a532-210d2c496cef","order_by":9,"name":"Giulio Bernardi","email":"","orcid":"","institution":"IMT School for Advanced Studies","correspondingAuthor":false,"prefix":"","firstName":"Giulio","middleName":"","lastName":"Bernardi","suffix":""},{"id":468152529,"identity":"85ec88f7-05c5-4e55-ba8f-20899b7df191","order_by":10,"name":"Robert Leech","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Leech","suffix":""},{"id":468152530,"identity":"edd545a3-d394-420c-b315-c518789b03f2","order_by":11,"name":"Carlos H. Schenck","email":"","orcid":"","institution":"University of Minnesota Medical School","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"H.","lastName":"Schenck","suffix":""},{"id":468152531,"identity":"5d19deef-e070-47c1-a134-d82126fbeca3","order_by":12,"name":"Liborio Parrino","email":"","orcid":"","institution":"Parma University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liborio","middleName":"","lastName":"Parrino","suffix":""},{"id":468152532,"identity":"99836d48-4091-4964-b7bc-50027a505b47","order_by":13,"name":"Peter J Goadsby","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"J","lastName":"Goadsby","suffix":""},{"id":468152533,"identity":"6fe999ea-1e3f-45ff-8a56-0ad1b840dea9","order_by":14,"name":"Adam Ockelford","email":"","orcid":"","institution":"University of Roehampton","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"","lastName":"Ockelford","suffix":""},{"id":468152534,"identity":"2aa407c1-3b11-4838-bc66-eb58042ac728","order_by":15,"name":"Ivana Rosenzweig","email":"data:image/png;base64,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","orcid":"","institution":"King’s College London","correspondingAuthor":true,"prefix":"","firstName":"Ivana","middleName":"","lastName":"Rosenzweig","suffix":""}],"badges":[],"createdAt":"2025-05-27 17:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6761771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6761771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84249872,"identity":"6a54a29e-df5a-44df-9ed0-efc36f0b1d3f","added_by":"auto","created_at":"2025-06-09 18:00:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph analyses procedures\u003c/strong\u003e. Structural and semantic speech graphs were generated from the transcribed dream reports of sighted controls, late blind and congenitally blind participants, deposited in the DreamBank\u003csup\u003e27\u003c/sup\u003e, as previously described\u003csup\u003e22, 25\u003c/sup\u003e. Major \u003cem\u003egeneral\u003c/em\u003e, \u003cem\u003econnectivity\u003c/em\u003e, \u003cem\u003erecurrence\u003c/em\u003e and \u003cem\u003eglobal\u003c/em\u003e speech graph attributes were defined, and three-group comparisons (Kruskal–Wallis test) and post hoc pairwise comparisons (Bonferroni-corrected Mann–Whitney U-tests) were conducted. Overall, we demonstrated a decrease in lexical diversity (measured by the number of nodes in the word graph) in both blind cohorts. This was, however, accompanied with increased textual cohesiveness (measured with long-range recurrence (LSC)), in both congenital and late blind cohorts, when compared to sighted controls. Interestingly, an increase in short-range recurrence (measured with L2+L3) was only noted in late blind cohort speech graphs, by comparison to sighted group. In past studies, this parameter has been associated with either younger neurodevelopmental age, an earlier age when leaving full time education, or with attention-deficit/hyperactivity disorder symptoms in participants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eIn the main panel\u003c/em\u003e, a representative structural speech graphs from one sighted control, late blind and congenitally blind participant, are shown. Distinct respective graph attributes are highlighted: loops of 2 and 3 nodes (L2 and L3) are highlighted by \u003cem\u003ered colour\u003c/em\u003e, the largest connected component (LCC; \u003cem\u003ethe light blue shade\u003c/em\u003e) and the largest strongly connected component (LSC; \u003cem\u003ethe dark blue shade\u003c/em\u003e). The LCC counts the largest set of nodes directly or indirectly linked by some path, whilst the LSC counts the largest set of nodes linked by reciprocal paths, so that all the nodes in the component are mutually reachable\u003csup\u003e24\u003c/sup\u003e. Additional \u003cem\u003epost hoc \u003c/em\u003eanalyses were also performed. Spectral analysis\u003csup\u003e41\u003c/sup\u003e of structural and semantic graphs was done to address the holistic aspects of the graphs, and for the structural graphs, dream reports were additionally analysed to control for verbosity, using a moving window of a fixed word length (30 words), with a step of three words\u003csup\u003e22, 24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/2ac20795d77d51c68753e940.png"},{"id":84250700,"identity":"e3fda4bb-e92d-4f05-bfa2-d83075d71095","added_by":"auto","created_at":"2025-06-09 18:16:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304877,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative images of the structural (\u003cstrong\u003ea\u003c/strong\u003e) and semantic graphs (\u003cstrong\u003eb\u003c/strong\u003e) are based on the dream reports of a sighted control subject, late blind subject and congenitally blind subject. Loops of 3 nodes (L3) are highlighted by red colour. Please refer to the Supplementary Data Source section for the core dream reports.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/e7480856c6a75f605404da9d.png"},{"id":84250699,"identity":"094849d7-07df-4f39-b6f0-5c59a3710c86","added_by":"auto","created_at":"2025-06-09 18:16:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":377905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural and semantic speech graph features differ between blind and sighted individuals during dream recall. In \u003c/strong\u003ea), lexical diversity is shown, measured by the number of unique nodes per graph. It was reduced in both congenitally blind (CB) and late blind (LB) individuals compared to sighted controls (SC). In b), long-range recurrence is indexed by the largest strongly connected component (LSC); it was significantly increased in both blind groups, suggesting greater structural cohesiveness in their dream narratives. In c), Short-range recurrence (sum of 2- and 3-node loops) was shown elevated only in the LB group, potentially reflecting transitional adaptation following visual loss. In d), spectral gap of structural graphs is shown; it was greater in the CB group relative to SC, consistent with globally enhanced network connectivity. Finally, in e), semantic clustering coefficient is shown; it was significantly higher in both blind groups, indicating tighter syntactic-conceptual linkage across dream language. \u003cem\u003eBoxplots show median (line), interquartile range (box), 95% confidence interval (whiskers); individual points are omitted for clarity. Group differences are supported by Kruskal–Wallis and Bonferroni-corrected Mann Whitney U-tests.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/e47a3fe353bd61417c9617b0.png"},{"id":93649047,"identity":"5b3196c7-2744-43c7-b895-5f9486b0a161","added_by":"auto","created_at":"2025-10-16 05:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2480884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/75615c72-903a-494d-8adc-377043374c5d.pdf"},{"id":84250697,"identity":"1bf0f6af-3e6e-4bb0-81f5-c7cbbe388511","added_by":"auto","created_at":"2025-06-09 18:16:49","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116193,"visible":true,"origin":"","legend":"","description":"","filename":"Table14.docx","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/8a8071cc2a6ba47b01fa2015.docx"},{"id":84250327,"identity":"adef5cfd-a4f6-4c53-b0e2-279a5ff0c74d","added_by":"auto","created_at":"2025-06-09 18:08:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6304291,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENT.docx","url":"https://assets-eu.researchsquare.com/files/rs-6761771/v1/aae9605a81984451aaa5a173.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural and Semantic Speech Graph Analysis of Dream Reports in Congenitally and Late Blind Individuals","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe structure of human thought is profoundly shaped by the sensory modalities through which we experience the world\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Of these, vision is arguably the most dominant, providing rich spatial, temporal, and contextual cues that scaffold cognitive development from early life onward. It has long been theorized that the loss or absence of vision prompts the brain to reorganize its functional architecture, with downstream effects on language, memory, and spatial cognition\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Yet, the extent to which this sensory reconfiguration penetrates the generative processes of internal experiences, such as dreams, remains largely unexplored.\u003c/p\u003e \u003cp\u003eDreams represent a unique window into endogenous cognition: spontaneous, internally driven simulations that integrate perceptual, emotional, and autobiographical elements\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Unlike externally constrained waking cognition, dreams are self-generated and decoupled from environmental inputs, offering an ecologically valid opportunity to probe the architecture of mental representation in the absence of external sensory cues\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For individuals with blindness, particularly those blind from birth, dream content may offer a useful perspective on how non-visual modalities contribute to experiential encoding and the organisation of narrative. However, most previous work on the dreams of blind individuals has focused on phenomenology, or content analysis, rather than the underlying cognitive-linguistic structure through which these dreams are expressed\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlindness has been associated with distinct patterns of spatial representation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Research in both behavioural and neuroimaging domains suggests that blind individuals rely more heavily on egocentric reference frames, those anchored to the self, compared to allocentric reference frames, which are centered on external landmarks or geometric layouts. For instance, congenitally blind individuals describe spatial environments more sequentially and emphasize the path of movement rather than landmarks or environmental layout\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This egocentric dominance is thought to result from compensatory recruitment of non-visual modalities, such as proprioception and audition, and the repurposing of visual cortical regions for non-visual processing\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the neural level, these differences in spatial cognition have been linked to distinct activation patterns in parietal and medial temporal regions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Notably, recent work has shown altered entorhinal grid-cell coding geometry in congenitally blind individuals, with reduced evidence for the characteristic hexagonal symmetry typically associated with allocentric cognitive maps. Instead, a 90\u0026deg; symmetry pattern has been observed, potentially reflecting a shift towards more discrete, self-referenced spatial coding\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These changes correlate with enhanced activation in the parietal cortex during navigation tasks, suggesting a neuroplastic reweighting of spatial computation in the absence of visual input\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite this growing body of evidence, little is known about how such spatial cognitive adaptations affect language, particularly the structure of spontaneous language, as might emerge in the context of dream recall. Language is not merely a communication tool but a representational system that encodes and reflects underlying cognitive processes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In this regard, dreams may offer a valuable platform for examining spontaneous narrative organization in a controlled yet naturalistic setting. Dreams are internally structured yet free from immediate social or sensory constraints, and thus, may provide an ideal context for studying how the brain generates and sequences complex experiences in the absence of visual scaffolding\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent advances in computational linguistics and graph theory have enabled a new class of analytic tools that can quantify the structural and semantic properties of language with high granularity. Speech graph analysis (SGA) is one such method, in which linguistic elements, typically words or lemmas, are modeled as nodes, and their sequential or semantic relationships as edges\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This framework enables the quantification of features such as lexical diversity (e.g. number of unique nodes), recurrence (e.g. cycles of two or three nodes), and global cohesion (e.g. clustering coefficients, strongly connected components)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. SGA has been validated as a sensitive tool in psychiatric research, distinguishing between typical and disordered thought in conditions such as schizophrenia\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and Alzheimer\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the context of dream reports, SGA provides a language-invariant and objective measure of how experiences are mentally encoded and subsequently narrated\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Preliminary evidence has shown that dream speech organization can reflect underlying neurophysiological states, including sleep stage, cognitive maturity, and psychopathology\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, to our knowledge, no prior study has applied these methods to examine how blindness, whether congenital or acquired, affects the structure of dream language. Doing so offers a unique opportunity to investigate how altered perceptual scaffolding may impact spontaneous cognitive and linguistic expression.\u003c/p\u003e \u003cp\u003eIn the present exploratory study, we set out to address this gap by applying both structural and semantic speech graph analysis to a large corpus of dream reports obtained from congenitally blind, late blind, and sighted individuals. Our primary aim was to test whether blindness is associated with specific alterations in the linguistic topology of dreams, and whether these alterations differ between those with congenital and late-onset visual deprivation. By analyzing both word-level transitions and deeper semantic dependencies, we sought to construct a multidimensional linguistic profile of dream narratives across sensory backgrounds.\u003c/p\u003e \u003cp\u003eWe hypothesized that dream reports from blind individuals would show reduced lexical diversity and increased internal cohesion, consistent with the hypothesized dominance of egocentric spatial and narrative frameworks. We further anticipated that these features would be more pronounced in the congenitally blind, who lack any visual experience and therefore rely exclusively on non-visual modalities throughout development. Additionally, we explored whether the presence of short-range recurrence patterns, previously associated with cognitive immaturity and neurodevelopmental conditions\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, would differentiate between late blind and congenitally blind participants, potentially indexing differences in neuroplastic adaptation. Our aim was to shed light on the intersection between sensory experience, spatial cognition, and linguistic representation, using dreams as a model system for endogenous cognitive architecture.\u003c/p\u003e"},{"header":"2. Methods and Materials","content":"\u003cp\u003eThis retrospective, cross-sectional study investigated the structural and semantic organization of dream-related language in individuals with differing visual experiences. The central aim was to determine whether congenital or acquired blindness is associated with measurable alterations in the linguistic structure of dream narratives, and whether these differences reflect underlying cognitive adaptations. To address this, we applied a multi-stage computational pipeline comprising natural language processing (NLP), speech graph analysis (SGA), and spectral graph theory to a carefully curated set of dream reports.\u003c/p\u003e\n\u003cp\u003eThe dataset was compiled from the DreamBank\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e corpus (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dreambank.net/\u003c/span\u003e\u003c/span\u003e), a large, open-access repository of anonymized dream narratives obtained under informed consent. Four dream series were selected for analysis: two comprising blind participants (both congenitally and late blind), and two consisting of age- and sex-matched sighted controls. Specifically, the blind cohort was drawn from the \u0026ldquo;Blind Dreamers (Female)\u0026rdquo; and \u0026ldquo;Blind Dreamers (Male)\u0026rdquo; series, while the control cohort was selected from the \u0026ldquo;Hall/VdC Norms: Female\u0026rdquo; and \u0026ldquo;Hall/VdC Norms: Male\u0026rdquo; series\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Inclusion criteria required that dream reports contain between 50 and 300 words, in order to standardize narrative length and avoid excessive sparsity or verbosity in graph representation. Dreams from individuals with partial vision or light perception were excluded from the congenitally blind group\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In total, 333 dream reports were included: 118 from congenitally blind individuals, 75 from late blind individuals, and 140 from sighted controls. Supplementary Tables\u0026nbsp;1 and 2 provide demographic and sample distribution details.\u003c/p\u003e\n\u003cp\u003eEach dream report underwent minimal preprocessing, limited to correction of typographic inconsistencies, and was then subjected to two parallel analytic streams: one based on structural word adjacency and the other on semantic dependencies. For structural analysis, each report was treated as a directed sequence of words, with individual words constituting nodes and adjacent word-pairs forming directed edges. Speech graphs were generated using the publicly available SpeechGraphs Java software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://neuro.ufrn.br/softwares/speechgraphs\u003c/span\u003e\u003c/span\u003e), which calculates a suite of 14 topological attributes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These attributes capture general properties (e.g. number of nodes and edges), measures of graph connectivity (e.g. largest connected component, largest strongly connected component, average total degree [ATD]), recurrence patterns (e.g. repeated edges [RE], parallel edges [PE], and short loops of one, two, or three nodes [L1, L2 or L3]), and global graph topology (e.g. density, clustering coefficient, average shortest path length, and graph diameter). All graph metrics were normalized by the number of nodes to control for narrative length (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A full list of speech graph attributes and definitions is provided in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNormalised descriptive statistics for the SGA of structural and semantic graphs (corrected per number of nodes).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eStructural Graphs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eSemantic Graphs\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003eSighted Controls (n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMdn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% BCI lower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003cp\u003eBCI\u003c/p\u003e\n \u003cp\u003eupper\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMdn\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% BCI lower\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% BCI upper\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eATD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiam (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eASP (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003e\u003cstrong\u003eLate Blind (n\u0026thinsp;=\u0026thinsp;75)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMdn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBCI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMdn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eATD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiam (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eASP (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"15\"\u003e\n \u003cp\u003e\u003cstrong\u003eCongenitally Blind (n\u0026thinsp;=\u0026thinsp;118)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMdn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBCI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMdn\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI lower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% BCI upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEdges\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eL3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLSC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eATD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiam (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eASP (LCC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: \u003cstrong\u003eATD\u003c/strong\u003e; Average total distance, \u003cstrong\u003eASP\u003c/strong\u003e; Average shortest path, \u003cstrong\u003eCC\u003c/strong\u003e; Clustering Coefficient, \u003cstrong\u003eDiam;\u003c/strong\u003e diameter, \u003cstrong\u003eLCC\u003c/strong\u003e; Largest Connected Component, \u003cstrong\u003eLSC\u003c/strong\u003e; Largest strongly connected component, \u003cstrong\u003eL1\u003c/strong\u003e; Cycles of one node, \u003cstrong\u003eL2\u003c/strong\u003e; Cycles of two nodes, \u003cstrong\u003eL3\u003c/strong\u003e; Cycles of three nodes, \u003cstrong\u003eMdn;\u003c/strong\u003e median, \u003cstrong\u003ePE\u003c/strong\u003e; parallel edges, \u003cstrong\u003eRE\u003c/strong\u003e; repeated edges, \u003cstrong\u003eSD\u003c/strong\u003e; Standard Deviation, \u003cstrong\u003eSE\u003c/strong\u003e; Standard Error, \u003cstrong\u003eSGA\u003c/strong\u003e; Speech Graph Attributes, \u003cstrong\u003eQ1\u003c/strong\u003e; first quartile, \u003cstrong\u003eQ3;\u003c/strong\u003e third quartile, \u003cstrong\u003e95% BCI lower;\u003c/strong\u003e 95% bootstrap confidence interval lower, \u003cstrong\u003e95% BCI upper;\u003c/strong\u003e 95% bootstrap confidence interval upper.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTo further account for verbosity and analyze language at a finer granularity, a sliding window approach was employed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Dream texts were segmented into overlapping windows of 30 words with a one-word step size, enabling localized assessments of graph properties across each narrative. This technique facilitated examination of microstructural variation in linguistic organization within and across participant groups.\u003c/p\u003e\n\u003cp\u003eFor semantic (or more correctly, syntactic) analysis, dream texts were processed using the Stanford CoreNLP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stanfordnlp.github.io/CoreNLP/demo.html\u003c/span\u003e\u003c/span\u003e) toolkit\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This included tokenization, part-of-speech tagging, and lemmatisation, followed by syntactic dependency parsing to identify relational structures between words. Lemmas, base forms of words\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, were used as graph nodes, and syntactic dependencies served as directed edges. Custom Python scripts incorporating BeautifulSoup \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, NumPy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, Pandas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and NetworkX libraries were developed to transform the CoreNLP outputs into semantic graphs. These graphs were constructed as directed and unweighted, with non-linguistic elements (e.g. punctuation, root nodes) and ambiguous self-loops removed. Unlike the structural graphs, which were analyzed using a sliding window, semantic graphs were constructed from entire dream reports to preserve the integrity of syntactic dependency chains.\u003c/p\u003e\n\u003cp\u003eTo capture the holistic organization of linguistic networks, both structural and semantic graphs were further subjected to spectral graph analysis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This approach uses the eigenvalues of the graph Laplacian matrix to characterize global connectivity and structural regularity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Disconnected graphs were excluded from this analysis. For each graph, we computed the spectral radius (the largest eigenvalue), the spectral gap (the difference between the largest and second largest eigenvalues), algebraic connectivity (the second-smallest eigenvalue, also known as the Fiedler value), and the eigenratio (the ratio of the spectral radius to the Fiedler value). These metrics provide insight into graph-wide features such as cohesion, modularity, and synchronizability. Spectral analyses were performed using the NetworkX\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and SciPy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e libraries in Python, and all Laplacian matrices were normalized to minimize size-related artifacts.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using non-parametric methods, as the Shapiro-Wilk test indicated that many graph attributes did not conform to normal distributions. Group-level differences across the three conditions (congenitally blind, late blind, and sighted controls) were assessed using the Kruskal\u0026ndash;Wallis test. Where significant main effects were observed, \u003cem\u003epost hoc\u003c/em\u003e comparisons were performed using the Mann\u0026ndash;Whitney U-test with Bonferroni correction for multiple comparisons. For metrics potentially influenced by word count, additional rank-based analyses of covariance were conducted, incorporating word count as a covariate. Effect sizes (\u0026eta;\u0026sup2;) were calculated for all comparisons and interpreted using standard thresholds (0.01\u0026thinsp;=\u0026thinsp;small, 0.06\u0026thinsp;=\u0026thinsp;moderate, 0.14\u0026thinsp;=\u0026thinsp;large). All statistical analyses were carried out in IBM SPSS Statistics (version 28.0.0) and Python (SciPy library) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with institutional and international guidelines for secondary data analysis of anonymized data. All original data are openly accessible through DreamBank\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. All data that support the findings of this study are available and open source at the DreamBank; the data analysed in this study were obtained from the open-access DreamBank repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dreambank.net/\u003c/span\u003e\u003c/span\u003e). The dataset consists of fully anonymised narratives collected with informed consent. As per UK regulations and institutional guidance (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.kcl.ac.uk/research/support/ethics\u003c/span\u003e\u003c/span\u003e), no additional ethical approval was required for this secondary analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 333 dream reports were analyzed: 118 from congenitally blind (CB), 75 from late blind (LB), and 140 from sighted controls (SC). Linguistic structure differed significantly between groups across both structural and semantic graph domains. Group-level statistical comparisons for structural and semantic graph features are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Detailed group comparisons and descriptive statistics are presented in Supplementary Tables\u0026nbsp;3\u0026ndash;19.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThree group comparison of normalised SGA of structural and semantic dream graphs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSGA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eStructural Graphs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eSemantic Graphs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDFH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eη2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eη2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.26E-06*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5.13E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;10\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.29E-07*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.93E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;12\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.76E-08*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e5.07E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;14\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.84E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;09\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.005*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.07E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;13\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.017*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.36E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;12\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.031*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.61E-17*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e81.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e1.65E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;18\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7.28E-06*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e4.64E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;14\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.034*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e3.28E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;06\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiam/\u003c/p\u003e \u003cp\u003eDiam-LCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASP/\u003c/p\u003e \u003cp\u003eASP-LCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.64E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;05\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e2.85E\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;14\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Structural Graph Analysis\u003c/h2\u003e \u003cp\u003eAcross the entire dataset, CB and LB participants exhibited significantly reduced lexical diversity compared to SC, as reflected by a lower number of unique nodes per dream graph. The Kruskal\u0026ndash;Wallis test revealed a significant group effect (H\u0026thinsp;=\u0026thinsp;33.38, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;5.7 \u0026times; 10⁻⁸, η\u0026sup2; = 0.095). Post hoc comparisons confirmed lower node counts in both CB (U\u0026thinsp;=\u0026thinsp;11304.5, z\u0026thinsp;=\u0026thinsp;5.10, p_adj\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 10⁻⁶) and LB (U\u0026thinsp;=\u0026thinsp;7215.5, z\u0026thinsp;=\u0026thinsp;4.52, p_adj\u0026thinsp;=\u0026thinsp;1.9 \u0026times; 10⁻⁵) relative to SC.\u003c/p\u003e \u003cp\u003eIn contrast, both blind groups showed greater internal connectivity and cohesiveness. Long-range recurrence, measured by the largest strongly connected component (LSC), was significantly increased in CB (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u0026thinsp;=\u0026thinsp;0.879\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009) and LB (0.867\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012) compared to SC (0.789\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009; Kruskal\u0026ndash;Wallis H\u0026thinsp;=\u0026thinsp;76.37, p\u0026thinsp;=\u0026thinsp;2.6 \u0026times; 10⁻\u0026sup1;⁷, η\u0026sup2; = 0.225). Pairwise Mann\u0026ndash;Whitney tests yielded significant differences for CB vs. SC (U\u0026thinsp;=\u0026thinsp;3420.0, z = \u0026minus;\u0026thinsp;8.11, p_adj\u0026thinsp;=\u0026thinsp;1.6 \u0026times; 10⁻\u0026sup1;⁵) and LB vs. SC (U\u0026thinsp;=\u0026thinsp;2550.0, z = \u0026minus;\u0026thinsp;6.21, p_adj\u0026thinsp;=\u0026thinsp;1.6 \u0026times; 10⁻⁹).\u003c/p\u003e \u003cp\u003eLate blind participants also showed a unique increase in short-range recurrence cycles, particularly in loops of two and three nodes. For L2 cycles, a significant group effect was observed (H\u0026thinsp;=\u0026thinsp;10.55, p\u0026thinsp;=\u0026thinsp;0.005, η\u0026sup2; = 0.026), with LB showing higher recurrence than SC (U\u0026thinsp;=\u0026thinsp;3944.0, z = \u0026minus;\u0026thinsp;3.00, p_adj\u0026thinsp;=\u0026thinsp;0.007, η\u0026sup2; = 0.042). Similarly, L3 cycles were elevated in LB (mean\u0026thinsp;=\u0026thinsp;0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004) relative to SC (0.038\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003; U\u0026thinsp;=\u0026thinsp;3975.5, z = \u0026minus;\u0026thinsp;2.93, p_adj\u0026thinsp;=\u0026thinsp;0.010, η\u0026sup2; = 0.040), while CB did not differ significantly from controls in these metrics.\u003c/p\u003e \u003cp\u003eClustering coefficient (CC), a measure of local node interconnectivity, was also elevated in both blind groups compared to controls (Kruskal\u0026ndash;Wallis H\u0026thinsp;=\u0026thinsp;19.57, p\u0026thinsp;=\u0026thinsp;5.6 \u0026times; 10⁻⁵, η\u0026sup2; = 0.053). Pairwise comparisons indicated significantly higher CC in CB vs. SC (U\u0026thinsp;=\u0026thinsp;5910.5, z = \u0026minus;\u0026thinsp;3.94, p_adj\u0026thinsp;=\u0026thinsp;2.5 \u0026times; 10⁻⁴) and in LB vs. SC (U\u0026thinsp;=\u0026thinsp;3764.0, z = \u0026minus;\u0026thinsp;3.42, p_adj\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eSliding-window analyses (30-word window, 1-word step) replicated these findings, revealing sustained differences in lexical diversity, recurrence, and cohesion (see Supplementary Tables\u0026nbsp;8\u0026ndash;9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Semantic (Syntactic) Graph Analysis\u003c/h2\u003e \u003cp\u003eSemantic dependency graphs yielded comparable patterns. Both CB and LB groups showed increased edge density and recurrence in dependency relationships compared to SC. Total normalized edge counts were significantly higher in CB (mean\u0026thinsp;=\u0026thinsp;1.954\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032) and LB (1.894\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037) than SC (1.693\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020; H\u0026thinsp;=\u0026thinsp;42.78, p\u0026thinsp;=\u0026thinsp;5.1 \u0026times; 10⁻\u0026sup1;⁰, η\u0026sup2; = 0.124). Post hoc tests confirmed significant differences for CB vs. SC (U\u0026thinsp;=\u0026thinsp;4661.5, z = \u0026minus;\u0026thinsp;6.03, p_adj\u0026thinsp;=\u0026thinsp;5.1 \u0026times; 10⁻⁹) and LB vs. SC (U\u0026thinsp;=\u0026thinsp;3213.0, z = \u0026minus;\u0026thinsp;4.69, p_adj\u0026thinsp;=\u0026thinsp;8.4 \u0026times; 10⁻⁶).\u003c/p\u003e \u003cp\u003eShort-range recurrence was similarly increased in LB across multiple measures, including repeated edges (RE: U\u0026thinsp;=\u0026thinsp;3033.5, z = \u0026minus;\u0026thinsp;5.10, p_adj\u0026thinsp;=\u0026thinsp;1.0 \u0026times; 10⁻⁶, η\u0026sup2; = 0.121), parallel edges (PE: U\u0026thinsp;=\u0026thinsp;2700.5, z = \u0026minus;\u0026thinsp;5.86, p_adj\u0026thinsp;=\u0026thinsp;1.3 \u0026times; 10⁻⁸, η\u0026sup2; = 0.160), and two-node cycles (L2: U\u0026thinsp;=\u0026thinsp;2608.0, z = \u0026minus;\u0026thinsp;6.08, p_adj\u0026thinsp;=\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁰, η\u0026sup2; = 0.172). These effects were also observed in the CB group, but were slightly attenuated relative to LB (Supplementary Tables\u0026nbsp;10\u0026ndash;13).\u003c/p\u003e \u003cp\u003eBoth blind groups showed significantly higher LSC values in semantic graphs, consistent with greater conceptual cohesion (H\u0026thinsp;=\u0026thinsp;81.89, p\u0026thinsp;=\u0026thinsp;1.7 \u0026times; 10⁻\u0026sup1;⁸, η\u0026sup2; = 0.242). Blind participants also exhibited higher semantic clustering coefficients than SC (mean CC: CB\u0026thinsp;=\u0026thinsp;0.108, LB\u0026thinsp;=\u0026thinsp;0.105, SC\u0026thinsp;=\u0026thinsp;0.073; H\u0026thinsp;=\u0026thinsp;62.38, p\u0026thinsp;=\u0026thinsp;2.8 \u0026times; 10⁻\u0026sup1;⁴).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spectral Graph Measures\u003c/h2\u003e \u003cp\u003eGlobal structural properties assessed via spectral graph analysis further supported group-level distinctions. The spectral gap was significantly larger in CB than in SC (U\u0026thinsp;=\u0026thinsp;6428.0, z = \u0026minus;\u0026thinsp;2.58, p_adj\u0026thinsp;=\u0026thinsp;0.030), suggesting increased network connectivity and modular integrity in the blind group. Algebraic connectivity, another index of global robustness, was also higher in CB vs. SC (U\u0026thinsp;=\u0026thinsp;6200.0, z = \u0026minus;\u0026thinsp;2.98, p_adj\u0026thinsp;=\u0026thinsp;0.009). Similar trends were observed for semantic graphs, including elevated spectral radius and reduced eigenratio in blind individuals, indicating more cohesive and integrated linguistic networks (Supplementary Tables\u0026nbsp;14\u0026ndash;19).\u003c/p\u003e \u003cp\u003eSummarised, dream language from blind individuals, particularly those blind from birth, is characterized by decreased lexical diversity, increased local and global cohesion, and elevated structural recurrence. Late blind individuals further display distinctive short-range cyclic recurrence, potentially reflecting transitional stages of linguistic adaptation. These findings are consistent across structural and semantic graph domains and are reinforced by global network-level differences in spectral measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis pilot study demonstrates distinct differences in the structural and semantic organization of dream language in individuals with congenital and acquired (late) blindness (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-e). Using a multi-tiered approach combining speech graph analysis, natural language processing, and spectral graph theory, we show that blind individuals, regardless of onset timing, exhibit reduced lexical diversity and increased linguistic cohesiveness during dream recall, relative to sighted controls. These differences were particularly marked in the congenitally blind group and were evident in both structural word adjacency and syntactic-semantic dependency graphs. An overview of observed linguistic trends across groups is presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Notably, late blind participants exhibited unique patterns of short-range recurrence, potentially reflecting transitional features of adaptive reorganization. These preliminary findings suggest that visual experience may contribute to the neural organisation of endogenous cognitive narratives. For instance, the reduced lexical diversity (fewer unique nodes) in dream speech from blind individuals may reflect a more constrained repertoire of sensory-derived concepts, or a more sequential, path-oriented narrative strategy. This is consistent with prior work showing that blind speakers tend to focus on path and egocentric reference frames, emphasizing actions relative to the self rather than external landmarks\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In contrast, the increase in long-range structural cohesion (indexed by LSC and clustering coefficients) may suggest that blind individuals construct internally consistent, tightly interconnected narratives, perhaps indicative of more recursive, or self-referential linguistic structuring.\u003c/p\u003e\n\u003cp\u003eThese linguistic shifts appear to parallel known adaptations in spatial cognition and cortical organization\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Recent neuroimaging studies have shown that congenitally blind individuals exhibit altered entorhinal grid-cell geometry and enhanced recruitment of parietal regions during navigation, potentially suggestive of a shift from allocentric to egocentric spatial coding\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In keeping, the present findings may reflect a downstream manifestation of these neural adaptations in the structure of spontaneous language production. Thus, dream reports, being internally generated and unconstrained by immediate sensory input, may serve as a natural test case for examining how such neural reweighting manifests in cognitive and linguistic terms.\u003c/p\u003e\n\u003cp\u003eInterestingly, while both blind groups showed similar trends in overall graph architecture, only the late blind cohort exhibited increased short-range recurrence (L2, L3 loops). These metrics have previously been associated with immature language development, lower educational attainment, and neurodevelopmental disorders such as attention-deficit and hyperactivity disorder (ADHD)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this context, the increase in short recurrent cycles in late blind individuals may indicate a partially reorganized, or incompletely stabilized, language encoding system following sensory loss. Whereas the congenitally blind develop within a consistently non-visual framework, those who lose vision later in life may have to adapt from a system originally calibrated to visual input, potentially leading to divergent pathways of neuroplastic reconfiguration. It is of note that such differences can be detected using graph-theoretical analysis, and this may suggest that in future SGA could offer a useful lens for exploring aspects of neurocognitive organization.\u003c/p\u003e\n\u003cp\u003eArguably, language graph analysis appears to offer a data-driven, language-invariant approach to characterising how experiences are internally structured and expressed\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Previous studies have applied this framework in clinical contexts, including schizophrenia, Alzheimer\u0026rsquo;s disease, and parasomnias\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The present findings tentatively extend this approach to the domain of sensory neurodiversity, suggesting that graph-based metrics may be sensitive to subtle variations in linguistic organisation associated with altered perceptual experience.\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, this was a retrospective study based on secondary analysis of archival dream reports. While the DreamBank dataset is well-annotated and diverse, it was not originally assembled for the specific purpose of investigating sensory deprivation. As such, we could not control for potential confounding factors, including sleep quality, cognitive status, socioeconomic background, or the consistency of dream collection protocols. Second, although word count was addressed through both statistical adjustment and analytic design, we cannot rule out the influence of unmeasured individual differences in linguistic competence, such as verbal fluency, educational attainment, or preferred narrative style. Third, the dataset was derived from a relatively small number of individuals within each group, limiting generalisability and potentially amplifying idiosyncratic language patterns. Moreover, many of the dream reports, notably those from blind individuals, were collected several decades ago, raising the possibility that cultural, educational, or temporal factors may have shaped both content and expression in ways that are not fully accounted for. Finally, the cross-sectional nature of the data precludes any strong causal inference regarding the developmental impact of visual experience on cognitive and linguistic organisation.\u003c/p\u003e\n\u003cp\u003eNonetheless, these exploratory findings open several promising avenues for future research. Longitudinal studies could examine how language organization evolves in individuals who acquire blindness later in life and whether early interventions can modulate these trajectories. Neuroimaging work could complement the present findings by linking speech graph features with neural markers of spatial representation, memory consolidation, or sensory substitution. Importantly, the application of speech graph metrics to dream content, an unconstrained, internally generated domain, may offer a unique perspective on cognition in the absence of visual input, and may have broader relevance for understanding spontaneous thought processes in other populations, including those with autism, dementia, or trauma.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we show that blindness is associated with distinct patterns of linguistic organisation during dream recall, reflecting both shared and differential adaptations in individuals with congenital and acquired vision loss. These findings underscore the close interdependence of perception, cognition, and language, and suggest that even the structure of internally generated narratives may be shaped, in part, by one\u0026rsquo;s sensory history.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data that support the findings of this study are available and open source at the DreamBank\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;All methods were carried out in accordance with relevant guidelines and regulations. This study used human dream report data from the open-access DreamBank database (https://www.dreambank.net/), which collects and archives anonymized dream narratives under informed consent. As the analysis involved only retrospective, fully anonymized, publicly available data, additional institutional ethical approval was not required. Informed consent was obtained from all participants or their legal guardians at the time of original data collection.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted to our KCL colleagues, David Sherrin and Stephen Shemilt, whose generous help and care enabled this work. \u0026nbsp;Special thanks is similarly owed to the DreamBank\u0026rsquo;s Adam Schneider and G. William Domhoff (Psychology Department, UC Santa Cruz, USA) for all their generous help in using the DreamBank.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This research was funded in whole or in part by the Wellcome Trust [103952/Z/14/Z]. For open access, the author IR has applied for a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This paper represents independent research in part funded by the NIHR Maudsley Biomedical Research Centre in South London and the Maudsley NHS Foundation Trust and King\u0026rsquo;s College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMamus, E., Speed, L.J., Rissman, L., Majid, A. \u0026amp; Ozyurek, A. 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Matplotlib: A 2D graphics environment. \u003cem\u003eComputing in science \u0026amp; engineering \u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e, 90-95 (2007).\u003cstrong\u003e\u003cstrong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1,4","content":"\u003cp\u003eTable 1,4 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blindness, Dream Recall, Speech Graph Analysis, Cognitive Mapping, Natural Language Processing","lastPublishedDoi":"10.21203/rs.3.rs-6761771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6761771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eVisual input is thought to influence spatial cognition and language. While blind individuals often rely on egocentric spatial representations, it remains unclear whether and how visual deprivation affects dream-related language. This study applied speech graph analysis (SGA) to investigate linguistic differences in dream reports from congenitally blind (CB), late blind (LB), and sighted control (SC) individuals.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 333 dream reports from an open-access database (DreamBank): 118 from CB, 75 from LB, and 140 from SC individuals. Graph-theoretical metrics of structural and semantic speech organization were extracted using validated NLP and SGA pipelines, including recurrence (L2/L3), connectivity (LSC), and lexical diversity (nodes).\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eCompared to SC, both CB and LB groups showed significantly reduced lexical diversity and increased long-range recurrence (LSC), suggesting greater linguistic cohesiveness. LB reports showed a specific increase in short-range recurrence cycles (L2, L3), not observed in CB. Spectral analysis supported these group differences, indicating altered graph-wide connectivity properties in blind groups.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eBlind individuals demonstrate distinct structural and semantic features in dream-related language, consistent with more egocentric narrative construction. These findings support a potential role of sensory experience in shaping cognitive-linguistic encoding. Further prospective studies are needed to explore underlying neural mechanisms and developmental trajectories.\u003c/p\u003e","manuscriptTitle":"Structural and Semantic Speech Graph Analysis of Dream Reports in Congenitally and Late Blind Individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 18:00:44","doi":"10.21203/rs.3.rs-6761771/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"80195bad-2cca-4a94-9174-3d34232fbb0c","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49703422,"name":"Biological sciences/Neuroscience"},{"id":49703423,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-10-16T05:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 18:00:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6761771","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6761771","identity":"rs-6761771","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}