Multisensory Integration Research Articles

Enhancing EFL Learning Through Multimodal Integration: The Role of Visual and Auditory Features in Moroccan Textbooks

This study examines the impact of multimodal integration, particularly the combination of visual and auditory elements, on EFL learning through Moroccan textbooks, specifically English Spotlight 1 and English Horizons. It investigates how these educational materials rely on visuals, diagrams, and audio tools to foster comprehension, retention, and engagement among secondary school students in Morocco. Employing a mixed-methods approach that includes both qualitative and quantitative analyses, the study evaluates the effectiveness of these features from the viewpoints of students and teachers. The results indicate that visual components significantly and positively affect vocabulary acquisition and grammatical comprehension, while auditory elements enhance listening skills and pronunciation. The research emphasizes the necessity for more advanced multimodal content in Moroccan EFL education and identifies technological barriers that impede full integration. It recommends an increased emphasis on multimodal resources in textbook development and the improvement of technological infrastructure in educational institutions.

Continuous integration of heading and goal directions guides steering.

Navigating animals must integrate a diverse array of sensory cues into a single locomotor decision. Insects perform intricate navigational feats using a brain region termed the central complex in which an animal's heading direction is transformed through several layers of circuitry to elicit goal-directed locomotion. These transformations occur mostly in the fan-shaped body (FB), a major locus of multi-sensory integration in the central complex. Key aspects of these sensorimotor computations have been extensively characterized by functional studies, leveraging the genetic tools available in the fruit fly. However, our understanding of how neuronal activity in the FB dictates locomotor behaviors during navigation remains enigmatic. Here, we manipulate the activity of two key neuronal populations that input into the FB-the PFN a and PFN d neurons-used to encode the direction of two complex navigational cues: wind plumes and optic flow, respectively. We find that flies presented with unidirectional optic flow steer along curved walking trajectories, but silencing PFN d neurons abolishes this curvature. We next use optogenetic activation to introduce a fictive heading signal in the PFNs to establish the causal relationship between their activity and steering behavior. Our studies reveal that the central complex guides locomotion by summing the PFN-borne directional signals and shifting movement trajectories left or right accordingly. Based on these results, we propose a model of central complex-mediated locomotion wherein the fly achieves fine-grained control of sensory-guided steering by continuously integrating its heading and goal directions over time.

Spatiotemporal mapping of auditory onsets during speech production.

The human auditory cortex is organized according to the timing and spectral characteristics of speech sounds during speech perception. During listening, the posterior superior temporal gyrus is organized according to onset responses, which segment acoustic boundaries in speech, and sustained responses, which further process phonological content. When we speak, the auditory system is actively processing the sound of our own voice to detect and correct speech errors in real time. This manifests in neural recordings as suppression of auditory responses during speech production compared to perception, but whether this differentially affects onset and sustained temporal profiles is not known. Here we investigated this question using intracranial EEG recorded from seventeen pediatric, adolescent, and adult patients with medication-resistant epilepsy while they performed a reading/listening task. We identified onset and sustained responses to speech in bilateral auditory cortex and observed a selective suppression of onset responses during speech production. We conclude that onset responses provide a temporal landmark during speech perception that is redundant with forward prediction during speech production and are therefore suppressed. Phonological feature tuning in these "onset suppression" electrodes remained stable between perception and production. Notably, auditory onset responses and phonological feature tuning were present in the posterior insula during both speech perception and production, suggesting an anatomically and functionally separate auditory processing zone that we believe to be involved in multisensory integration during speech perception and feedback control.Significance Statement Specific neural populations in the auditory cortex preferentially respond to the onset of speech sounds. These "onset responses" aid in perceiving boundaries in continuous speech. We recorded neural responses from patients with intracranial electrodes during a speaking and listening task to investigate the role of onset responses in speech production. Onset responses were present in the auditory cortex during listening, but absent during speaking. On the other hand, onset responses were observed in the insula during both conditions, suggesting a different functional role for the insula in auditory feedback processing. These findings extend our knowledge of how different parts of the brain involved in feedback control operate during speech production by identifying two functionally and anatomically distinct patterns of activity.

Decoding the brain: EEG applications in sensory processing, emotion recognition, and pain assessment

This work explores the diverse applications of electroencephalography (EEG) across sensory processing, emotion recognition, and pain assessment. It analyzes recent studies employing EEG to investigate auditory perception, visual processing, emotional states, and pain experiences, highlighting common methodological approaches such as preprocessing techniques, feature extraction methods, and classification algorithms. Key findings reveal EEG’s potential in decoding complex brain states, from identifying neural correlates of music perception to detecting pain signals, and discuss the advancement of machine learning techniques, particularly deep learning, in enhancing the accuracy and real-time capabilities of EEG-based systems. The paper also addresses challenges in EEG research, such as limited spatial resolution and the need for larger, more diverse datasets, and proposes future directions emphasizing multimodal integration, standardization of protocols, and the development of user-friendly tools for clinical applications. This paper underscores EEG’s significant role in advancing our understanding of brain function and its potential to revolutionize neurological diagnostics and treatments.

In-Bed Monitoring: A Systematic Review of the Evaluation of In-Bed Movements Through Bed Sensors

The growing popularity of smart beds and devices for remote healthcare monitoring is based on advances in artificial intelligence (AI) applications. This systematic review aims to evaluate and synthesize the growing literature on the use of machine learning (ML) techniques to characterize patient in-bed movements and bedsore development. This review is conducted according to the principles of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and is registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42022314329). The search was performed through nine scientific databases. The review included 78 articles, including 142 ML models. The applied ML models revealed significant heterogeneity in the various methodologies used to identify and classify patient behaviors and postures. The assortment of ML models encompassed artificial neural networks, deep learning architectures, and multimodal sensor integration approaches. This review shows that the models for analyzing and interpreting in-bed movements perform well in experimental settings. Large-scale real-life studies are lacking in diverse patient populations.

Modal-nexus auto-encoder for multi-modality cellular data integration and imputation

Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there’s a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E’s accuracy and robustness in multi-modality cellular data integration and imputation.

Integrated and sustainable performance evaluation of urban rail transit systems using fuzzy sustainability index

The complexity of prominent dimensions in urban rail transportation sector arises from the involvement of several stakeholders and the various interacting variables for evaluating transportation performance. This study uses a fuzzy logic-based method to present an index-based framework for sustainable performance assessment. The proposed framework presents 4 main indicators which include environmental, economic, financial and social sustainability, 14 sub-indicators, 4 main indicators for transportation system effectiveness with 13 sub-indicators and 1 main indicator for multi modal integration with 6 sub-indicators. The performance ratings and importance weights of the transport sustainability attributes are evaluated using a fuzzy logic approach. A numerical illustration is provided to demonstrate the applicability of the approach. The results show that system capacity (passenger capacity of a route), comfort and customer service (passenger environment and ride quality, maintenance cycle) and multimodal integration (park and ride facilities) were found to be weaker attributes. The proposed methodology facilitates the systematic understanding of the prominent attributes for achieving integrated and sustainable transport performance by using the ‘Fuzzy Transport Sustainability Index’.

Neural effects of multisensory dance training in Parkinson's disease: evidence from a longitudinal neuroimaging single case study.

Dance is associated with beneficial outcomes in motor and non-motor domains in Parkinson's disease (PD) and regular participation may help delay symptom progression in mild PD. However, little is known about the neurobiological mechanisms of dance interventions for PD. The present case study explored potential neuroplastic changes in a 69-year-old male with mild PD participating in regular dance classes over 29 weeks. Functional MRI was performed at four timepoints (pre-training, 11 weeks, 18 weeks, 29 weeks), where the individual imagined a dance choreography while listening to the corresponding music. Neural activity was compared between dance-imagery and fixation blocks at each timepoint. Analysis of functionally defined regions revealed significant blood-oxygen-level-dependent (BOLD) signal activation in the supplementary motor area, right and left superior temporal gyri and left and right insula, with modulation of these regions observed over the training period except for the left insula. The results suggest the potential for dance to induce neuroplastic changes in people with PD in regions associated with motor planning and learning, auditory processing, rhythm, emotion, and multisensory integration. The findings are consistent with dance being a multimodal therapeutic activity that could provide long-term benefits for people with PD.

Spike-based computational primitives to reproduce neural dynamics in the parietal cortex during motor preparation

Abstract Biologically plausible spiking neural network models of sensory cortices can be instrumental in understanding and validating their principles of computation.
 Models based on Cortical Computational Primitives (CCPs), such as Hebbian plasticity and Winner-Take-All (WTA) networks, have already been successful in this approach.
 However, the specific nature and roles of CCPs in sensorimotor cortices during cognitive tasks are yet to be fully deciphered.
 The evolution of motor intention in the Posterior Parietal Cortex (PPC) before arm-reaching movements is a well-suited cognitive process to assess the effectiveness of different CCPs.
 To this end, we propose a biologically plausible model composed of heterogeneous spiking neurons which implements and combines multiple CCPs, such as multi-timescale learning and soft WTA modules.
 By training the model to replicate the dynamics of in-vivo recordings from non-human primates, we show how it is effective in generating meaningful representations from unbalanced input data, and in faithfully reproducing the transition from motor planning to action selection.
 Our findings elucidate the importance of distributing spike-based plasticity across multi-timescales, and provide an explanation for the role of different CCPs in models of frontoparietal cortical networks for performing multisensory integration to efficiently inform action execution.

Multisensory Integration of Native and Nonnative Speech in Bilingual and Monolingual Adults.

Face-to-face speech communication is an audiovisual process during which the interlocuters use both the auditory speech signals as well as visual, oral articulations to understand the other. These sensory inputs are merged into a single, unified process known as multisensory integration. Audiovisual speech integration is known to be influenced by many factors, including listener experience. In this study, we investigated the roles of bilingualism and language experience on integration. We used a McGurk paradigm in which participants were presented with incongruent auditory and visual speech. This included an auditory utterance of 'ba' paired with visual articulations of 'ga' that often induce the perception of 'da' or 'tha', a fusion effect that is strong evidence of integration, as well as an auditory utterance of 'ga' paired with visual articulations of 'ba' that often induce the perception of 'bga', a combination effect that is weaker evidence of integration. We compared fusion and combination effects on three groups ( N = 20 each), English monolinguals, Spanish-English bilinguals, and Arabic-English bilinguals, with stimuli presented in all three languages. Monolinguals exhibited significantly stronger multisensory integration than bilinguals in fusion effects, regardless of the stimulus language. Bilinguals exhibited a nonsignificant trend by which greater experience led to increased integration as measured by fusion. These results held regardless of whether McGurk presentations were presented as stand-alone syllables or in the context of real words.

High-resolution 7T fMRI reveals the visual zone of the human claustrum

Abstract The claustrum is a thin grey matter structure located between the insular cortex and the putamen. The function of the claustrum is largely unknown with diverse hypotheses ranging from multisensory integration and consciousness to attention and cognitive control. Much research on the function of the claustrum relies on invasive techniques in animal models, as the claustrum’s uniquely thin shape makes it difficult to image non-invasively in human subjects. In the current proof-of-concept study, we used high-resolution ultra-high field (7 Tesla) functional magnetic resonance imaging (fMRI) to measure activity in the human claustrum during the processing of naturalistic stimuli. We presented short video clips as visual only, auditory only or audiovisual conditions while participants performed a central fixation task. We found distinct visual responses in both the left and the right claustrum at a consistent spatial location across participants, hemispheres and sessions. We also found deactivations in response to auditory stimulation. These deactivations were confined to the right claustrum and did not overlap with visual activity. The deactivation in response to auditory stimulation demonstrates the complexity of claustrum’s functional organization and suggests functional differentiation within the claustrum. This is the first study to demonstrate sensory-specific effects within the human claustrum. It opens the possibility for studying the claustrum’s role in higher-level aspects of sensory processing in humans.

Wearable EEG-Based Brain-Computer Interface for Stress Monitoring.

Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain-Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI.

Enhanced multisensory gain in older adults may be a by-product of inverse effectiveness: Evidence from a speeded response-time task.

Older adults experience a greater benefit from multisensory integration than their younger counterparts, but it is unclear why. One hypothesis is that age-related sensory decline weakens unisensory stimulus effectiveness, causing a boost in multisensory gain through inverse effectiveness. Many previous studies present stimuli at the same intensity for both younger and older adults (i.e., stimulus-matched), as opposed to accounting for each participant's unique perceptual ability (i.e., perception-matched). This makes it difficult to discern the source of age-related differences in multisensory gain. As such, we used two experiments to examine whether sensory decline is contributing to age-related differences in multisensory gain. In the first, we presented auditory (pure tones in noise), visual (Gabor patches in noise), and audiovisual stimuli and recorded response times from 31 younger (18-25) and 30 older (55-80) adults. Importantly, all participants were given identical stimuli, with the expectation that older adults would show worse unisensory performance, inducing inverse effectiveness. The second task was identical (younger N = 31, older N = 34), except stimuli were presented at each participant's 50% detection threshold, identified with an adaptive psychophysical staircase, controlling for any influence of inverse effectiveness. Older adults were found to exhibit greater multisensory gain (as measured by race model violations) on stimulus- but not perception-matched tasks, thus aligning with the principle of inverse effectiveness. That is, when accounting for potential age-related differences in perceptual abilities, older adults no longer experienced greater benefit from multisensory integration. These two experiments together suggest that the age-related increases in multisensory integration previously reported may be in part due to age-related declines in vision and audition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian mapping of protein kinases to vasopressin-regulated phosphorylation sites in renal collecting duct.

Vasopressin controls water permeability in the renal collecting duct by regulating the water channel protein, aquaporin-2 (AQP2). Phosphoproteomic studies have identified multiple proteins that undergo phosphorylation changes in response to vasopressin. The kinases responsible for the phosphorylation of most of these sites have not been identified. Here, we use large-scale Bayesian data integration to predict the responsible kinases for 51 phosphoproteomically identified vasopressin-regulated phosphorylation sites in the renal collecting duct. To do this, we applied Bayes' rule to rank the 515 known mammalian protein kinases for each site. Bayes' rule was applied recursively to integrate each of the seven independent datasets, each time using the posterior probability vector of a given step as the prior probability vector of the next step. In total, 30 of the 33 phosphorylation sites that increase with vasopressin were predicted to be phosphorylated by protein kinase A (PKA) catalytic subunit-α, consistent with prior studies implicating PKA in vasopressin signaling. Eighteen of the vasopressin-regulated phosphorylation sites were decreased in response to vasopressin and all but three of these sites were predicted to be targets of extracellular signal-regulated kinases, ERK1 and ERK2. This result implies that ERK1 and ERK2 are inhibited in response to vasopressin V2 receptor occupation, secondary to PKA activation. The six phosphorylation sites not predicted to be phosphorylated by PKA or ERK1/2 are potential targets of other protein kinases previously implicated in aquaporin-2 regulation, including cyclin-dependent kinase 18 (CDK18), calmodulin-dependent kinase 2δ (CAMK2D), AMP-activated kinase catalytic subunit-α-1 (PRKAA1) and CDC42 binding protein kinase β (CDC42BPB).NEW & NOTEWORTHY Vasopressin regulates water transport in the renal collecting duct in part through phosphorylation or dephosphorylation of proteins that regulate aquaporin-2. Prior studies have identified 51 vasopressin-regulated phosphorylation sites in 45 proteins. This study uses Bayesian data integration techniques to combine information from multiple prior proteomics and transcriptomics studies to predict the protein kinases that phosphorylate the 51 sites. Most of the regulated sites were predicted to be phosphorylated by protein kinase A or ERK1/ERK2.

Ion-migration-induced synaptic plasticity in high ion-storable fluorine graphdiyne for information processing

Neuromorphic devices encounter constraints associated with complementary metal–oxide–semiconductor technology, such as intricate electronics and high power consumption. Recently, solid polymer electrolytes (SPE) have gained attraction in electronic devices for constructing artificial synapses, primarily due to their low-voltage operation enabled by ion-assisted signal transmission. This study presents a two-terminal artificial synapse fabricated using fluorine graphdiyne (F-GDY) synthesized through chemical vapor deposition and ion-doped SPE. The device architecture consists of Au/SPE/F-GDY/Si, which operates by ion migration induced by external potential pulses. Electrical measurements are conducted to depict the synaptic plasticity of the devices. Among devices employing different ion-doped SPEs (Li-SPE, Na-SPE, K-SPE, and Ca-SPE), the one utilizing Li-SPE demonstrates the highest synaptic weight. This is attributed to the superior Li+ ion storage capacity of F-GDY; hence, Li-SPE/F-GDY was chosen for in-depth examinations of synaptic activity presenting long-term potentiation, short-term depression, and pair-pulse facilitation. The device has good durability and stability and can respond to low action potential with power consumption at the pJ level. Furthermore, Modified National Institute of Standards and Technology simulations reveal its learning and pattern recognition capability with 91.3 % accuracy after 10 training epochs. Finally, flexible substrate-based SPE/F-GDY devices integrated with gate electrodes were fabricated to record physiological signals, showing promise for in-sensing applications. This work shows the potential of F-GDY/Li-SPE devices for low-power neuromorphic computing, though real-time parallel processing and multimodal integration (e.g., visual, auditory) remain unexplored. However, the biocompatibility and efficient ion-assisted transmission of F-GDY suggest promising future applications in these areas.

The Integration of Radiomics and Artificial Intelligence in Modern Medicine.

With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.

Real-Time Forecasting of Intradialytic Hypotension Using Deep Learning and Multimodal Data Integration

Real-Time Forecasting of Intradialytic Hypotension Using Deep Learning and Multimodal Data Integration

Excitatory neurons and oligodendrocyte precursor cells are vulnerable to focal cortical dysplasia type IIIa as suggested by single-nucleus multiomics.

Focal cortical dysplasia (FCD) is a heterogeneous group of cortical developmental malformations that constitute a common cause of medically intractable epilepsy. FCD type IIIa (FCD IIIa) refers to temporal neocortex alterations in architectural organisation or cytoarchitectural composition in the immediate vicinity of hippocampal sclerosis. Slight alterations in the temporal neocortex of FCD IIIa patients pose a challenge for the preoperative diagnosis and definition of the resection range. We have performed multimodal integration of single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin sequencing in the epileptogenic cortex of four patients with FCD IIIa, and three relatively normal temporal neocortex were chosen as controls. Our study revealed that the most significant dysregulation occurred in excitatory neurons (ENs) and oligodendrocyte precursor cells (OPCs) in the epileptogenic cortex of FCD IIIa patients. In ENs, we constructed a transcription factor (TF)-hub gene regulatory network and found DAB1high ENs subpopulation mediates neuronal immunity characteristically in FCD IIIa. Western blotting and immunofluorescence were used to validate the changes in protein expression levels caused by some of the key genes. The OPCs were activated and exhibited aberrant phenotypes in FCD IIIa, and TFs regulating reconstructed pseudotime trajectory were identified. Finally, our results revealed aberrant intercellular communication between ENs and OPCs in FCD IIIa patients. Our study revealed significant and intricate alterations in the transcriptomes and epigenomes in ENs and OPCs of FCD IIIa patients, shedding light on their cell type-specific regulation and potential pathogenic involvement in this disorder. This work will help evaluate the pathogenesis of cortical dysplasia and epilepsy and explore potential therapeutic targets. Paired snRNA-seq and snATAC-seq data were intergrated and analysed to identify crucial subpopulations of ENs and OPCs in the epileptogenic cortex of FCD IIIa patients and explore their possible pathogenic role in the disease. A TF-hub gene regulatory network was constructed in ENs, and the DAB1high Ex-1 mediated neuronal immunity was characterstically in FCD IIIa patients. The OPCs were activated and exhibited aberrant phenotypes in FCD IIIa patients, and TFs regulating reconstructed pseudotime traectory were identified. Aberrant intercelluar communications between ENs and OPCs in FCD IIIa patients were identified.

Multimodal biometric integration: Trends and insights from the past quinquennial

Recent years have seen a notable breakthrough in multimodal biometric systems, which combine numerous biometric modalities for increased security and accuracy. This paper offers a thorough analysis of recent advances in the field, addressing the many multimodal biometric strategies that have been put out by different researchers. Numerous modalities are covered by the evaluation, such as palm print, speech, iris, facial features, palm print, fingerprint, body form, gait, and more. To obtain amazing results in terms of accuracy and security, researchers have used cutting-edge methods like convolutional neural networks (CNNs), support vector machines (SVM), recurrent neural networks (RNNs), deep learning, and optimization algorithms. Many fusion strategies have been investigated to efficiently merge data from many modalities, such as feature-level fusion, decision-level fusion, and score-level fusion. Developments in template protection systems have also addressed security issues related to transmission and storage of biometric data. Even though a lot of the suggested systems have shown great accuracy rates, there are still issues with hardware restrictions, dataset biases, privacy problems, and computational costs. Prospective study avenues encompass investigating more extensive and heterogeneous datasets, crafting resilient fusion algorithms, and amalgamating cutting-edge technologies like deep learning-based biometric cryptosystems and federated learning. All things considered, the literature study demonstrates the enormous potential of multimodal biometric systems in offering safe and dependable authentication solutions for a wide range of applications, from on-line student authentication and proctoring systems to customized healthcare networks.

Peripersonal Space Plasticity in Relation to Psychopathology and Anomalous Subjective Experiences in Individuals With Early-Onset and Adult-Onset Schizophrenia.

Individuals with schizophrenia present anomalies in the extension and plasticity of the peripersonal space (PPS), the section of space surrounding the body, shaped through motor experiences. A weak multisensory integration in PPS would contribute to an impairment of self-embodiment processing, a core feature of the disorder linked to specific subjective experiences. In this exploratory study, we aimed at: (1) testing an association between PPS features, psychopathology, and subjective experiences in schizophrenia; (2) describing the PPS profile in individuals with early-onset schizophrenia. Twenty-seven individuals with schizophrenia underwent a task measuring the PPS size and boundaries demarcation before and after a motor training with a tool. The Positive And Negative Syndrome Scale (PANSS), the Examination of Anomalous Self Experience scale (EASE) and the Autism Rating Scale (ARS) were used to assess psychopathology. Subsequently, participants were divided into two subgroups, early and adult-onset schizophrenia. The two groups were compared in regard to their PPS and psychopathological profiles. PPS patterns were associated with psychopathology, particularly positively with PANSS negative scale score, and negatively with subjective experiences of existential reorientation (EASE Domain 5 scores) and of social encounters (ARS scores). Only PPS parameters and ARS scores differentiated between early and adult-onset participants. Our results, although preliminary and exploratory, can suggest a link between PPS patterns, negative symptoms, and disturbances of the subjective experience, particularly in the intersubjective domain, in schizophrenia. Moreover, they seem to suggest that specific PPS profiles and schizophrenic autism traits could be markers of early-onset schizophrenia.