Temporal Aspects Research Articles

Face Manipulation Detection

The rise of digital manipulation techniques has led to the proliferation of deepfakes and other manipulated facial images, posing significant challenges to online trust and security. This paper proposes a novel deep learning model aimed at efficiently and effectively detecting face manipulations. The architecture combines the strengths of Efficient Net, a convolutional neural network (CNN) renowned for its accuracy and efficiency, with Long Short-Term Memory (LSTM) networks. Efficient Net is utilized for extracting high-level features from facial images, enabling the model to capture subtle inconsistencies that may indicate manipulation. These features serve as crucial inputs to the subsequent analysis performed by LSTM networks. LSTMs excel at capturing temporal dependencies within sequences of data, making them particularly well-suited for detecting manipulations in video sequences. By leveraging the power of CNNs for feature extraction and the sequential learning capabilities of LSTMs, the proposed hybrid approach aims to achieve superior performance in face manipulation detection. This combination allows the model to effectively analyse both spatial and temporal aspects of facial images, enhancing its ability to detect various forms of manipulation accurately

Temporal Aspects of Psychosocial Mediators of the Exercise-Weight Loss Maintenance Relationship Within Scalable Behavior-Change Treatments.

Limited knowledge of psychological correlates of weight loss is associated with continuing failures of behavioral obesity treatments beyond the short term. This study aimed to inform health professionals' obesity interventions via an increased knowledge of mediators of the exercise-weight loss maintenance relationship. Women participated in 6-month obesity treatments within community settings emphasizing moderate exercise and self-regulation skills development via primarily in-person (n = 54) or primarily written (n = 54) means. Changes in mood, self-regulating eating, and weight over 6, 12, and 24 months were assessed. A moderated mediation model was tested using the PROCESS macro instruction. Improvements in mood, self-regulating eating, and weight were significantly greater in the in-person group. The relationship between a dichotomous measure of completing at least 3 sessions of exercise per week (or not) and change in weight over 6 months was no longer significant when the mediators of changes in negative mood and self-regulation of eating were sequentially entered. Paths of exercise→negative mood reduction→eating self-regulation increase→weight loss over 6, 12 and 24 months were significant. Exercise self-regulation at Month 3 significantly moderated the mood change→eating self-regulation change relationship. Based on the identified paths, scalable obesity-treatment content and emphases were informed. This could help guide health professionals' actions concerning the management of obesity.

Identification of Counterfeit Videos using Deep Learning Methodology

The rise of deep learning has ushered in a proliferation of deep fake videos, posing significant challenges to the credibility of visual content. Our research introduces a groundbreaking approach by merging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to enhance the accuracy of deepfake prediction. This unique integration, which has not been previously implemented, significantly boosts the model's capability to discern deepfakes. The synergy of CNNs and RNNs in our methodology represents an advancement, contributing to increased accuracy in detecting synthetic content. We leverage CNNs and RNNs for an efficient solution. First, we employ a Res-Next CNN to extract distinctive features from individual video frames, effectively encoding spatial information. These features are then used in the subsequent phase, where a LSTM-RNN models temporal dynamics within the videodata.The temporal aspect is crucial in differentiating deep fake videos due to subtle inconsistencies over time. The LSTM RNN processes the feature sequence, enabling the model to identify temporal patterns unique to deep fakes. This holistic approach, combining spatial and temporal analysis, enhances the model's ability to detect even highly convincing synthetic content. Our model is trained on a comprehensive dataset with rigorous evaluations, demonstrating competitive performance through standard metrics such as accuracy, precision. Practically, our model offers real- time video analysis, automatically identifying deep fake content and mitigating potential risks. Importantly, our approach is simple and robust, suitable for deployment across diverse scenarios. In summary, our research provides an effective solution to the critical issue of deepfake detection. By synergizing CNNs and LSTM-based RNNs, we offer a practical means to uphold the integrity of visual content in an era where digital information authenticity is paramount. Keywords— Deep Learning, CNN, RNN, Deepfake, LSTM, accuracy, precision, visual content, digital information

MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI Recommendation

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia, and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) Ignoring personalized spatial- and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users; (2) Insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

Research of visual attention networks in deaf individuals: a systematic review

The impact of deafness on visual attention has been widely discussed in previous research. It has been noted that deficiencies and strengths of previous research can be attributed to temporal or spatial aspects of attention, as well as variations in development and clinical characteristics. Visual attention is categorized into three networks: orienting (exogenous and endogenous), alerting (phasic and tonic), and executive control. This study aims to contribute new neuroscientific evidence supporting this hypothesis. This paper presents a systematic review of the international literature from the past 15 years focused on visual attention in the deaf population. The final review included 24 articles. The function of the orienting network is found to be enhanced in deaf adults and children, primarily observed in native signers without cochlear implants, while endogenous orienting is observed only in the context of gaze cues in children, with no differences found in adults. Results regarding alerting and executive function vary depending on clinical characteristics and paradigms used. Implications for future research on visual attention in the deaf population are discussed.

Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics

Objective. Traditional quantification of fluorescence signals, such as ΔF/F , relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution. Approach. We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics. Main result. We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure ( ΔF/F ). Additionally, we demonstrate ARES’s utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite. Significance. ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics—where traditional ratiometric measures falter—underscores its potential to revolutionize baseline-independent analyses in the field.

Cellular diversity through space and time: adding new dimensions to GBM therapeutic development

The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.

A dynamic system optimal dedicated lane design for connected and autonomous vehicles in a heterogeneous urban transport network

Numerous contemporary studies have posited that connected and autonomous vehicles (CAVs) hold the potential to enhance traffic safety and augment efficiency substantially. One widely discussed approach to optimize CAV operations within urban traffic networks involves the implementation of dedicated lanes (DLs). This study aims to assist system planners in optimally deploying DLs within heterogeneous urban traffic networks of CAVs and Human-Driven Vehicles (HDVs). In pursuit of this objective, we have introduced a multi-class dynamic traffic assignment framework that enhances network performance and offers insights into traffic dynamics. Additionally, our methodology considers dynamic routing behaviour while devising DLs, formulating and approximating the problem as a mixed-integer linear program (MILP). The resulting strategy delineates the temporal and spatial aspects of the deployment of DLs for CAVs, specifying the quantity and locations of these lanes. Subsequently, we assessed our framework using test-bed networks of varying sizes and demand profiles, evaluating the solution’s quality and the model’s adaptability to diverse traffic conditions. Our findings indicate that implementing DLs for CAVs can bolster vehicular throughput across the network while neglecting dynamic capacity variation in mixed traffic may yield misleading outcomes.

Effortful Swallow Maneuver and Modifications on Swallow Dynamics in Healthy Adults.

Effortful Swallow (ES) is a widely used technique in dysphagia management, believed to strengthen oropharyngeal muscles and enhance swallowing safety and efficiency. Although its impact on the oral phase of swallowing is well-documented, its effects on pharyngeal swallowing physiology remain inconsistent. This study aimed to elucidate the effects of ES on swallowing kinematics and timing, addressing existing inconsistencies in the literature. This study involved 22 healthy adults using archived videofluoroscopic swallowing studies. Participants performed three swallow trials of 10 ml puree under regular conditions and with the ES maneuver with a specific focus on pharyngeal squeezing. The outcome measures comprised of swallow timing and kinematic measures. Swallow timing parameters included time to maximum hyoid and laryngeal excursion, laryngeal vestibule closure reaction and duration, pharyngeal constriction duration, pharyngoesophageal segment (PES) opening duration, and swallow duration. Swallow kinematic parameters encompassed hyoid and laryngeal excursions, pharyngeal constriction ratio, and maximum PES width. All variables were analyzed via videofluoroscopy. Paired t-tests were used to examine the effect of ES on each outcome measure, with a significance threshold set at p <0.004. The ES maneuver significantly increased the duration of laryngeal vestibule closure, pharyngeal constriction, PES opening, and overall swallow duration. It also improved the pharyngeal constriction ratio. However, there were no significant changes in hyoid and laryngeal excursions. Submental muscle activity showed a notable increase during ES. The study demonstrates that ES can effectively modify certain temporal and kinematic aspects of swallowing, particularly by prolonging key phases and enhancing pharyngeal constriction. These findings suggest the potential utility of ES in dysphagia rehabilitation, especially in cases that require prolonged pharyngeal constriction, laryngeal vestibule closure, and PES opening. However, the limited impact on hyoid and laryngeal excursions along with laryngeal vestibule closure reaction time, indicates that ES may not address all aspects of dysphagia. The findings highlight the need for tailored therapeutic approaches in dysphagia management, considering individual physiological impairment profiles.

Reduced Late Positive Potentials to distress in individuals with high psychopathic traits during pain judgment tasks

This study examined the empathic processing of individuals with psychopathic traits and healthy controls in response to pain, applying affective perspective-taking (Self vs. Other). Twenty subjects with high psychopathic traits and twenty control subjects performed pain judgment tasks in the study. During the tasks, late positive potentials (LPPs) of the participants were measured to assess emotional processing in reaction to visual stimuli depicting painful or non-painful situations. In early LPP time stage (500-700ms), the control group and the psychopathic trait group exhibited comparable levels of empathic processing regarding pain. However, in late LPP time window (700-1,100ms), the control group showed a greater LPP amplitude to Pain stimuli than No-pain stimuli, whereas the psychopathic trait group exhibited non-significant amplitude differences between Pain and No-pain stimuli. These findings imply that individuals with high psychopathic traits may swiftly terminate the processing and encounter difficulties in reappraising distress cues, especially in the late stage, providing psychophysiological support for distinctive empathic processing with temporal aspects.

Evaluating the influence of functional unit on life cycle assessment (LCA) reliability of concrete

The linear lifecycle of concrete, from production through usage to disposal, highlights its status as one of the most carbon-, energy-, and resource-intensive processes in the industry. The development of life cycle assessment (LCA) models to quantify concrete’s embodied and buildings’ operational emissions is strongly influenced by the selection of the functional unit (FU) since it dictates the magnitude and significance of the environmental burdens from concrete and buildings' life span. The chosen FU should comprehensively capture the concrete's functionalities and construction parameters. Nonetheless, establishing a FU that encompasses spatial, temporal, and intensity aspects of cement/concrete and its applications remains a significant limitation in the practice of current LCA models. This study analyzed the FU influence on the environmental footprint of concrete samples with specific compressive strengths (material level) via LCA. The LCA was conducted in OneClick LCA using the environmental product declaration (EPD) provided by the software for each ready-mix concrete sample. From the LCA results, the C 30/37 concrete mix (lowest compressive strength) exhibited the lowest carbon footprint for all the environmental indicators when a volume-based FU was considered. In contrast, the results were more favorable for the C 65/70 concrete specimen when the compressive strength (28 curing days) was chosen because less cement is needed to achieve one MPa of strength. This behavior was also observed in the modeling of a column in a building, where the mixture with the highest compressive strength (C 65/70) showed the lowest environmental impact for all the indicators. However, this assessment does not represent the actual building system's footprint. Thus, regionally specific functional units need to be established, considering not just concrete functionalities but also national regulations, stakeholder needs, and local resource availability.

Multi-objective optimization for perishable product dispatch in a FEFO system for a food bank single warehouse

One of the main challenges of food bank warehouses in developing countries is to determine how to allocate perishable products to beneficiary agencies with different expiry dates while ensuring food safety, meeting nutritional requirements, and minimizing the shortage. The contribution of this research is to introduce a new multi-objective, multi-product, and multi-period perishable food allocation problem based on a single warehouse management system for a First Expired-First Out (FEFO) policy. Moreover, it incorporates the temporal aspect, guaranteeing the dispatch of only those perishable products that meet the prescribed minimum quality standards. A weighted sum approach converts the multi-objective problem of minimizing a vector of objective functions into a scalar problem by constructing a weighted sum of all the objectives. The problem can then be solved using a standard constrained optimization procedure. The proposed mixed integer linear model is solved by using the CPLEX solver. The solution obtained from the multi-objective problem allows us to identify days and products experiencing shortages. In such cases, when there is insufficient available inventory, the total quantity of product to be dispatched is redistributed among beneficiaries according to a pre-established prioritization. These redistributions are formulated as integer programming problems using a score-based criterion and solved by an exact method based on dynamic programming. Computational results demonstrate the applicability of the novel model for perishable items to a real-world study case.

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

The rapid pace of urbanization and the increasing concentration of populations in urban areas have generated a substantial demand for architectural structures, resulting in a significant increase in building stock and continuous material flows that interact with the environment. This study emphasizes the importance of high-spatial-resolution mapping of residential building stock for effective urban-construction resource management, planning, and waste management. Focusing on Xi’an as a case study, the research develops a comprehensive framework for mapping urban residential building stock by integrating diverse data dimensions, including temporal, spatial, network, and multi-attribute aspects. The findings indicate that between 1990 and 2020, approximately 4758 residential communities were established in central Xi’an. The analysis of seven key residential construction materials revealed that the building stock escalated from 1.53 million tons to 731.12 million tons, with a steady spatial expansion of material distribution. The study attributes this growth to factors such as population increase, economic advancement, and policy initiatives, which, in turn, have driven the demand for residential building materials and reinforced the interdependence between urban expansion and residential construction development. Remarkably, from 1990 to 2020, the population surged by 2.1-fold, the economy by 66-fold, and the stock of residential building materials by 477-fold, indicating that the growth rate of material stock consistently outpaced that of both population and economic growth. Over the past three decades, the rapid expansion of residential buildings has led to the encroachment of urban ecological spaces by concrete structures. The methodology proposed in this study for quantifying building material offers valuable insights for policymakers and urban and environmental planners to foster responsible resource consumption and supports component-level circularity in the built environment.

The theory of high hazard (HHT) based on grounded theory method and theories integration

PurposeOrganizations working in high-hazard environments contribute significantly to modern society and the economy, not only for the valuable resources they hold but also for the indispensable products and services they provide, such as power generation, transportation and defense weapons. Therefore, the main purpose of this study is to develop a framework that outlines future research on systems safety and provides a better understanding of how organizations can effectively manage hazard events.Design/methodology/approachIn this research, we developed the high hazard theory (HHT) and a theoretical framework based on the grounded theory method (GTM) and the integration of three established theoretical perspectives: normal accident theory (NAT), high reliability theory (HRT) and resilience engineering (RE) theory.FindingsWe focused on the temporal aspect of accidents to create a timeline showing the progression of hazard events and the factors contributing to safety and hazards in organizations. Given the limitations of the previous theories in providing a coherent explanation of hazard event escalation in high-hazard organizations (HHOs), we argue that the highlighted theories can be more complementary than contradictory regarding their standpoints on disasters and accident prevention.Practical implicationsA proper appreciation of the hazard nature of organizations can help reduce their susceptibility to failure, prevent outages and breakdowns of systems, identify areas for improvement and develop strategies to enhance performance.Originality/valueBy developing HHT, we contribute to systems safety research by developing a new, refined theory and enrich the theoretical debate. We also expand the understanding of scholars and practitioners about the characteristics of organizations working in high-hazard environments.

Gain and loss cycles revisited: What to consider when testing key assumptions of conservation of resources theory

In this editorial, we discuss approaches to the empirical test of gain and loss cycles as described within Hobfoll's conservation of resources theory (COR). We present COR theory's basic assumptions about gain and loss cycles and critically discuss typical empirical studies that aim at testing these assumptions. We highlight conceptual and empirical problems when testing gain and loss cycles of resources and provide guidance for researchers with respect to theoretical, temporal, and data-analytic aspects. We offer some suggestions for constructive replications and reproducibility.

‘Social’ robot and social relations in care settings: Undefined positionality and fixed temporality

Socially assistive robots in eldercare settings, i.e. ‘social’ robots, are currently being promoted and studied, particularly in Japan. Among these studies, clinical evaluation of their effectiveness in experimental research predominates, while studies that critically and analytically describe relations between actors in long-term care settings where robots are present are growing but still scarce. In this paper, we argue that understanding how older adults facing the loss of autonomy and their caregivers interact with robots in relational terms is helpful to explain the usage and non-usage of, and eventual resistance to social robots entering into care settings. This is why, in order to understand human relationships with social robots in elder care homes, this paper combines a ‘script’ approach and a ‘critical care’ one. Based on content analysis of a unique set of reports on trials of a social robot in multiple care facilities in Japan from 2018 to 2020, as well as interviews with stakeholders and ethnographic observations conducted in 2022, we answer two questions: 1) how do social robots shape social relations of care? 2) how do social robots affect social relations between care professionals, care recipients and robots? We proceed to discuss two of the characteristics emphasised in the ethics of care and examine them in depth: the positional and temporal aspects. Our major result is that, while the robot was easily adaptable to care environments because its position in relationships is not defined, embedded clock time flow in the robot was objective and linear, which indicates that some robot-created situations were seen as problematic by actors in care settings.

Video Ethogram of Equine Social Behaviour.

Equine social behaviour studies face challenges stemming from the absence of a comprehensive ethogram with unequivocal standardised definitions and the resulting limits to data comparison across studies. To address these constraints, this ethogram offers researchers a standardised framework, defining thirty-seven distinct equine social behaviours supplemented by video examples for enhanced clarity. These definitions amalgamate insights from existing ethograms and are fine-tuned through meticulous video observations, encompassing contextual cues such as distinguishing between aggressive and playful circling based on ear position and facial expressions and communicative nuances to provide a detailed representation of equine social behaviours. Video recordings complement the standardised definitions by capturing the dynamic flow and sequence of social interactions. By providing a dynamic and detailed representation, videos allow researchers to observe the temporal aspects of behaviour, including the sequence, duration, and rhythm of interactions. These detailed data are crucial for interpreting social behaviours and unravelling the complexities of equine societies. Standardized and video-illustrated definitions of equine social behaviour facilitate clear and consistent communication between researchers, enabling cross-study comparisons regarding the impact of husbandry practices and health conditions on equine social behaviour, which, in turn, can facilitate the assessment and optimisation of management practices and equine welfare.

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks.

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects' hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p < 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation.

Making Order in Household Accounting - Digital Invoices as Domestic Work Artifacts

AbstractThe digitization of financial activities in consumers' lives is increasing, and the digitalization of invoicing processes is expected to play a significant role, although this area is not well understood regarding the private sector. Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW) research have a long history of analyzing the socio-material and temporal aspects of work practices that are relevant for the domestic domain. The socio-material structuring of invoicing work and the working styles of consumers must be considered when designing effective consumer support systems. In this ethnomethodologically-informed, design-oriented interview study, we followed 17 consumers in their daily practices of dealing with invoices to make the invisible administrative work involved in this process visible. We identified and described the meaningful artifacts that were used in a spatial-temporal process within various storage locations such as input, reminding, intermediate (for postponing cases) buffers, and archive systems. Furthermore, we identified three different working styles that consumers exhibited: direct completion, at the next opportunity, and postpone as far as possible. This study contributes to our understanding of household economics and domestic workplace studies in the tradition of CSCW and has implications for the design of electronic invoicing systems.

Investigating silent pauses in connected speech: integrating linguistic, neuropsychological, and neuroanatomical perspectives across narrative tasks in post-stroke aphasia.

Silent pauses are regarded as integral components of the temporal organization of speech. However, it has also been hypothesized that they serve as markers for internal cognitive processes, including word access, monitoring, planning, and memory functions. Although existing evidence across various pathological populations underscores the importance of investigating silent pauses' characteristics, particularly in terms of frequency and duration, there is a scarcity of data within the domain of post-stroke aphasia. The primary objective of the present study is to scrutinize the frequency and duration of silent pauses in two distinct narrative tasks within a cohort of 32 patients with chronic post-stroke aphasia, in comparison with a control group of healthy speakers. Subsequently, we investigate potential correlation patterns between silent pause measures, i.e., frequency and duration, across the two narrative tasks within the patient group, their performance in neuropsychological assessments, and lesion data. Our findings showed that patients exhibited a higher frequency of longer-duration pauses in both narrative tasks compared to healthy speakers. Furthermore, within-group comparisons revealed that patients tended to pause more frequently and for longer durations in the picture description task, while healthy participants exhibited the opposite trend. With regard to our second research question, a marginally significant interaction emerged between performance in semantic verbal fluency and the narrative task, in relation to the location of silent pauses-whether between or within clauses-predicting the duration of silent pauses in the patient group. However, no significant results were observed for the frequency of silent pauses. Lastly, our study identified that the duration of silent pauses could be predicted by distinct Regions of Interest (ROIs) in spared tissue within the left hemisphere, as a function of the narrative task. Overall, this study follows an integrative approach of linguistic, neuropsychological and neuroanatomical data to define silent pauses in connected speech, and illustrates interrelations between cognitive components, temporal aspects of speech, and anatomical indices, while it further highlights the importance of studying connected speech indices using different narrative tasks.