Temporo-frontoparietal hypoconnectivity as a biomarker for isolated language impairment in mild cognitive impairment: A cross-cohort comparison.

INTRODUCTION: Whether brain functional connectivity (FC) is consistently disrupted in individuals with mild cognitive impairment (MCI) with isolated language impairment (ilMCI), and its potential to differentiate between MCI subtypes remains uncertain. METHODS: Cross-sectional data from 404 participants in two cohorts (the Chinese Preclinical Alzheimer's Disease Study and the Alzheimer's Disease Neuroimaging Initiative) were analyzed, including neuropsychological tests, resting-state functional magnetic resonance imaging (fMRI), cerebral amyloid positivity, and apolipoprotein E (APOE) status. RESULTS: Temporo-frontoparietal FC, particularly between the bilateral superior temporal pole and the left inferior frontal/supramarginal gyri, was consistently decreased in ilMCI compared to amnestic MCI (aMCI) and normal controls, which was correlated with semantic impairment. Using mean temporo-frontoparietal FC as a classifier could improve accuracy in identifying ilMCI subgroups with positive cerebral amyloid deposition and APOE risk alleles. DISCUSSION: Temporal-frontoparietal hypoconnectivity was observed in individuals with ilMCI, which may reflect semantic impairment and serve as a valuable biomarker to indicate potential mechanisms of underlying neuropathology. HIGHLIGHTS: Temporo-frontoparietal hypoconnectivity was observed in impaired language mild cognitive impairment (ilMCI). Temporo-frontoparietal hypoconnectivity may reflect semantic impairment. Temporo-frontoparietal functional connectivity can classify ilMCI subtypes.

The causal role of the subthalamic nucleus in the inhibitory network.

The neural network mediating successful response inhibition mainly includes right hemisphere activation of the pre-supplementary motor area, inferior frontal gyrus (IFG), subthalamic nucleus (STN), and caudate nucleus. However, the causal role of these regions in the inhibitory network is undefined. Five patients with Parkinson's disease were assessed prior to and after therapeutic thermal ablation of the right STN in two separate functional magnetic resonance imaging (fMRI) sessions while performing a stop-signal task. Initiation times were faster but motor inhibition with the left hand (contralateral to the lesion) was significantly impaired as evident in prolonged stop-signal reaction times. Reduced inhibition after right subthalamotomy was associated (during successful inhibition) with the recruitment of basal ganglia regions outside the established inhibitory network. They included the putamen and caudate together with the anterior cingulate cortex and IFG of the left hemisphere. Subsequent network connectivity analysis (with the seed over the nonlesioned left STN) revealed a new inhibitory network after right subthalamotomies. Our results highlight the causal role of the right STN in the neural network for motor inhibition and the possible basal ganglia mechanisms for compensation upon losing a key node of the inhibition network.

Windows of opportunity: how age and sex shape the influence of prenatal depression on the child brain.

Maternal prenatal depression can affect child brain and behavioural development. Specifically, altered limbic network structure and function is a likely mechanism through which prenatal depression impacts the life-long mental health of exposed children. While developmental trajectories are influenced by many factors that exacerbate risk or promote resiliency, the role of child age and sex in the relationship between prenatal depression and the child brain remains unclear. Here, we review studies of associations between prenatal depression and brain structure and function, with a focus on the role of age and sex in these relationships. After exposure to prenatal depression, altered amygdala, hippocampal and frontal cortical structure, as well as changes in functional and structural connectivity within the limbic network are evident during the fetal, infant, preschool, childhood, and adolescent stages of development. Sex appears to play a key role in this relationship, with evidence of differential findings particularly in infants, with males showing smaller and females larger hippocampal and amygdala volumes following prenatal depression. Longitudinal studies in this area have only begun to emerge within the last five years and will be key to understanding critical windows of opportunity. Future research focused on the role of age and sex in this relationship is essential to further inform screening, policy, and interventions for children exposed to prenatal depression, interrupt the intergenerational transmission of depression, and ultimately support healthy brain development.

Individualized prediction of online shopping addiction from whole-brain functional connectivity.

Online shopping addiction (OSA) is defined as a behavioral addiction where an individual exhibits an unhealthy and excessive attachment to shopping on the Internet. Since the OSA shown its adverse impacts on individuals' daily life and social functions, it is important to examine the neurobiological underpinnings of OSA that could be used in clinical practice to identify individuals with OSA. The present study addressed this question by employing a connectome-based prediction model approach to predict the OSA tendency of healthy subjects from whole-brain resting-state functional connectivity. The OSA connectome - a set of connections across multiple brain networks that contributed to predict individuals' OSA tendency was identified, including the functional connectivity between the frontal-parietal network (FPN) and cingulo-opercular network (CON) (i.e., positive network), as well as the functional connectivity within default mode network (DMN) and that between FPN and DMN (i.e., negative network). Key nodes that contributed to the prediction model included the middle frontal gyrus, inferior frontal gyrus, anterior cingulate cortex, and inferior temporal gyrus, which have been associated with impulsivity and emotional processing. Notably, this connectome has shown its specific role in predicting OSA by controlling for the influence of general Internet addiction. Moreover, the strength of the negative network mediated the relationship between OSA and impulsivity, highlighting that the negative network underlies the impulsivity characteristic of OSA. Together, these findings advanced our understanding of the neural correlates of OSA and provided a promising framework for diagnosing OSA.

Brain modulation after exergaming training in advanced forms of Parkinson's disease: a randomized controlled study.

BACKGROUND: Physical activity combined with virtual reality and exergaming has emerged as a new technique to improve engagement and provide clinical benefit for gait and balance disorders in people with Parkinson's disease (PD). OBJECTIVE: To investigate the effects of a training protocol using a home-based exergaming system on brain volume and resting-state functional connectivity (rs-FC) in persons with PD. METHODS: A single blind randomized controlled trial was conducted in people with PD with gait and/or balance disorders. The experimental (active) group performed 18 training sessions at home by playing a custom-designed exergame with full body movements, standing in front of a RGB-D Kinect® motion sensor, while the control group played using the computer keyboard. Both groups received the same training program. Clinical scales, gait recordings, and brain MRI were performed before and after training. We assessed the effects of both training on both the grey matter volumes (GVM) and rs-FC, within and between groups. RESULTS: Twenty-three patients were enrolled and randomly assigned to either the active (n = 11) or control (n = 12) training groups. Comparing pre- to post-training, the active group showed significant improvements in gait and balance disorders, with decreased rs-FC between the sensorimotor, attentional and basal ganglia networks, but with an increase between the cerebellar and basal ganglia networks. In contrast, the control group showed no significant changes, and rs-FC significantly decreased in the mesolimbic and visuospatial cerebellar and basal ganglia networks. Post-training, the rs-FC was greater in the active relative to the control group between the basal ganglia, motor cortical and cerebellar areas, and bilaterally between the insula and the inferior temporal lobe. Conversely, rs FC was lower in the active relative to the control group between the pedunculopontine nucleus and cerebellar areas, between the temporal inferior lobes and the right thalamus, between the left putamen and dorsolateral prefrontal cortex, and within the default mode network. CONCLUSIONS: Full-body movement training using a customized exergame induced brain rs-FC changes within the sensorimotor, attentional and cerebellar networks in people with PD. Further research is needed to comprehensively understand the neurophysiological effects of such training approaches. Trial registration ClinicalTrials.gov NCT03560089.

On tricyclic graphs with maximum atom-bond sum-connectivity index.

The sum-connectivity, Randic, and atom-bond connectivity indices have a prominent place among those topological indices that depend on the graph's vertex degrees. The ABS (atom-bond sum-connectivity) index is a variant of all the aforementioned three indices, which was recently put forward. Let T ( n ) be the class of all connected tricyclic graphs of order n. Recently, the problem of determining graphs from T ( n ) having the least possible value of the ABS index was solved in (Zuo et al., 2024 [39]) for the case when the maximum degree of the considered graphs does not exceed 4. The present paper addresses the problem of finding graphs from T ( n ) having the largest possible value of the ABS index for n ≥ 5 .

High segregation and diminished global integration in large-scale brain functional networks enhances the perceptual binding of cross-modal stimuli.

Speech perception requires the binding of spatiotemporally disjoint auditory-visual cues. The corresponding brain network-level information processing can be characterized by two complementary mechanisms: functional segregation which refers to the localization of processing in either isolated or distributed modules across the brain, and integration which pertains to cooperation among relevant functional modules. Here, we demonstrate using functional magnetic resonance imaging recordings that subjective perceptual experience of multisensory speech stimuli, real and illusory, are represented in differential states of segregation-integration. We controlled the inter-subject variability of illusory/cross-modal perception parametrically, by introducing temporal lags in the incongruent auditory-visual articulations of speech sounds within the McGurk paradigm. The states of segregation-integration balance were captured using two alternative computational approaches. First, the module responsible for cross-modal binding of sensory signals defined as the perceptual binding network (PBN) was identified using standardized parametric statistical approaches and their temporal correlations with all other brain areas were computed. With increasing illusory perception, the majority of the nodes of PBN showed decreased cooperation with the rest of the brain, reflecting states of high segregation but reduced global integration. Second, using graph theoretic measures, the altered patterns of segregation-integration were cross-validated.

Post-treatment alterations in white matter integrity in PTSD: Effects on symptoms and functional connectivity a secondary analysis of an RCT.

Post-traumatic stress disorder (PTSD) has been linked to altered communication within the limbic system, including reduced structural connectivity in the uncinate fasciculus (UNC; i.e., decreased fractional anisotropy; FA) and reduced resting-state functional connectivity (RSFC) between the hippocampus and ventromedial prefrontal cortex (vmPFC). Previous research has demonstrated attenuation of PTSD symptoms and alterations in RSFC following exposure-based psychotherapy. However, the relationship between changes in structural and functional connectivity patterns and PTSD symptoms following treatment remains unclear. To investigate this, we conducted a secondary analysis of data from a randomized clinical trial of intensive exposure therapy, evaluating alterations in UNC FA, hippocampus-vmPFC RSFC, and PTSD symptoms before (pre-treatment), 7 days after (post-treatment), and 30 days after (follow-up) the completion of therapy. Our results showed that post-treatment changes in RSFC were positively correlated with post-treatment and follow-up changes in UNC FA and that post-treatment changes in UNC FA were positively correlated with post-treatment and follow-up changes in PTSD symptoms. These findings suggest that early changes in functional connectivity are associated with sustained changes in anatomical connectivity, which in turn are linked to reduced PTSD symptom severity.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

AIMS: Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. METHODS: We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. RESULTS: Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. CONCLUSION: These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Spatiotemporal whole-brain activity and functional connectivity of melodies recognition.

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Birds influence vegetation coverage and structure on sandy biogeomorphic islands in the Dutch Wadden Sea.

Small uninhabited islands form important roosting and breeding habitats for many coastal birds. Previous studies have demonstrated that guano can promote ecosystem productivity and functionality on island ecosystems. Here, we assess the role of external nutrient input by coastal birds on the vegetation structure and coverage on sandy biogeomorphic islands, where island-forming processes depend on vegetation-sedimentation feedbacks. As a first step, we investigated whether breeding birds affect vegetation productivity on sandy back-barrier islands in the Wadden Sea. Using a combination of bird observations and plant stable isotope (δ15N) analyses, we demonstrate that (i) breeding birds transport large quantities of nutrients via their faecal outputs to these islands annually and that (ii) this external nitrogen source influences vegetation development on these sandy, nutrient-limited, islands. Based on these results we discuss how this avian nutrient pump could impact island development and habitat suitability for coastal birds and discuss future directions for research. In general, we conclude that avian subsidies have the potential to affect both the ecological and biogeomorphic functioning of coastal soft-sediment systems. However, the strength and scale of especially these biogeomorphic interactions are not fully understood. For the conservation of both threatened coastal birds and sandy back-barrier islands and the design of appropriate management strategies, we argue that three-way interactions between birds, vegetation and sandy island morphodynamics need to be further elucidated.

Impacts of management practices on habitat selection during juvenile mountain lion dispersal.

Dispersal is a complex series of movements before an individual establishes a home range. Animals must travel and forage in unfamiliar landscapes that include anthropogenic risks such as road crossings, harvest, and urban landscapes. We compare dispersal behavior of juvenile mountain lions (Puma concolor) from two geographically distinct populations in California and Nevada, USA. These two sites are ecologically similar but have different management practices; hunting is permitted in Nevada, whereas mountain lions are protected in California. We used GPS-collar data and net-squared displacement analysis to identify three dispersal states: exploratory, departure, and transient home range. We then compared each dispersal state of the two mountain lion populations using an integrated step selection analysis (iSSA). The model included explanatory variables hypothesized to influence one or more dispersal states, including distance to forest, shrub, water, hay and crop, developed lands, and four-wheel drive roads, as well as elevation and terrain ruggedness. Results revealed consistent habitat selection between sites across most landscape variables, with one notable exception: anthropogenic covariates, including distance to developed land, distance to hay and crop, and distance to four-wheeled drive roads, were only statistically significant on modeled habitat selection during dispersal in the population subject to hunting (i.e., Nevada). Results suggest that hunting (pursuit with hounds resulting in harvest) and non-lethal pursuit (pursuit with hounds but no harvest allowed) increase avoidance of anthropogenic landscapes during dispersal for juvenile mountain lions. By comparing populations, we provided valuable insights into the role of management in shaping dispersal behavior.

Cortical hubs of highly superior autobiographical memory.

Highly Superior Autobiographical Memory (HSAM) is a rare form of enhanced memory in which individuals demonstrate an extraordinary ability to remember details of their personal lives with high levels of accuracy and vividness. Neuroimaging studies have identified brain regions - specifically, midline areas within the default network - associated with remembering events from one's past. Extending this research on the neural underpinnings of autobiographical memory, the present study utilizes graph theory analyses to compare functional brain connectivity in a cohort of HSAM (n = 12) and control participants (n = 29). We perform seed-based analysis in resting-state fMRI data to assess how specific cortical regions within the autobiographical memory network are differentially connected in HSAM individuals. Additionally, we apply a whole-brain connectivity analysis to identify differences in brain hub-network topology associated with enhanced autobiographical memory. Seed-based results show converging patterns of increased connectivity in HSAM across midline areas. Whole-brain analysis also reveals enhanced connectivity across medial prefrontal and posterior cingulate cortex in HSAM individuals. Together, these results extend prior research, highlighting cortical hubs within the default network associated with enhanced autobiographical memory.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis.

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM.

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the "STEW" dataset, which consists of two tasks, namely "No task" and "simultaneous capacity (SIMKAP)-based multitasking activity". We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

Dynamic functional network connectivity in patients with a mismatch between white matter hyperintensity and cognitive function.

Objective: White matter hyperintensity (WMH) in patients with cerebral small vessel disease (CSVD) is strongly associated with cognitive impairment. However, the severity of WMH does not coincide fully with cognitive impairment. This study aims to explore the differences in the dynamic functional network connectivity (dFNC) of WMH with cognitively matched and mismatched patients, to better understand the underlying mechanisms from a quantitative perspective. Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive function scale assessment of the patients were acquired. Preprocessing of the rs-fMRI data was performed, and this was followed by dFNC analysis to obtain the dFNC metrics. Compared the dFNC and dFNC metrics within different states between mismatch and match group, we analyzed the correlation between dFNC metrics and cognitive function. Finally, to analyze the reasons for the differences between the mismatch and match groups, the CSVD imaging features of each patient were quantified with the assistance of the uAI Discover system. Results: The 149 CSVD patients included 20 cases of "Type I mismatch," 51 cases of Type I match, 38 cases of "Type II mismatch," and 40 cases of "Type II match." Using dFNC analysis, we found that the fraction time (FT) and mean dwell time (MDT) of State 2 differed significantly between "Type I match" and "Type I mismatch"; the FT of States 1 and 4 differed significantly between "Type II match" and "Type II mismatch." Correlation analysis revealed that dFNC metrics in CSVD patients correlated with executive function and information processing speed among the various cognitive functions. Through quantitative analysis, we found that the number of perivascular spaces and bilateral medial temporal lobe atrophy (MTA) scores differed significantly between "Type I match" and "Type I mismatch," while the left MTA score differed between "Type II match" and "Type II mismatch." Conclusion: Different mechanisms were implicated in these two types of mismatch: Type I affected higher-order networks, and may be related to the number of perivascular spaces and brain atrophy, whereas Type II affected the primary networks, and may be related to brain atrophy and the years of education.

Aberrant functional connectivity in anterior cingulate gyrus subregions in migraine without aura patients.

Background: The anterior cingulate gyrus (ACG) is an important regulatory region for pain-related information. However, the ACG is composed of subregions with different functions. The mechanisms underlying the brain networks of different subregions of the ACG in patients with migraine without aura (MwoA) are currently unclear. Methods: In the current study, resting-state functional magnetic resonance imaging (rsfMRI) and functional connectivity (FC) were used to investigate the functional characteristics of ACG subregions in MwoA patients. The study included 17 healthy volunteers and 28 MwoA patients. The FC calculation was based on rsfMRI data from a 3 T MRI scanner. The brain networks of the ACG subregions were compared using a general linear model to see if there were any differences between the two groups. Spearman correlation analysis was used to examine the correlation between FC values in abnormal brain regions and clinical variables. Results: Compared with healthy subjects, MwoA patients showed decreased FC between left subgenual ACG and left middle cingulate gyrus and right middle temporal gyrus. Meanwhile, MwoA patients also showed increased FC between pregenual ACG and right angular gyrus and increased FC between right pregenual ACG and right superior occipital gyrus. The FC values between pregenual ACG and right superior occipital gyrus were significantly positively correlated with the visual analogue scale. Conclusion: Disturbances of FC between ACG subregions and default model network and visual cortex may play a key role in neuropathological features, perception and affection of MwoA. The current study provides further insights into the complex scenario of MwoA mechanisms.

E-consultation as existential media: Exploring doctor-patient 'digital thrownness' in Danish general practice.

In this article we use an existential media framework to explore the asynchronous, written and digital form of GP-patient communication that takes place through e-consultations in a Danish general practice context. This approach acknowledges e-consultation as more than a tool for information delivery and frames GP and patient not as skilful media users but as dependent co-existers: Both thrown into and trying to navigate the digital healthcare ecology. Through a thematic analysis of 38 semi-structured qualitative interviews with patients and GPs we carve out three themes unpacking the existential dimensions of e-consultation: 1. Patient and GP are placed in a Culture of non-stop connectivity and we show the ambivalences arising herein fostering both relief, reassurance and new insecurities. 2. Ethical challenges of responsible co-existence points to dilemmas of boundary setting and caring for self and co-exister in the digital encounter. 3. We-experiences illustrates the potential of e-consultation to signal GP presence, even when the GP is silent. We also discuss the existential ethics of care emerging from the contemporary digital healthcare ecology and call for empirically grounded studies of the existential dimensions tied to encounters in contemporary digital care infrastructures.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke.

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Inter- and intra-subject similarity in network functional connectivity across a full narrative movie.

Naturalistic paradigms, such as watching movies during functional magnetic resonance imaging, are thought to prompt the emotional and cognitive processes typically elicited in real life situations. Therefore, naturalistic viewing (NV) holds great potential for studying individual differences. Previous studies have primarily focused on using shorter movie clips, geared toward eliciting specific and often isolated emotions, while the potential behind using full narratives depicted in commercial movies as a proxy for real-life experiences has barely been explored. Here, we offer preliminary evidence that a full narrative movie (FNM), that is, a movie covering a complete narrative arc, can capture complex socio-affective dynamics and their links to individual differences. Using the studyforrest dataset, we investigated inter- and intra-subject similarity in network functional connectivity (NFC) of 14 meta-analytically defined networks across a full narrative, audio-visual movie split into eight consecutive movie segments. We characterized the movie segments by valence and arousal portrayed within the sequences, before utilizing a linear mixed model to analyze which factors explain inter- and intra-subject similarity. Our results show that the model best explaining inter-subject similarity comprised network, movie segment, valence and a movie segment by valence interaction. Intra-subject similarity was influenced significantly by the same factors and an additional three-way interaction between movie segment, valence and arousal. Overall, inter- and intra-subject similarity in NFC were sensitive to the ongoing narrative and emotions in the movie. We conclude that FNMs offer complex content and dynamics that might be particularly valuable for studying individual differences. Further characterization of movie features, such as the overarching narratives, that enhance individual differences is needed for advancing the potential of NV research.