Explore the vast range of titles available.

This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Studies were included if they assessed cognitive states using physiological signals and applied machine learning (ML) or deep learning (DL) techniques in practical task settings. The review highlights a pivotal shift from shallow ML to DL approaches for analysing physiological signals, driven by DL’s ability to autonomously learn complex patterns in large datasets. By 2023, DL has become the dominant methodology, though traditional ML techniques remain relevant. Additionally, there has been a move from neuroimaging to multimodal physiological modalities, with the decrease in neuroimaging use reflecting a trend towards integrating various physiological signals for more comprehensive insights. Cognitive state recognition is applied across diverse domains such as the automotive, aviation, maritime, and healthcare industries, enhancing performance and safety in high-stakes environments. Electrocardiogram (ECG) is the most utilised modality, with convolutional neural networks (CNNs) being the primary DL approach. The trend in cognitive state recognition research is moving towards integrating ECG signals with CNNs and adopting privacy-preserving methodologies like differential privacy and federated learning, highlighting the potential of cognitive state recognition to enhance performance, safety, and innovation across various real-world applications.

Sarcoidosis is a systemic disease of unknown cause that is characterised by the formation of immune granulomas in various organs, mainly the lungs and the lymphatic system. Studies show that sarcoidosis might be the result of an exaggerated granulomatous reaction after exposure to unidentified antigens in individuals who are genetically susceptible. Several new insights have been made, particularly with regards to the diagnosis and care of some important manifestations of sarcoidosis. The indications for endobronchial ultrasound in diagnosis and for PET in the assessment of inflammatory activity are now better specified. Recognition of unexplained persistent disabling symptoms, fatigue, small-fibre neurological impairment, cognitive failure, and changes to health state and quality of life, has improved. Mortality in patients with sarcoidosis is higher than that of the general population, mainly due to pulmonary fibrosis. Predicted advances for the future are finding the cause of sarcoidosis, and the elucidation of relevant biomarkers, reliable endpoints, and new efficient treatments, particularly in patients with refractory sarcoidosis, lung fibrosis, and those with persistent disabling symptoms.

Existing computerized cognitive tests (CCTs) lack speech recognition, which limits their assessment of language function. Therefore, we developed CogMo, a self-administered CCT that uses automatic speech recognition (ASR) to assess multi-domain cognitive functions, including language. This study investigated the validity and reliability of CogMo in discriminating cognitive impairments. CogMo automatically provides CCT results; however, manual scoring using recorded audio was performed to verify its ASR accuracy. The mini–mental state examination (MMSE) was used to assess cognitive functions. Pearson’s correlation was used to analyze the relationship between the MMSE and CogMo results, intraclass correlation coefficient (ICC) was used to evaluate the test-retest reliability of CogMo, and receiver operating characteristic (ROC) analysis validated its diagnostic accuracy for cognitive impairments. Data of 100 participants (70 with normal cognition, 30 with cognitive impairment), mean age 74.6±7.4 years, were analyzed. The CogMo scores indicated significant differences in cognitive levels for all test items, including manual and automatic scoring for the speech recognition test, and a very high correlation (r = 0.98) between the manual and automatic CogMo scores. Additionally, the total CogMo and MMSE scores exhibited a strong correlation (r = 0.89). Moreover, CogMo exhibited high test-retest reliability (ICC = 0.94) and ROC analysis yielded an area under the curve of 0.89 (sensitivity = 90.0%, specificity = 82.9%) at a cutoff value of 68.8 points. The CogMo demonstrated adequate validity and reliability for discriminating multi-domain cognitive impairment, including language function, in community-dwelling older adults.

Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive–compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual’s potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.

Background Mild Cognitive Impairment (MCI) is a frequent precursor to Alzheimer’s dementia (AD). Mitochondrial dysfunction, marked by reduced cytochrome c oxidase (CCO) activity and lower ATP production, is linked to these neurodegenerative diseases. This study evaluates transcranial photobiomodulation (tPBM), a non‐invasive technique using near‐infrared light to stimulate mitochondrial CCO, potentially enhancing neuronal energy and cognitive function in individuals with MCI. Method Twenty patients with mild cognitive impairment (MCI) were randomly assigned to an active treatment group (n = 10) or a sham control group (n = 10) using visually identical devices to maintain blinding. Participants completed daily home‐based tPBM sessions for 6 weeks. Pre‐ and post‐treatment assessments included cognitive tests (MMSE, TMT‐A & B, CVLT‐II) and biomarker evaluations (blood samples via ELISA). Neuroimaging included proton magnetic resonance spectroscopy (¹H‐MRS) of the posterior cingulate cortex (PCC) and whole‐brain structural and resting‐state functional MRI. Change scores were calculated by subtracting baseline from post‐treatment values. Result Compliance exceeded 98% in both groups, and tPBM was well‐tolerated. The active tPBM group showed significantly greater post‐treatment improvements from baseline (p < 0.05) compared to the sham group, including: (1) better recognition memory (higher long‐delay hits, fewer false positives on the CVLT‐II); (2) improved cognition (higher MMSE); (3) faster processing speed (shorter TMT‐B times); (4) decreased plasma IL‐6 levels; (5) higher choline/creatine ratio and a trend toward increased myoinositol/creatine in the PCC; (6) increased left nucleus‐accumbens volume; and (7) enhanced functional connectivity within the DMN and between the caudate and DMN, along with decreased FC within the limbic network. Conclusion Daily, home‐based tPBM is a well‐tolerated and feasible intervention that led to significant improvements in both cognitive function and biological markers in individuals with MCI. The active tPBM group demonstrated enhanced cognition, recognition memory, and processing speed, alongside reductions in inflammation and structural and functional brain changes, including increased neuroplasticity and alterations in brain connectivity. These findings suggest that tPBM may promote neuronal function and brain network modifications, particularly within the default mode network and limbic regions, providing evidence for its potential as a therapeutic approach in the early stages of Alzheimer's disease.

Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate neurodevelopmental disorders that are both characterized by difficulties in social cognition and social functioning. Due to methodological confounds, the degree of similarity in social cognitive impairments across these two disorders is currently unknown. This study therefore conducted a comprehensive comparison of social cognitive ability in ASD and SCZ to aid efforts to develop optimized treatment programs. In total, 101 individuals with ASD, 92 individuals with SCZ or schizoaffective disorder, and 101 typically developing (TD) controls, all with measured intelligence in the normal range and a mean age of 25.47 years, completed a large battery of psychometrically validated social cognitive assessments spanning the domains of emotion recognition, social perception, mental state attribution, and attributional style. Both ASD and SCZ performed worse than TD controls, and very few differences were evident between the two clinical groups, with effect sizes (Cohen's d) ranging from 0.01 to 0.34. For those effects that did reach statistical significance, such as greater hostility in the SCZ group, controlling for symptom severity rendered them non-significant, suggesting that clinical distinctions may underlie these social cognitive differences. Additionally, the strength of the relationship between neurocognitive and social cognitive performance was of similar, moderate size for ASD and SCZ. Findings largely suggest comparable levels of social cognitive impairment in ASD and SCZ, which may support the use of existing social cognitive interventions across disorders. However, future work is needed to determine whether the mechanisms underlying these shared impairments are also similar or if these common behavioral profiles may emerge via different pathways.

Folk psychology advocates the existence of gender differences in socio-cognitive functions such as 'reading' the mental states of others or discerning subtle differences in body-language. A female advantage has been demonstrated for emotion recognition from facial expressions, but virtually nothing is known about gender differences in recognizing bodily stimuli or body language. The aim of the present study was to investigate potential gender differences in a series of tasks, involving the recognition of distinct features from point light displays (PLDs) depicting bodily movements of a male and female actor. Although recognition scores were considerably high at the overall group level, female participants were more accurate than males in recognizing the depicted actions from PLDs. Response times were significantly higher for males compared to females on PLD recognition tasks involving (i) the general recognition of 'biological motion' versus 'non-biological' (or 'scrambled' motion); or (ii) the recognition of the 'emotional state' of the PLD-figures. No gender differences were revealed for a control test (involving the identification of a color change in one of the dots) and for recognizing the gender of the PLD-figure. In addition, previous findings of a female advantage on a facial emotion recognition test (the 'Reading the Mind in the Eyes Test' (Baron-Cohen, 2001)) were replicated in this study. Interestingly, a strong correlation was revealed between emotion recognition from bodily PLDs versus facial cues. This relationship indicates that inter-individual or gender-dependent differences in recognizing emotions are relatively generalized across facial and bodily emotion perception. Moreover, the tight correlation between a subject's ability to discern subtle emotional cues from PLDs and the subject's ability to basically discriminate biological from non-biological motion provides indications that differences in emotion recognition may - at least to some degree - be related to more basic differences in processing biological motion per se.

Midlife blood pressure is associated with structural brain changes, cognitive decline, and dementia in late life. However, the relationship between early adulthood blood pressure exposure, brain structure and function, and cognitive performance in midlife is not known. A better understanding of these relationships in the preclinical stage may advance our mechanistic understanding of vascular contributions to late-life cognitive decline and dementia and may provide early therapeutic targets. To identify resting-state functional connectivity of executive control networks (ECNs), a group independent components analysis was performed of functional MRI scans of 600 individuals from the Coronary Artery Risk Development in Young Adults longitudinal cohort study, with cumulative systolic blood pressure (cSBP) measured at nine visits over the preceding 30 y. Dual regression analysis investigated performance-related connectivity of ECNs in 578 individuals (mean age 55.5 ± 3.6 y, 323 female, 243 Black) with data from the Stroop color–word task of executive function. Greater connectivity of a left ECN to the bilateral anterior gyrus rectus, right posterior orbitofrontal cortex, and nucleus accumbens was associated with better executive control performance on the Stroop. Mediation analyses showed that while the relationship between cSBP and Stroop performance was mediated by white matter hyperintensities (WMH), resting-state connectivity of the ECN mediated the relationship between WMH and executive function. Increased connectivity of the left ECN to regions involved in reward processing appears to compensate for the deleterious effects of WMH on executive function in individuals across the burden of cumulative systolic blood pressure exposure in midlife.

Despite considerable research into whether face perception is impaired in autistic individuals, clear answers have proved elusive. In the present study, we sought to determine whether co-occurring alexithymia (characterized by difficulties interpreting emotional states) may be responsible for face-perception deficits previously attributed to autism. Two experiments were conducted using psychophysical procedures to determine the relative contributions of alexithymia and autism to identity and expression recognition. Experiment 1 showed that alexithymia correlates strongly with the precision of expression attributions, whereas autism severity was unrelated to expression-recognition ability. Experiment 2 confirmed that alexithymia is not associated with impaired ability to detect expression variation; instead, results suggested that alexithymia is associated with difficulties interpreting intact sensory descriptions. Neither alexithymia nor autism was associated with biased or imprecise identity attributions. These findings accord with the hypothesis that the emotional symptoms of autism are in fact due to co-occurring alexithymia and that existing diagnostic criteria may need to be revised.

INTRODUCTION We investigated the validity, feasibility, and effectiveness of a voice recognition‐based digital cognitive screener (DCS), for detecting dementia and mild cognitive impairment (MCI) in a large‐scale community of elderly participants. METHODS Eligible participants completed demographic, cognitive, functional assessments and the DCS. Neuropsychological tests were used to assess domain‐specific and global cognition, while the diagnosis of MCI and dementia relied on the Clinical Dementia Rating Scale. RESULTS Among the 11,186 participants, the DCS showed high completion rates (97.5%) and a short administration time (5.9 min) across gender, age, and education groups. The DCS demonstrated areas under the receiver operating characteristics curve (AUCs) of 0.95 and 0.83 for dementia and MCI detection, respectively, among 328 participants in the validation phase. Furthermore, the DCS resulted in time savings of 16.2% to 36.0% compared to the Mini‐Mental State Examination (MMSE) and Montral Cognitive Assessment (MoCA). DISCUSSION This study suggests that the DCS is an effective and efficient tool for dementia and MCI case‐finding in large‐scale cognitive screening. Highlights To our best knowledge, this is the first cognitive screening tool based on voice recognition and utilizing conversational AI that has been assessed in a large population of Chinese community‐dwelling elderly. With the upgrading of a new multimodal understanding model, the DCS can accurately assess participants' responses, including different Chinese dialects, and provide automatic scores. The DCS not only exhibited good discriminant ability in detecting dementia and MCI cases, it also demonstrated a high completion rate and efficient administration regardless of gender, age, and education differences. The DCS is economically efficient, scalable, and had a better screening efficacy compared to the MMSE or MoCA, for wider implementation.