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BACKGROUND: Major Depressive Disorder (MDD) is one of the most prevalent psychiatric disorders, and involves high relapse rates in which persistent negative thinking and rumination (i.e., perseverative cognition [PC]) play an important role. Positive fantasizing and mindfulness are common evidence-based psychological interventions that have been shown to effectively reduce PC and subsequent depressive relapse. How the interventions cause changes in PC over time, is unknown, but likely differ between the two. Whereas fantasizing may change the valence of thought content, mindfulness may operate through disengaging from automatic thought patterns. Comparing mechanisms of both interventions in a clinical sample and a non-clinical sample can give insight into the effectivity of interventions for different individuals. The current study aims to 1) test whether momentary psychological and psychophysiological indices of PC are differentially affected by positive fantasizing versus mindfulness-based interventions, 2) test whether the mechanisms of change by which fantasizing and mindfulness affect PC differ between remitted MDD versus never-depressed (ND) individuals, and 3) explore potential moderators of the main effects of the two interventions (i.e., what works for whom). METHODS: In this cross-over trial of fantasizing versus mindfulness interventions, we will include 50 remitted MDD and 50 ND individuals. Before the start of the measurements, participants complete several individual characteristics. Daily-life diary measures of thoughts and feelings (using an experience sampling method), behavioural measures of spontaneous thoughts (using the Sustained Attention to Response Task), actigraphy, physiological measures (impedance cardiography, electrocardiography, and electroencephalogram), and measures of depressive mood (self-report questionnaires) are performed during the week before (pre-) the interventions and the week during (peri-) the interventions. After a wash-out of at least one month, pre- and peri-intervention measures for the second intervention are repeated. DISCUSSION: This is the first study integrating self-reports, behavioural-, and physiological measures capturing dynamics at multiple time scales to examine the differential mechanisms of change in PC by psychological interventions in individuals remitted from multiple MDD episodes and ND individuals. Unravelling how therapeutic techniques affect PC in remitted individuals might generate insights that allows development of personalised targeted relapse prevention interventions. TRIAL REGISTRATION: ClinicalTrials.gov: NCT06145984, November 16, 2023.
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Transtorno Depressivo Maior , Atenção Plena , Humanos , Atenção Plena/métodos , Depressão/psicologia , Transtorno Depressivo Maior/prevenção & controle , Transtorno Depressivo Maior/psicologia , Estudos Cross-Over , Cognição , Recidiva , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
Performing a cognitive task requires going through a sequence of functionally diverse stages. Although it is typically assumed that these stages are characterized by distinct states of cortical synchrony that are triggered by sub-cortical events, little reported evidence supports this hypothesis. To test this hypothesis, we first identified cognitive stages in single-trial MEG data of an associative recognition task, showing with a novel method that each stage begins with local modulations of synchrony followed by a state of directed functional connectivity. Second, we developed the first whole-brain model that can simulate cortical synchrony throughout a task. The model suggests that the observed synchrony is caused by thalamocortical bursts at the onset of each stage, targeted at cortical synapses and interacting with the structural anatomical connectivity. These findings confirm that cognitive stages are defined by distinct states of cortical synchrony and explains the network-level mechanisms necessary for reaching stage-dependent synchrony states.
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Encéfalo , Tálamo , CogniçãoRESUMO
Mind-wandering is a ubiquitous mental phenomenon that is defined as self-generated thought irrelevant to the ongoing task. Mind-wandering tends to occur when people are in a low-vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind-wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self-reported mind-wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind-wandering above-chance level, while a classifier trained on self-reports of mind-wandering was able to do so. This suggests that mind-wandering is a mental state different from low vigilance or performing tasks with low demands-both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source-localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine-learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.
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Atenção , Pensamento , Eletroencefalografia , Humanos , Aprendizado de Máquina , VigíliaRESUMO
Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive because it is difficult to track when someone's mind is wandering based only on behavior. The goal of this study is to develop a machine-learning classifier that can determine someone's mind-wandering state online using electroencephalography (EEG) in a way that generalizes across tasks. In particular, we trained machine-learning models on EEG markers to classify the participants' current state as either mind-wandering or on-task. To be able to examine the task generality of the classifier, two different paradigms were adopted in this study: a sustained attention to response task (SART) and a visual search task. In both tasks, probe questions asking for a self-report of the thoughts at that moment were inserted at random moments, and participants' responses to the probes were used to create labels for the classifier. The 6 trials preceding an off-task response were labeled as mind-wandering, whereas the 6 trials predicting an on-task response were labeled as on-task. The EEG markers used as features for the classifier included single-trial P1, N1, and P3, the power and coherence in the theta (4-8 Hz) and alpha (8.5-12 Hz) bands at PO7, Pz, PO8, and Fz. We used a support vector machine as the training algorithm to learn the connection between EEG markers and the current mind-wandering state. We were able to distinguish between on-task and off-task thinking with an accuracy ranging from 0.50 to 0.85. Moreover, the classifiers were task-general: The average accuracy in across-task prediction was 60%, which was above chance level. Among all the extracted EEG markers, alpha power was most predictive of mind-wandering.
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Atenção/fisiologia , Eletroencefalografia/métodos , Desempenho Psicomotor/fisiologia , Máquina de Vetores de Suporte , Pensamento/fisiologia , Adolescente , Adulto , Ritmo alfa/fisiologia , Feminino , Humanos , Masculino , Adulto JovemRESUMO
In this study, we investigated the time course and neural correlates of the retrieval process underlying visual working memory. We made use of a rare dataset in which the same task was recorded using both scalp electroencephalography (EEG) and Electrocorticography (ECoG), respectively. This allowed us to examine with great spatial and temporal detail how the retrieval process works, and in particular how the medial temporal lobe (MTL) is involved. In each trial, participants judged whether a probe face had been among a set of recently studied faces. With a method that combines hidden semi-Markov models and multivariate pattern analysis, the neural signal was decomposed into a sequence of latent cognitive stages with information about their durations on a trial-by-trial basis. Analyzed separately, EEG and ECoG data yielded converging results on discovered stages and their interpretation, which reflected 1) a brief pre-attention stage, 2) encoding the stimulus, 3) retrieving the studied set, and 4) making a decision. Combining these stages with the high spatial resolution of ECoG suggested that activity in the temporal cortex reflected item familiarity in the retrieval stage; and that once retrieval is complete, there is active maintenance of the studied face set in the decision stage in the MTL. During this same period, the frontal cortex guides the decision by means of theta coupling with the MTL. These observations generalize previous findings on the role of MTL theta from long-term memory tasks to short-term memory tasks.
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Lobo Frontal/fisiologia , Memória de Curto Prazo/fisiologia , Rememoração Mental/fisiologia , Lobo Temporal/fisiologia , Adulto , Córtex Cerebral/fisiologia , Eletrocorticografia , Eletroencefalografia , Humanos , Cadeias de Markov , Análise Multivariada , Vias Neurais/fisiologia , Fatores de Tempo , Adulto JovemRESUMO
Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single-trial basis with a novel hidden semi-Markov model multivariate pattern analysis (HSMM-MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4-9 Hz theta band. Familiarity is associated with frontal phase locking in the 9-14 Hz alpha band. Decision making is associated with frontal and temporo-central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left-temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right-temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM-MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition.
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Aprendizagem por Associação/fisiologia , Ondas Encefálicas/fisiologia , Córtex Cerebral/fisiologia , Cognição/fisiologia , Memória/fisiologia , Desempenho Psicomotor/fisiologia , Adolescente , Adulto , Eletroencefalografia/métodos , Feminino , Humanos , Masculino , Cadeias de Markov , Análise Multivariada , Adulto JovemRESUMO
We investigated how self-referential processing (SRP) affected self-generated thought in a complex working memory task (CWM) to test the predictions of a computational cognitive model. This model described self-generated thought as resulting from competition between task- and distracting processes, and predicted that self-generated thought interferes with rehearsal, reducing memory performance. SRP was hypothesized to influence this goal competition process by encouraging distracting self-generated thinking. We used a spatial CWM task to examine if SRP instigated such thoughts, and employed eye-tracking to examine rehearsal interference in eye-movement and self-generated thinking in pupil size. The results showed that SRP was associated with lower performance and higher rates of self-generated thought. Self-generated thought was associated with less rehearsal and we observed a smaller pupil size for mind wandering. We conclude that SRP can instigate self-generated thought and that goal competition provides a likely explanation for how self-generated thoughts arises in a demanding task.
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Atenção/fisiologia , Ego , Movimentos Oculares/fisiologia , Memória de Curto Prazo/fisiologia , Reconhecimento Visual de Modelos/fisiologia , Desempenho Psicomotor/fisiologia , Pupila/fisiologia , Pensamento/fisiologia , Adulto , Medições dos Movimentos Oculares , Feminino , Humanos , Masculino , Adulto JovemRESUMO
In contrast to our increasing knowledge of the role that oscillations in single brain regions play in cognition, very little is known about how coherence between oscillations in distant brain regions is related to information transmission. Here I present a cognitive modeling approach that can address that question. Specifically, I show how a model of the attentional blink implemented in the ACT-R cognitive architecture is related to the amplitude and coherence of EEG oscillations. The dynamics of the model's working memory resource is primarily associated with parietal 4-9 Hz theta oscillations, while the dynamics of the model's declarative memory, visual perception and procedural resources together are correlated with posterior theta oscillations. I further show that model predictions about inter-module communication during the processes of stimulus identification and target consolidation are associated with selective increases in coherence at the predicted points in time.
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Ondas Encefálicas/fisiologia , Encéfalo/fisiologia , Cognição/fisiologia , Rede Nervosa/fisiologia , Eletroencefalografia , Humanos , Modelos NeurológicosRESUMO
The information processing capacity of the human mind is limited, as is evidenced by the so-called 'attentional-blink' deficit. This deficit is believed to result from competition between stimuli for limited attentional resources. We examined to what extent advanced meditators can manipulate their attentional state and control performance on an attentional blink task. We compared the magnitude of the attentional blink between states of focused attention meditation (in which one focuses tightly on an object) and states of open monitoring meditation (in which one is simply aware of whatever comes into experience) in a sample of experienced meditators. We found a smaller attentional blink during open monitoring compared to focused attention meditation due to reduced T1 capture. Of note, this effect was only found for very experienced meditators (on average 10,704 h of experience). These data may suggest that very advanced practitioners can exert some control over their conscious experience.
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Intermitência na Atenção Visual/fisiologia , Meditação/métodos , Meditação/psicologia , Estimulação Luminosa/métodos , Tempo de Reação/fisiologia , Adolescente , Adulto , Análise de Variância , Budismo , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Países Baixos , Inquéritos e Questionários , Adulto JovemRESUMO
Coordinating actions with others is thought to require Theory of Mind (ToM): the ability to take perspective by attributing underlying intentions and beliefs to observed behavior. However, researchers have yet to establish a causal role for specific cognitive processes in coordinated action. Since working memory load impairs ToM in single-participant paradigms, we tested whether load manipulation affects two-person coordination. We used EEG to measure P3, an assessment of working memory encoding, as well as inter-brain synchronization (IBS), which is thought to capture mutual adjustment of behavior and mental states during coordinated action. In a computerized coordination task, dyads were presented with novel abstract images and tried selecting the same image, with selections shown at the end of each trial. High working memory load was implemented by a concurrent n-back task. Compared with a low-load control condition, high load significantly diminished coordination performance and P3 amplitude. A significant relationship between P3 and performance was found. Load did not affect IBS, nor did IBS affect performance. These findings suggest a causal role for working memory in two-person coordination, adding to a growing body of evidence challenging earlier claims that social alignment is domain-specific and does not require executive control in adults.
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Memória de Curto Prazo , Teoria da Mente , Adulto , Humanos , Eletroencefalografia/métodos , EncéfaloRESUMO
Ruminative thinking, characterized by a recurrent focus on negative and self-related thought, is a key cognitive vulnerability marker of depression and, therefore, a key individual difference variable. This study aimed to develop a computational cognitive model of rumination focusing on the organization and retrieval of information in memory, and how these mechanisms differ in individuals prone to rumination and individuals less prone to rumination. Adaptive Control of Thought-Rational (ACT-R) was used to develop a rumination model by adding memory chunks with negative valence to the declarative memory. In addition, their strength of association was increased to simulate recurrent negative focus, thereby making it harder to disengage from. The ACT-R models were validated by comparing them against two empirical datasets containing data from control and depressed participants. Our general and ruminative models were able to recreate the benchmarks of free recall while matching the behavior exhibited by the control and the depressed participants, respectively. Our study shows that it is possible to build a computational theory of rumination that can accurately simulate the differences in free recall dynamics between control and depressed individuals. Such a model could enable a more fine-tuned investigation of underlying cognitive mechanisms of depression and potentially help to improve interventions by allowing them to more specifically target key mechanisms that instigate and maintain depression.
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Objective. Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNNs) to track mind-wandering across studies.Approach. We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N= 6) and tested the meta-learner on the data from an independent study for across-study predictions.Main results. The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel.Significance. Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps.
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Eletroencefalografia , Aprendizado de Máquina , Humanos , Eletroencefalografia/métodos , Redes Neurais de Computação , Processos MentaisRESUMO
Human performance shows substantial endogenous variability over time, and this variability is a robust marker of individual differences. Of growing interest to psychologists is the realisation that variability is not fully random, but often exhibits temporal dependencies. However, their measurement and interpretation come with several controversies. Furthermore, their potential benefit for studying individual differences in healthy and clinical populations remains unclear. Here, we gather new and archival datasets featuring 11 sensorimotor and cognitive tasks across 526 participants, to examine individual differences in temporal structures. We first investigate intra-individual repeatability of the most common measures of temporal structures - to test their potential for capturing stable individual differences. Secondly, we examine inter-individual differences in these measures using: (1) task performance assessed from the same data, (2) meta-cognitive ratings of on-taskness from thought probes occasionally presented throughout the task, and (3) self-assessed attention-deficit related traits. Across all datasets, autocorrelation at lag 1 and Power Spectra Density slope showed high intra-individual repeatability across sessions and correlated with task performance. The Detrended Fluctuation Analysis slope showed the same pattern, but less reliably. The long-term component (d) of the ARFIMA(1,d,1) model showed poor repeatability and no correlation to performance. Overall, these measures failed to show external validity when correlated with either mean subjective attentional state or self-assessed traits between participants. Thus, some measures of serial dependencies may be stable individual traits, but their usefulness in capturing individual differences in other constructs typically associated with variability in performance seems limited. We conclude with comprehensive recommendations for researchers. Supplementary Information: The online version contains supplementary material available at 10.1007/s42113-022-00162-1.
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Objective:Recent progress in network sciences has made it possible to apply key findings from control theory to the study of networks. Referred to as network control theory, this framework describes how the interactions between interconnected system elements and external energy sources, potentially constrained by different optimality criteria, result in complex network behavior. A typical example is the quantification of the functional role certain brain regions or symptoms play in shaping the temporal dynamics of brain activity or the clinical course of a disease, a property that is quantified in terms of the so-called controllability metrics. Critically though, contrary to the engineering context in which control theory was originally developed, a mathematical understanding of the network nodes and connections in neurosciences cannot be assumed. For instance, in the case of psychological systems such as those studied to understand psychiatric disorders, a potentially large set of related variables are unknown. As such, while the measures offered by network control theory would be mathematically correct, in that they can be calculated with high precision, they could have little translational values with respect to their putative role suggested by controllability metrics. It is therefore critical to understand if and how the controllability metrics estimated over subnetworks would deviate, if access to the complete set of variables, as is common in neurosciences, cannot be taken for granted.Approach:In this paper, we use a host of simulations based on synthetic as well as structural magnetic resonance imaging (MRI) data to study the potential deviation of controllability metrics in sub- compared to the full networks. Specifically, we estimate average- and modal-controllability, two of the most widely used controllability measures in neurosciences, in a large number of settings where we systematically vary network type, network size, and edge density.Main results:We find out, across all network types we test, that average and modal controllability are systematically, over- or underestimated depending on the number of nodes in the sub- and full network and the edge density.Significance:Finally, we provide formal theoretical proof that our observations generalize to any network type and discuss the ramifications of this systematic bias and potential solutions to alleviate the problem.
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Encéfalo , Transtornos Mentais , Humanos , Imageamento por Ressonância MagnéticaRESUMO
Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.
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Transtorno Depressivo Maior , Humanos , Transtorno Depressivo Maior/diagnóstico , Depressão/diagnóstico , Qualidade de Vida , Eletroencefalografia/métodos , Biomarcadores , Aprendizado de MáquinaRESUMO
BACKGROUND AND OBJECTIVES: Mind-wandering, and specifically the frequency and content of mind-wandering, plays an important role in the psychological well-being of individuals. Repetitive negative thinking has been associated with a high risk to develop and maintain Major Depressive Disorder. We here combined paradigms and techniques from cognitive sciences and experimental clinical psychology to study the transdiagnostic psychiatric phenomenon of repetitive negative thinking. This allowed us to investigate the adjustability of the content and characteristics of mind-wandering in individuals varying in their susceptibility to negative affect. METHODS: Participants high (n = 42) or low (n = 40) on their vulnerability for negative affect and depression performed a Sustained Attention to Response Task (SART) after a single session of positive fantasizing and a single session of stress induction in a cross-over design. Affective states were measured before and after the interventions. RESULTS: After stress, negative affect increased, while after fantasizing both positive affect increased and negative affect decreased. Thoughts were less off-task, past-related and negative after fantasizing compared to after stress. Individuals more susceptible to negative affect showed more off-task thinking after stress than after fantasizing compared to individuals low on this. LIMITATIONS: In this cross-over design, no baseline measurement was included, limiting comparison to 'uninduced' mind-wandering. Inclusion of self-related concerns in the SART could have led to negative priming. CONCLUSIONS: Stress-induced negative thinking underlying vulnerability for depression could be partially countered by fantasizing in a non-clinical sample, which may inform the development of treatments for depression and other disorders characterized by maladaptive thinking.
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Depressão , Transtorno Depressivo Maior , Humanos , Afeto/fisiologia , Depressão/psicologia , Emoções , Estudos Cross-OverRESUMO
We argue that many of the crises currently afflicting science can be associated with a present failure of science to sufficiently embody its own values. Here, we propose a response beyond mere crisis resolution based on the observation that an ethical framework of flourishing derived from the Buddhist tradition aligns surprisingly well with the values of science itself. This alignment, we argue, suggests a recasting of science from a competitively managed activity of knowledge production to a collaboratively organized moral practice that puts kindness and sharing at its core. We end by examining how Flourishing Science could be embodied in academic practice, from individual to organizational levels, and how that could help to arrive at a flourishing of scientists and science alike.
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BACKGROUND: The recurrent nature of Major Depressive Disorder (MDD) asks for a better understanding of mechanisms underlying relapse. Previously, self-referential processing abnormalities have been linked to vulnerability for relapse. We investigated whether abnormalities in self-referential cognitions and functioning of associated brain-networks persist upon remission and predict relapse. METHODS: Remitted recurrent MDD patients (n = 48) and never-depressed controls (n = 23) underwent resting-state fMRI scanning at baseline and were additionally assessed for their implicit depressed self-associations and ruminative behaviour. A template-based dual regression approach was used to investigate between-group differences in default mode, cingulo-opercular and frontoparietal network resting-state functional connectivity (RSFC). Additional prediction of relapse status at 18-month follow-up was investigated within patients using both regression analyses and machine learning classifiers. RESULTS: Remitted patients showed higher rumination, but no implicit depressed self-associations or RSFC abnormalities were observed between patients and controls. Nevertheless, relapse was related to i) baseline RSFC between the ventral default mode network and the precuneus, dorsomedial frontal gyrus, and inferior occipital lobe, ii) implicit self-associations, and iii) uncontrollability of ruminative thinking, when controlled for depressive symptomatology. Moreover, preliminary machine learning classifiers demonstrated that RSFC within the investigated networks predicted relapse on an individual basis. CONCLUSIONS: Remitted MDD patients seem to be commonly characterized by abnormal rumination, but not by implicit self-associations or abnormalities in relevant brain networks. Nevertheless, relapse was predicted by self-related cognitions and default mode RSFC during remission, suggesting that variations in self-relevant processing play a role in the complex dynamics associated with the vulnerability to developing recurrent depressive episodes. CLINICAL TRIAL REGISTRATION: Netherlands Trial Register, August 18, 2015, trial number NL53205.042.15.
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Transtorno Depressivo Maior , Humanos , Transtorno Depressivo Maior/diagnóstico por imagem , Depressão , Encéfalo/diagnóstico por imagem , Lobo Frontal , Imageamento por Ressonância Magnética , Recidiva , Mapeamento EncefálicoRESUMO
This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.
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Sistemas de Apoio a Decisões Clínicas , Transtornos Mentais , Transtornos Psicóticos , Humanos , Processamento de Linguagem Natural , Linguística , Transtornos Psicóticos/diagnósticoRESUMO
For a large proportion of our daily lives, spontaneously occurring thoughts tend to disengage our minds from goal-directed thinking. Previous studies showed that EEG features such as the P3 and alpha oscillations can predict mind-wandering to some extent, but only with accuracies of around 60%. A potential candidate for improving prediction accuracy is the Steady-State Visual Evoked Potential (SSVEP), which is used frequently in single-trial contexts such as brain-computer interfaces as a marker of the direction of attention. In this study, we modified the sustained attention to response task (SART) that is usually employed to measure spontaneous thought to incorporate the SSVEP elicited by a 12.5-Hz flicker. We then examined whether the SSVEP could track and allow for the prediction of the stickiness and task-relatedness dimensions of spontaneous thought. Our results show that the SSVEP evoked by flickering words was able to distinguish between more and less sticky thinking but not between whether a participant was on- or off-task. This suggests that the SSVEP is able to track spontaneous thinking when it is strongly disengaged from the task (as in the sticky form of off-task thinking) but not off-task thought in general. Future research should determine the exact dimensions of spontaneous thought to which the SSVEP is most sensitive.