Patients with schizophrenia have often been considered to be "in their own world". However, this casual observation has not been proven by scientific evidence so far. This can be explained because ...scientific research has usually addressed cognition related to the processing of external stimuli, but only recently have efforts been made to explain thoughts, images and feelings not directly related to the external environment. This internally directed cognition has been called mind wandering. In this paper, we have explored mind wandering in schizophrenia under the hypothesis that a predominance of mind wandering would be a core dysfunction in this disorder. To this end, we collected verbal reports and measured electrophysiological signals from patients with schizophrenia spectrum disorders and matched healthy controls while they were presented with segments of films. The results showed that mind wandering was more frequent in patients than in controls. This higher frequency of mind wandering did not correlate with deficits in attentional, memory or executive functioning. In addition, mind wandering in patients was characterized by a different pattern of Electroencephalography (EEG) complexity in patients than in controls, leading to the suggestion that mind wandering in schizophrenia could be of a different nature. These findings could have relevant implications for the conceptualization of this severe mental disorder.
•Consciousness is linked to the complexity of distributed interactions within the human brain.•Complexity of structural and functional patterns is detectable with fractal dimension (FD).•A Fractal ...Dimension Index (FDI) is computed combining integration FD and differentiation FD.•FDI is significantly lower in sleep and sedation when compared to wakefulness.•FDI provides an almost perfect intra-subject discrimination between conscious and unconscious states.
Knowing whether a subject is conscious or not is a current challenge with a deep potential clinical impact. Recent theoretical considerations suggest that consciousness is linked to the complexity of distributed interactions within the corticothalamic system. The fractal dimension (FD) is a quantitative parameter that has been extensively used to analyse the complexity of structural and functional patterns of the human brain. In this study we investigate FD to assess whether it can discriminate between consciousness and different states of unconsciousness in healthy individuals.
We study 69 high-density electroencephalogram (hd-EEG) measurements after transcranial magnetic stimulation (TMS) in 18 healthy subjects progressing from wakefulness to non-rapid eye movement (NREM) sleep and sedation induced by different anaesthetic agents (xenon and propofol). We quantify the integration of thalamocortical networks by calculating the FD of a spatiotemporal voxelization obtained from the locations of all sources that are significantly activated by the perturbation (4DFD). Moreover, we study the temporal evolution of the evoked spatial distributions and compute a measure of the differentiation of the response by means of the Higuchi FD (HFD). Finally, a Fractal Dimension Index (FDI) of perturbational complexity is computed as the product of both quantities: integration FD (4DFD) and differentiation FD (HFD).
We found that FDI is significantly lower in sleep and sedation when compared to wakefulness and provides an almost perfect intra-subject discrimination between conscious and unconscious states.
These results support the combination of FD measures of cortical integration and cortical differentiation as a novel paradigm of tracking complex spatiotemporal dynamics in the brain that could provide further insights into the link between complexity and the brain's capacity to sustain consciousness.
Although there is an extensive literature on the study of the neural correlates of consciousness (NCC) this is a subject that is far from being considered over. In this paper we present a novel ...experimental paradigm, based on binocular rivalry, to study internally and externally generated conscious experiences. We called this procedure bimodal rivalry. In addition, and assuming the non-linear nature of the EEG signals, we propose the use of fractal dimension to characterize the complexity of the EEG signal associated with each percept. Analysis of the data showed a significant difference in complexity between the internally generated and externally generated percepts. Moreover, EEG complexity was dissimilar for externally generated auditory and visual percepts. These results support fractal dimension analyses as a new tool to characterize conscious perception.
We explore the idea that cognitive demands of the handwriting would influence the degree of automaticity of the handwriting process, which in turn would affect the geometric parameters of texts. We ...compared the heterogeneity of handwritten texts in tasks with different cognitive demands; the heterogeneity of texts was analyzed with lacunarity, a measure of geometrical invariance. In Experiment 1, we asked participants to perform two tasks that varied in cognitive demands: transcription and exposition about an autobiographical episode. Lacunarity was significantly lower in transcription. In Experiment 2, we compared a veridical and a fictitious version of a personal event. Lacunarity was lower in veridical texts. We contend that differences in lacunarity of handwritten texts reveal the degree of automaticity in handwriting.
The analysis of handwriting has been used in several contexts. For example, handwriting has shown to be of value in the study of motor symptoms in neurological and mental disorders. In the present ...work, the geometric analysis of handwriting patterns is proposed as a tool to evaluate motor symptoms in psychotic disorders. Specifically, we have employed the lacunarity, a measure of the heterogeneity of a spatial structure. Forty-two patients with a psychotic disorder and 35 matched healthy controls participated in the study. Participants were asked to copy some patterns with a pen on a white paper. The results showed that lacunarity was significantly higher in handwritten patterns from patients than from controls. In addition, we found higher values of lacunarity in handwritten patterns from patients with severe motor symptoms in comparison with patients with mild or absent motor symptoms. Lacunarity of handwritten patterns was significantly correlated with clinical scores of rigidity. In conclusion we argue that the heterogeneity of handwritten patterns could be used as a simple and objective measure of motor symptoms.
•Early diagnosis of schizophrenia is a very difficult task.•A processing pipeline for selecting features based on resting state EEG data is proposed.•Complexity measures allowed standard machine ...learning algorithms to perform very efficiently.
Schizophrenia is a severe mental disorder associated with a wide spectrum of cognitive and neurophysiological dysfunctions. Early diagnosis is still difficult and based on the manifestation of the disorder. In this study, we have evaluated whether machine learning techniques can help in the diagnosis of schizophrenia, and proposed a processing pipeline in order to obtain machine learning classifiers of schizophrenia based on resting state EEG data. We have computed well-known linear and non-linear measures on sliding windows of the EEG data, selected those measures which better differentiate between patients and healthy controls, and combined them through principal component analysis. These components were finally used as features in five standard machine learning algorithms: k-nearest neighbours (kNN), logistic regression (LR), decision trees (DT), random forest (RF) and support vector machines (SVM). Complexity measures showed a high level of ability in differentiating schizophrenia patients from healthy controls. These differences between groups were mainly located in a delimited zone of the right brain hemisphere, corresponding to the opercular area and the temporal pole. Based on the area under the curve parameter in receiver operating characteristic curve analysis, we obtained high classification power in almost all of the machine learning algorithms tested: SVM (0.89), RF (0.87), LR (0.86), kNN (0.86) and DT (0.68). Our results suggest that the proposed processing pipeline on resting state EEG data is able to easily compute and select a set of features which allow standard machine learning algorithms to perform very efficiently in differentiating schizophrenia patients from healthy subjects.
The box-counting (BC) algorithm is one of the most popular methods for calculating the fractal dimension (FD) of binary data. FD analysis has many important applications in the biomedical field, such ...as cancer detection from 2D computed axial tomography images, Alzheimer’s disease diagnosis from magnetic resonance 3D volumetric data, and consciousness states characterization based on 4D data extracted from electroencephalography (EEG) signals, among many others. Currently, these kinds of applications use data whose size and amount can be very large, with high computation times needed to calculate the BC of the whole datasets. In this study we present a very efficient parallel implementation of the BC algorithm for its execution on Graphics Processing Units (GPU). Our algorithm can process 2D, 3D and 4D data and we tested it on two platforms with different hardware configurations. The results showed speedups of up to 92.38 × (2D), 57.27 × (3D) and 75.73 × (4D) with respect to the corresponding CPU single-thread implementations of the same algorithm. Against an OpenMP multi-thread CPU implementation, our GPU algorithm achieved speedups of up to 16.12 × (2D), 6.86 × (3D) and 7.49 × (4D). We have also compared our algorithm to a previous GPU implementation of the BC algorithm in 3D, achieving a speedup of up to 4.79 × . Finally, as a practical application of our GPU BC algorithm a study comparing the FD of 4D data extracted from the EEGs of a schizophrenia patient and a healthy subject was performed. The computation time for processing 40 4D matrices was reduced from three hours (sequential CPU) to less than three minutes with our GPU algorithm.
•Our GPU box-counting algorithm computes the fractal dimension of 2D, 3D and 4D data.•Speedups of up to 92.38 × (2D), 57.27 × (3D) and 75.73 × (4D) were achieved regarding the CPU algorithm.•Speedups of up to 16.12 × (2D), 6.86 × (3D) and 7.49 × (4D) were achieved regarding the OpenMP implementation.•Our GPU algorithm is more than four times faster than the previous GPU implementation of the box-counting.•Our GPU algorithm was applied for accelerating the computation of 4D data extracted from EEG.
This article reports an empirical investigation of the cognitive effort required to decide in multiattribute binary choice using a variation of the Additive Difference strategy. In contrast with ...other studies, this paper focuses on the effect of various context variables (rather than task variables) on cognitive effort. In order to select the context variables to be manipulated, we used the model proposed by Shugan (1980; J. Consumer Res. 75 (1980) 99). Our results indicate that there is a positive relationship between the cognitive effort required to decide and the mean of the differences between the dimensions of the choice alternatives. We have also found an inverse relationship between cognitive effort and the variance of the differences between the dimensions of the choice alternatives. Finally, we have found that in negative correlation contexts the effort needed to decide is greater than in positive and null correlation contexts. PUBLICATION ABSTRACT
This paper examines how a number of decision context variables affect the cognitive effort required for decision making on dichotomical choice tasks. Subjects are trained in the use of a strategy in ...which information processing is alternative-based. The correlation between the attributes of the alternatives and the mean and variance of the difference between the attributes is manipulated. The results show that the effort needed for decision making increases as the mean of the differences decreases. Yet, neither the variance of the differences nor the correlation context affect the decision making effort in this type of strategies.PUBLICATION ABSTRACT
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