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Decoding movements from the human cortex has been a topic of great interest for controlling an artificial limb in non-human primates and severely paralyzed people. Here we investigate feasibility of decoding gestures from the sensorimotor cortex in humans, using 7 T fMRI. Twelve healthy volunteers performed four hand gestures from the American Sign Language Alphabet. These gestures were performed in a rapid event related design used to establish the classifier and a slow event-related design, used to test the classifier. Single trial patterns were classified using a pattern-correlation classifier. The four hand gestures could be classified with an average accuracy of 63 % (range 3595 %), which was significantly above chance (25 %). The hand region was, as expected, the most active region, and the optimal volume for classification was on average about 200 voxels, although this varied considerably across individuals. Importantly, classification accuracy correlated significantly with consistency of gesture execution. The results of our study demonstrate that decoding gestures from the hand region of the sensorimotor cortex using 7 T fMRI can reach very high accuracy, provided that gestures are executed in a consistent manner. Our results further indicate that the neuronal representation of hand gestures is robust and highly reproducible. Given that the most active foci were located in the hand region, and that 7 T fMRI has been shown to agree with electrocorticography, our results suggest that this confined region could serve to decode sign language gestures for intracranial braincomputer interfacing using surface grids.
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Gestos , Mano/fisiología , Corteza Motora/fisiología , Movimiento/fisiología , Adulto , Mapeo Encefálico , Femenino , Humanos , Imagen por Resonancia Magnética , Masculino , Adulto JovenRESUMEN
Introduction: Suicide is one of the leading causes of death among adolescents. Although it is known that both perfectionism and attention-deficit/hyperactivity disorder ('ADHD') are important risk factors for suicide, there are no studies that have investigated the relationship between suicidal behavior and perfectionism among people with ADHD. Aim: The current study investigates the association between perfectionism and suicide in adolescents with ADHD. Method: Subjects included 88 adolescents with ADHD and 96 non-clinical control adolescents. We used the Multidimensional Perfectionism Scale to evaluate perfectionism as well as its separate traits, and the Mini International Neuropsychologic Interview Kid to evaluate psychiatric disorders and suicidal behavior. Differences between the groups were statistically evaluated using t-tests, a Poisson regression analysis with suicide as a discrete variable, and a logistic regression analysis with suicide as a binary variable. Results: Compared to the control group, the ADHD group showed a significantly lower level on the adaptive 'Organization' trait of perfectionism, but not on any other trait, and a significantly higher level of suicidal behavior. Logistic regression results indicated a significant association for perfectionism in general (OR = 0.93, p = 0.003), and for the 'Personal Standards' trait (OR: 0.82, p = 0.039). Poisson regression analysis also showed a significant association with perfectionism in general (IRR = 0.90; p < 0.001) and with the 'Personal standards' trait model (IRR = 0.81, p = 0.019). Discussion: Our results indicate that a low level of perfectionism, in particular 'Personal standards', may be a risk factor for suicidal behavior in adolescents with ADHD. We recommend that psychoeducation and therapy of adolescents with ADHD should consider focusing on adaptive perfection as a possible risk factor for suicide as well.
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BACKGROUND: The main goal of this functional MRI (fMRI) study was to examine whether cognitive deficits in glioma patients prior to treatment are associated with abnormal brain activity in either the central executive network (CEN) or default mode network (DMN). METHODS: Forty-six glioma patients, and 23 group-matched healthy controls (HCs) participated in this fMRI experiment, performing an N-back task. Additionally, cognitive profiles of patients were evaluated outside the scanner. A region of interest-based analysis was used to compare brain activity in CEN and DMN between groups. Post hoc analyses were performed to evaluate differences between low-grade glioma (LGG) and high-grade glioma (HGG) patients. RESULTS: In-scanner performance was lower in glioma patients compared to HCs. Neuropsychological testing indicated cognitive impairment in LGG as well as HGG patients. fMRI results revealed normal CEN activation in glioma patients, whereas patients showed reduced DMN deactivation compared to HCs. Brain activity levels did not differ between LGG and HGG patients. CONCLUSIONS: Our study suggests that cognitive deficits in glioma patients prior to treatment are associated with reduced responsiveness of the DMN, but not with abnormal CEN activation. These results suggest that cognitive deficits in glioma patients reflect a reduced capacity to achieve a brain state necessary for normal cognitive performance, rather than abnormal functioning of executive brain regions. Solely focusing on increases in brain activity may well be insufficient if we want to understand the underlying brain mechanism of cognitive impairments in patients, as our results indicate the importance of assessing deactivation.
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Previous research shows that people can use a cue to mentally prepare for a cognitive challenge. The response to a cue has been defined as phasic alertness which is reflected in faster responses and increased activity in frontal, parietal, thalamic, and visual brain regions. We examine if and how phasic alertness can be tuned to the expected difficulty of an upcoming challenge. If people in general are able to tune their level of alertness, then an inability to tune may be linked to disease. Twenty-two healthy volunteers performed a cued visual perception task with two levels of task difficulty. Performance and brain activity were compared between these two levels. Performance was lower for difficult stimuli than for easy stimuli. For both cue types, participants showed activation in a network associated with central executive function and deactivation in regions of the default mode network (DMN) and visual cortex. Deactivation was significantly stronger for cues signaling difficult stimuli than for cues signaling easy stimuli. This effect was most prominent in medial prefrontal gyrus, visual, and temporal cortices. Activation did not differ between the cues. Our study shows that phasic alertness is represented by activated as well as deactivated brain regions. However only deactivated brain regions tuned their level of activity to the expected task difficulty. These results suggest that people, in general, are able to tune their level of alertness to an upcoming task. Cognition may be facilitated by a brain-state coupled to expectations about an upcoming cognitive challenge. Unique identifier = 842003004.
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A required response forces the brain to react overtly on a stimulus. This may be a factor that influences cognitive activity during a task, as it could facilitate for instance alertness, especially in tasks that are relatively easy. In the current article, we therefore tested the hypothesis that response frequency affects cognitive brain activity in an alertness task. In this 3T functional MRI study, healthy volunteers performed a continuous performance task with three conditions with increasing response frequency. Only scans during presentation of non-targets were analyzed, to exclude activity related to the change in frequency in response selection and motor responses between conditions. To evaluate changes in cognitive brain activity, a network analysis was performed based on two main networks including regions with task-induced activation and task-induced deactivation. We tested for differences in brain activity as an effect of target frequency. Performance results indicated no effect of target frequency on accuracy or reaction time. During non-targets, we found significant signal changes in TID for all three conditions, whereas TIA showed no significant signal changes in any condition. Target frequency did not have a significant effect on the level of signal change at network level, as well as at individual region level. Our study showed predominantly deactivation during non-responses in all three task conditions. Furthermore, our results indicate that response frequency does not influence brain activity during an alertness task. Our results provide additional information relevant for the understanding of the neurophysiological implementation of cognitive control or alertness.
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Atención/fisiología , Encéfalo/fisiología , Cognición/fisiología , Adulto , Femenino , Humanos , Imagen por Resonancia Magnética , MasculinoRESUMEN
BACKGROUND: Practice can have a profound effect on performance and brain activity, especially if a task can be automated. Tasks that allow for automatization typically involve repeated encoding of information that is paired with a constant response. Much remains unknown about the effects of practice on encoding and response selection in an automated task. METHODOLOGY: To investigate function-specific effects of automatization we employed a variant of a Sternberg task with optimized separation of activity associated with encoding and response selection by means of m-sequences. This optimized randomized event-related design allows for model free measurement of BOLD signals over the course of practice. Brain activity was measured at six consecutive runs of practice and compared to brain activity in a novel task. PRINCIPAL FINDINGS: Prompt reductions were found in the entire cortical network involved in encoding after a single run of practice. Changes in the network associated with response selection were less robust and were present only after the third run of practice. CONCLUSIONS/SIGNIFICANCE: This study shows that automatization causes heterogeneous decreases in brain activity across functional regions that do not strictly track performance improvement. This suggests that cognitive performance is supported by a dynamic allocation of multiple resources in a distributed network. Our findings may bear importance in understanding the role of automatization in complex cognitive performance, as increased encoding efficiency in early stages of practice possibly increases the capacity to otherwise interfering information.
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Encéfalo/fisiología , Análisis y Desempeño de Tareas , Adulto , Femenino , Humanos , Imagen por Resonancia Magnética , MasculinoRESUMEN
Recent studies have shown that obsessive compulsive disorder (OCD) is associated with a specific deficit in spatial working memory, especially when task difficulty (i.e., working memory load) is high. It is not clear whether this deficit is associated with dysfunction of the brain system that subserves spatial working memory, or whether it is associated with a more generalized effect on executive functions. In contrast to studies in healthy volunteers and schizophrenia, spatial working memory in OCD has not been investigated before using functional neuroimaging techniques. We conducted a functional MRI study in 11 treatment-free female patients with OCD and 11 for sex-, age-, education-, and handedness pairwise-matched healthy controls in order to assess performance on a parametric spatial n-back task as well as the underlying neuronal substrate and its dynamics. Patients with OCD performed poorly at the highest level of task difficulty and engaged the same set of brain regions as the matched healthy controls. In this set, the effect of difficulty on magnitude of brain activity was the same in patients and in controls except for a region covering the anterior cingulate cortex. In this region activity was significantly elevated in patients with OCD at all levels of the parametric task. These findings do not provide evidence for a deficit of the spatial working memory system proper, but suggest that the abnormal performance pattern may be secondary to another aspect of executive dysfunctioning in OCD.