Automatization and working memory capacity in schizophrenia.

Working memory (WM) dysfunction in schizophrenia is characterized by inefficient WM recruitment and reduced capacity, but it is not yet clear how these relate to one another. In controls practice of certain cognitive tasks induces automatization, which is associated with reduced WM recruitment and increased capacity of concurrent task performance. We therefore investigated whether inefficient function and reduced capacity in schizophrenia was associated with a failure in automatization. FMRI data was acquired with a verbal WM task with novel and practiced stimuli in 18 schizophrenia patients and 18 controls. Participants performed a dual-task outside the scanner to test WM capacity. Patients showed intact performance on the WM task, which was paralleled by excessive WM activity. Practice improved performance and reduced WM activity in both groups. The difference in WM activity after practice predicted performance cost in controls but not in patients. In addition, patients showed disproportionately poor dual-task performance compared to controls, especially when processing information that required continuous adjustment in WM. Our findings support the notion of inefficient WM function and reduced capacity in schizophrenia. This was not related to a failure in automatization, but was evident when processing continuously changing information. This suggests that inefficient WM function and reduced capacity may be related to an inability to process information requiring frequent updating.

Novelty modulates human striatal activation and prefrontal-striatal effective connectivity during working memory encoding.

The functional role of the basal ganglia (BG) in the gating of suitable motor responses to the cortex is well established. Growing evidence supports an analogous role of the BG during working memory encoding, a task phase in which the "input-gating" of relevant materials (or filtering of irrelevant information) is an important mechanism supporting cognitive capacity and the updating of working memory buffers. One important aspect of stimulus relevance is the novelty of working memory items, a quality that is understudied with respect to its effects on corticostriatal function and connectivity. To this end, we used functional magnetic resonance imaging (fMRI) in 74 healthy volunteers performing an established Sternberg working memory task with different task phases (encoding vs. retrieval) and degrees of stimulus familiarity (novel vs. previously trained). Activation analyses demonstrated a highly significant engagement of the anterior striatum, in particular during the encoding of novel working memory items. Dynamic causal modeling (DCM) of corticostriatal circuit connectivity identified a selective positive modulatory influence of novelty encoding on the connection from the dorsolateral prefrontal cortex (DLPFC) to the anterior striatum. These data extend prior research by further underscoring the relevance of the BG for human cognitive function and provide a mechanistic account of the DLPFC as a plausible top-down regulatory element of striatal function that may facilitate the "input-gating" of novel working memory materials.

Capturing the dynamics of response variability in the brain in ADHD.

ADHD is characterized by increased intra-individual variability in response times during the performance of cognitive tasks. However, little is known about developmental changes in intra-individual variability, and how these changes relate to cognitive performance. Twenty subjects with ADHD aged 7-24 years and 20 age-matched, typically developing controls participated in an fMRI-scan while they performed a go-no-go task. We fit an ex-Gaussian distribution on the response distribution to objectively separate extremely slow responses, related to lapses of attention, from variability on fast responses. We assessed developmental changes in these intra-individual variability measures, and investigated their relation to no-go performance. Results show that the ex-Gaussian measures were better predictors of no-go performance than traditional measures of reaction time. Furthermore, we found between-group differences in the change in ex-Gaussian parameters with age, and their relation to task performance: subjects with ADHD showed age-related decreases in their variability on fast responses (sigma), but not in lapses of attention (tau), whereas control subjects showed a decrease in both measures of variability. For control subjects, but not subjects with ADHD, this age-related reduction in variability was predictive of task performance. This group difference was reflected in neural activation: for typically developing subjects, the age-related decrease in intra-individual variability on fast responses (sigma) predicted activity in the dorsal anterior cingulate gyrus (dACG), whereas for subjects with ADHD, activity in this region was related to improved no-go performance with age, but not to intra-individual variability. These data show that using more sophisticated measures of intra-individual variability allows the capturing of the dynamics of task performance and associated neural changes not permitted by more traditional measures.

Developmental differences in higher-order resting-state networks in Autism Spectrum Disorder.

OBJECTIVE: Autism Spectrum Disorder (ASD) has been associated with a complex pattern of increases and decreases in resting-state functional connectivity. The developmental disconnection hypothesis of ASD poses that shorter connections become overly well established with development in this disorder, at the cost of long-range connections. Here, we investigated resting-state connectivity in relatively young boys with ASD and typically developing children. We hypothesized that ASD would be associated with reduced connectivity between networks, and increased connectivity within networks, reflecting poorer integration and segregation of functional networks in ASD. METHODS: We acquired resting-state fMRI from 27 boys with ASD and 29 age- and IQ-matched typically developing boys between 6 and 16 years of age. Functional connectivity networks were identified using Independent Component Analysis (ICA). Group comparisons were conducted using permutation testing, with and without voxel-wise correction for grey matter density. RESULTS: We found no between-group differences in within-network connectivity. However, we did find reduced functional connectivity between two higher-order cognitive networks in ASD. Furthermore, we found an interaction effect with age in the DMN: insula connectivity increased with age in ASD, whereas it decreased in typically developing children. CONCLUSIONS: These results show subtle changes in between network connectivity in relatively young boys with ASD. However, the global architecture of resting-state networks appeared to be intact. This argues against recent suggestions that changes in connectivity in ASD may be the most prominent during development.

fMRI guided rTMS evidence for reduced left prefrontal involvement after task practice.

INTRODUCTION: Cognitive tasks that do not change the required response for a stimulus over time ('consistent mapping') show dramatically improved performance after relative short periods of practice. This improvement is associated with reduced brain activity in a large network of brain regions, including left prefrontal and parietal cortex. The present study used fMRI-guided repetitive transcranial magnetic stimulation (rTMS), which has been shown to reduce processing efficacy, to examine if the reduced activity in these regions also reflects reduced involvement, or possibly increased efficiency. METHODS: First, subjects performed runs of a Sternberg task in the scanner with novel or practiced target-sets. This data was used to identify individual sites for left prefrontal and parietal peak brain activity, as well as to examine the change in activity related to practice. Outside of the scanner, real and sham rTMS was applied at left prefrontal and parietal cortex to examine their involvement novel and practiced conditions. RESULTS: Prefrontal as well as parietal rTMS significantly reduced target accuracy for novel targets. Prefrontal, but not parietal, rTMS interference was significantly lower for practiced than novel target-sets. rTMS did not affect non-target accuracy, or reaction time in any condition. DISCUSSION: These results show that task practice in a consistent environment reduces involvement of the prefrontal cortex. Our findings suggest that prefrontal cortex is predominantly involved in target maintenance and comparison, as rTMS interference was only detectable for targets. Findings support process switching hypotheses that propose that practice creates the possibility to select a response without the need to compare with target items. Our results also support the notion that practice allows for redistribution of limited maintenance resources.

Practice induces function-specific changes in brain activity.

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.