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Background Therapeutic sleep deprivation (SD) presents a unique paradigm to study the neurobiology of major depressive disorder (MDD). However, the rapid antidepressant mechanism, which differs from today's slow first-line treatments, is not sufficiently understood. We recently integrated two prominent hypotheses of MDD and sleep, the synaptic plasticity hypothesis of MDD and the synaptic homeostasis hypothesis of sleep-wake regulation, into a synaptic plasticity model of therapeutic SD in MDD. Here, we further tested this model positing that homeostatically elevating net synaptic strength through therapeutic SD shifts the initially deficient inducibility of associative synaptic long-term potentiation (LTP)-like plasticity in patients with MDD into a more favorable window of associative plasticity. Methods We used paired associative stimulation (PAS), a transcranial magnetic stimulation protocol (TMS), to quantify cortical LTP-like plasticity after one night of therapeutic sleep deprivation in 28 patients with MDD. Results We demonstrate a significantly different inducibility of associative plasticity in clinical responders to therapeutic SD (> 50% improvement on the 6-item Hamilton-Rating-Scale for Depression, n=13) compared to non-responders (n=15), which was driven by a long-term depression (LTD)-like response in SD-non-responders. Indices of global net synaptic strength (wake EEG theta activity, intracortical inhibition and BDNF serum levels) were increased after SD in both groups, with responders showing a generally lower intracortical inhibition than non-responders. Limitations Repetitive assessments prior to and after treatment would be needed to further determine potential mechanisms. Conclusion After a night of therapeutic SD, clinical responders show a significantly higher inducibility of associative LTP-like plasticity than non-responders.

Background:Arousal and sleep represent basic domains of behavior, and alterations are of high clinical importance. Objective/hypothesis:The aim of this study was to further elucidate the neurobiology of insomnia disorder (ID) and the potential for new treatment developments, based on the modulation of cortical activity through the non-invasivebrain stimulationtechnique transcranial direct current stimulation (tDCS). Specifically, we tested the hypotheses that bi-frontal anodal tDCS shortens and cathodal tDCS prolongs total sleep time in patients with ID, compared to sham stimulation. Furthermore, we tested for differences in indices of arousal between ID patients and healthy controls and explored their potential impact on tDCS effects. Methods:Nineteen ID patients underwent a within-subject repeated-measures sleep laboratory study with adaptation, baseline and three experimental nights. Bifrontal anodal, cathodal and sham tDCS was delivered in a counterbalanced order immediately prior to sleep. WakeEEGwas recorded prior to and after tDCS as well as on the following morning. Subsequently, we compared patients with ID to a healthy control group from an earlier dataset. Results:Against our hypothesis, we did not observe any tDCS effects on sleep continuity or sleep architecture in patients with ID. Further analyses of nights without stimulation demonstrated significantly increased levels of arousal in ID patients compared to healthy controls, as indexed by subjective reports, reduced total sleep time, increased wake after sleep onset and increased high frequency EEG power during wakefulness andNREM sleep. Of note, indices of increased arousal predicted the lack of effect of tDCS in ID patients. Conclusions:Our study characterizes for the first time differential effects of tDCS on sleep in patients with ID and healthy controls, presumably related to persistent hyperarousal in ID. These findings suggest that adapted tDCS protocols need to be developed to modulate arousal and sleep dependent on baseline arousal levels.

Sleep modulates motor learning, but its detailed impact on performance curves remains to be fully characterized. This study aimed to further determine the impact of brief daytime periods of NREM sleep on ‘offline’ (task discontinuation after initial training) and ‘on-task’ (performance within the test session) changes in motor skill performance (finger tapping task). In a mixed design (combined parallel group and repeated measures) sleep laboratory study (n = 17 ‘active’ wake vs. sleep, n = 19 ‘passive’ wake vs. sleep), performance curves were assessed prior to and after a 90 min period containing either sleep, active or passive wakefulness. We observed a highly significant, but state- (that is, sleep/wake)-independent early offline gain and improved on-task performance after sleep in comparison to wakefulness. Exploratory curve fitting suggested that the observed sleep effect most likely emerged from an interaction of training-induced improvement and detrimental ‘time-on-task’ processes, such as fatigue. Our results indicate that brief periods of NREM sleep do not promote early offline gains but subsequent on-task performance in motor skill learning.

The experimental study of electroencephalographic slow wave sleep (SWS) stretches over more than half a century and has corroborated its importance for basic physiological processes, such as brain plasticity, metabolism and immune system functioning. Alterations of SWS in aging or pathological conditions suggest that modulating SWS might constitute a window for clinically relevant interventions. This work provides a systematic and integrative review of SWS modulation through non-invasive brain stimulation in humans. A literature search using PubMed, conducted in May 2020, identified 3220 studies, of which 82 fulfilled inclusion criteria. Three approaches have been adopted to modulate the macro- and microstructure of SWS, namely auditory, transcranial electrical and transcranial magnetic stimulation. Our current knowledge about the modulatory mechanisms, the space of stimulation parameters and the physiological and behavioral effects are reported and evaluated. The integration of findings suggests that sleep slow wave modulation bears the potential to promote our understanding of the functions of SWS and to develop new treatments for conditions of disrupted SWS.

The APOE ε4 allele increases the risk for sporadic Alzheimer's disease and modifies brain activation patterns of numerous cognitive domains. We assessed cognitively intact older adults with a letter n-back task to determine if previously observed increases in ε4 carriers' working-memory-related brain activation are compensatory such that they serve to maintain working memory function. Using multiple regression models, we identified interactions of APOE variant and age in bilateral hippocampus independently from task performance: ε4 carriers only showed a decrease in activation with increasing age, suggesting high sensitivity of fMRI data for detecting changes in Alzheimer's disease-relevant brain areas before cognitive decline. Moreover, we identified ε4 carriers to show higher activations in task-negative medial and task-positive inferior frontal areas along with better performance under high working memory load relative to non-ε4 carriers. The increased frontal recruitment is compatible with models of neuronal compensation, extends on existing evidence, and suggests that ε4 carriers require additional neuronal resources to successfully perform a demanding working memory task.

Mammalian sleep emerges from attenuated activity in the ascending reticular arousal system (ARAS), the main arousal network of the brain. This system originates in the brainstem and activates the thalamus and cortex during wakefulness via a well-characterized 'bottom-up' pathway. Recent studies propose that a less investigated cortico-thalamic 'top-down' pathway also regulates sleep. The present work integrates the current evidence on sleep regulation with a focus on the 'top-down' pathway and explores the potential to translate this information into clinically relevant interventions. Specifically, we elaborate the concept that arousal and sleep continuity in humans can be modulated by non-invasive brain stimulation (NIBS) techniques that increase or decrease cortical excitability. Based on preclinical studies, the modulatory effects of the stimulation are thought to extend to subcortical arousal networks. Further exploration of the 'top-down' regulation of sleep and its modulation through non-invasive brain stimulation techniques may contribute to the development of novel treatments for clinical conditions of disrupted arousal and sleep, which are among the major health problems worldwide.

Therapeutic sleep deprivation (SD) is a rapid acting treatment for major depressive disorder (MDD). Within hours, SD leads to a dramatic decrease in depressive symptoms in 50-60% of patients with MDD. Scientifically, therapeutic SD presents a unique paradigm to study the neurobiology of MDD. Yet, up to now, the neurobiological basis of the antidepressant effect, which is most likely different from today's first-line treatments, is not sufficiently understood. This article puts the idea forward that sleep/wake-dependent shifts in synaptic plasticity, i.e., the neural basis of adaptive network function and behavior, represent a critical mechanism of therapeutic SD in MDD. Particularly, this article centers on two major hypotheses of MDD and sleep, the synaptic plasticity hypothesis of MDD and the synaptic homeostasis hypothesis of sleep-wake regulation, and on how they can be integrated into a novel synaptic plasticity model of therapeutic SD in MDD. As a major component, the model proposes that therapeutic SD, by homeostatically enhancing cortical synaptic strength, shifts the initially deficient inducibility of associative synaptic long-term potentiation (LTP) in patients with MDD in a more favorable window of associative plasticity. Research on the molecular effects of SD in animals and humans, including observations in the neurotrophic, adenosinergic, monoaminergic, and glutamatergic system, provides some support for the hypothesis of associative synaptic plasticity facilitation after therapeutic SD in MDD. The model proposes a novel framework for a mechanism of action of therapeutic SD that can be further tested in humans based on non-invasive indices and in animals based on direct studies of synaptic plasticity. Further determining the mechanisms of action of SD might contribute to the development of novel fast acting treatments for MDD, one of the major health problems worldwide.

Objective Paired associative stimulation (PAS) is a widely used transcranial magnetic stimulation (TMS) paradigm to induce synaptic long-term potentiation (LTP)-like plasticity in the intact human brain. The PAS effect is reduced in Alzheimer’s dementia (AD) but has not yet been assessed in patients with mild cognitive impairment (MCI). Methods PAS was assessed in a group of 24 MCI patients and 24 elderly controls. MCI patients were further stratified by their cognitive profile as well as hippocampal atrophy and Apolipoprotein E (ApoE) genotype. Results There was no difference in PAS effects between MCI patients and healthy controls. MCI patients tended to show a higher response rate and an average PAS effect. PAS effects were not correlated with markers of disease severity or ApoE genotype but were more pronounced in individuals with shorter sleep duration and in MCI subjects with higher ratings of subjective alertness. Conclusions Contrary to our initial hypothesis, there was no clear difference in PAS between MCI patients and healthy controls. Significance Our results argue against a continuous reduction of LTP-like plasticity along the spectrum of clinical MCI when stratified by MCI-subtype, APOE genotype or hippocampus atrophy.

Objective Executive deficits are frequent sequelae of neurological and psychiatric disorders, but their adequate neuropsychological assessment is still a matter of contention, given that executive tasks draw on a multitude of cognitive processes that are often not sufficiently specified. In line with this, results on psychometric properties of the Tower of London, a task measuring planning ability as a prototypical executive function, are equivocal and furthermore lacking completely for adult clinical populations. Methods We used a structurally balanced item set implemented in the Tower of London (Freiburg version, TOL-F) that accounts for major determinants of problem difficulty beyond the commonly used minimum number of moves to solution. Split-half reliability, internal consistency, and criterion-related concurrent validity of TOL-F accuracy were assessed in patients with stroke ( N =60), Parkinson syndrome ( N =51), and mild cognitive impairment ( N =29), and healthy adults ( N =155). Results Across samples, mean split-half and lower-bound indices of reliability of accuracy scores were adequate ( r ≥.7) or higher. Compared to a subset of healthy controls matched for age, sex, and education levels, deficits in planning accuracy emerged for all three clinical samples. Conclusions Based on consistently adequate reliability and a good criterion-related validity of accuracy scores, the TOL-F demonstrates its utility for testing planning ability in clinical samples and healthy adults. Using item sets systematically accounting for several determinants of task difficulty can thus significantly enhance the contended reliability of executive tasks and provide an opportunity to resolve the underspecification of cognitive processes contributing to executive functioning in health and disease.

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

Introduction The basal forebrain provides important output within the cholinergic system. The cholinergic system may also influence the effects of paired associative stimulation (PAS), a plasticity inducing protocol involving paired median nerve and motor cortex stimulation, and short latency afferent inhibition (SAI) in which a median nerve sensory afferent stimulus induces short lasting conditioning effects on motor cortex excitability. The responses to either PAS or SAI can be variable. Here we hypothesized that the basal forebrain structural characteristics, or those of the thalamus as important sensory-afferent relay station with cholinergic input, relate to the functional influence of the cholinergic system measured using PAS or SAI. We therefore asked whether structural variability in these cholinergic brain areas could explain some of that functional variability observed with PAS or SAI. Material and methods Forty healthy volunteers (12 male; mean age 60, range 26–81) underwent a facilitatory PAS protocol with an inter-stimulus interval (ISI) of around 25 ms. An LTP-like facilitatory response was defined as an increase post-PAS of MEP amplitude of at least 10% relative to pre-PAS MEP size. SAI employed median nerve condition of motor cortex stimulation with inhibitory as well as facilitatory ISIs. We obtained T1 MPRAGE sequences on a 3T MRI scanner. MRI data were processed using Statistical Parametric Mapping and the CAT12 Toolbox (http://www.neuro.uni-jena.de/cat/) implemented in Matlab 2015a. Volumes of the basal forebrain were determined using a stereotactic atlas based on combined postmortem MRI and histological examination of cholinergic nuclei. As it is based on the histological examination of a single brain specimen and may not generalize across individuals, we also used volumes of right and left BFCS as obtained from the CAT12 toolbox. We used ANOVA models for group comparisons of responders and non-responders to PAS or SAI. Results Nineteen participants (48%) had an LTP-like response to PAS. Age, sex, or motor thresholds did not predict the PAS response. SAI had inhibitory effects in 80% of participants (n = 32). There was no association between the amount of PAS effects and the SAI response. Basal forebrain and right thalamus volumes were larger in SAI responders while volumes were similar in PAS responders and non-responders. Discussion The results suggest that a larger basal forebrain cholinergic output system, and a larger thalamus as sensory-afferent relay station that receives cholinergic input facilitate the short lasting modulation of motor cortex excitability in the SAI paradigm. This could be because of higher cholinergic tone. The cholinergic system may be less important in longer lasting modulation of motor cortex excitability in the PAS paradigm where an LTP-like response may involve more GABA-ergic neurotransmission.

The early, preferably pre-clinical, identification of neurodegenerative diseases is important as treatment will be most successful before substantial neuronal loss. Here, we reasoned that functional brain changes as measured using functional magnetic resonance imaging (fMRI) will precede neurodegeneration. Three independent cohorts of patients with the genetic mutation leading to Huntington's Disease (HD) but without any clinical symptoms and matched controls performed three different fMRI tasks: Sequential finger tapping engaged the motor system, which is primarily affected by HD, whereas a working-memory task and a task aiming to induce irritation represented the range of low- and high-level cognitive functions also affected by HD. Each diagnostic group of every cohort included 11-16 subjects. After segmentation into 76 cortical and 14 subcortical regions, we extracted functional connectivity patterns through pairwise correlation between the signals in the regions. The resulting coefficients were directly embedded as input to a pattern classifier aiming to separate controls from gene mutation carriers. Alternatively, graph-theory measures such as degree and clustering coefficient were used as features for the discrimination. Classification accuracy never outperformed the accuracy of a grouping based on parameter estimates from a general-linear model approach or a grouping based on features extracted from anatomical images as reported in a previous analysis. Despite good within-subject stability between two runs of the same task, a high between-subject variability led to chance-level accuracy. These results indicate that standard graph-metrics are insufficient to detect subtle disease related changes when within-group variability is high. Developing methods that reduce variability related to noise should be the focus of future studies.

Attention-Deficit/Hyperactivity Disorder (ADHD) is marked by inhibitory and attentional deficits which can persist into adulthood. Those deficits have been associated with dysfunctional fronto-striatal and fronto-parietal circuits. The present study sought to delineate neural correlates of component specific inhibitory deficits in adult ADHD using functional magnetic resonance imaging (fMRI). 20 adult ADHD patients and 24 matched healthy controls were included. Brain activation was assessed during three stages of behavioral inhibition, i.e. interference inhibition (Simon task), action withholding (Go/no-go task) and action cancelation (Stop-signal task). Behaviorally, ADHD patients were affected in all tasks. Impaired interference inhibition was associated with hypoactivation in parietal and medial frontal regions. During action withholding and cancelation ADHD patients displayed hypoactivation in a fronto-striatal network. These findings support the notion of at least two disturbed neural circuits in ADHD differentially associated with deficits in separate inhibitory subcomponents. Thereby, deficits in inhibitory subcomponents which are closely connected to response interference were related to hypofunction in more attention related circuits, while stopping related deficits were rather associated with hypofunction in inhibitory circuits.

Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n = 17) or late MS (n = 17) (contrast I), low (n = 20) or high (n = 20) white matter lesion load (contrast II), and benign MS (BMS, n = 13) or non-benign MS (NBMS, n = 13) (contrast III) scanned on a single 1.5 T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application.

Fully automated machine learning methods based on structural magnetic resonance imaging (MRI) data can assist radiologists in the diagnosis of Alzheimer's disease (AD). These algorithms require large data sets to learn the separation of subjects with and without AD. Training and test data may come from heterogeneous hardware settings, which can potentially affect the performance of disease classification. A total of 518 MRI sessions from 226 healthy controls and 191 individuals with probable AD from the multicenter Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to investigate whether grouping data by acquisition hardware (i.e. vendor, field strength, coil system) is beneficial for the performance of a support vector machine (SVM) classifier, compared to the case where data from different hardware is mixed. We compared the change of the SVM decision value resulting from (a) changes in hardware against the effect of disease and (b) changes resulting simply from rescanning the same subject on the same machine. Maximum accuracy of 87% was obtained with a training set of all 417 subjects. Classifiers trained with 95 subjects in each diagnostic group and acquired with heterogeneous scanner settings had an empirical detection accuracy of 84.2±2.4% when tested on an independent set of the same size. These results mirror the accuracy reported in recent studies. Encouragingly, classifiers trained on images acquired with homogenous and heterogeneous hardware settings had equivalent cross-validation performances. Two scans of the same subject acquired on the same machine had very similar decision values and were generally classified into the same group. Higher variation was introduced when two acquisitions of the same subject were performed on two scanners with different field strengths. The variation was unbiased and similar for both diagnostic groups. The findings of the study encourage the pooling of data from different sites to increase the number of training samples and thereby improving performance of disease classifiers. Although small, a change in hardware could lead to a change of the decision value and thus diagnostic grouping. The findings of this study provide estimators for diagnostic accuracy of an automated disease diagnosis method involving scans acquired with different sets of hardware. Furthermore, we show that the level of confidence in the performance estimation significantly depends on the size of the training sample, and hence should be taken into account in a clinical setting.

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision-recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.

Huntington disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat. Its length can be used to estimate the time of clinical diagnosis, which is defined by overt motor symptoms. Non-motor symptoms begin before motor onset, and involve changes in hypothalamus-regulated functions such as sleep, emotion and metabolism. Therefore we hypothesized that hypothalamic changes occur already prior to the clinical diagnosis. We performed voxel-based morphometry and logistic regression analyses of cross-sectional MR images from 220 HD gene carriers and 75 controls in the Predict-HD study. We show that changes in the hypothalamic region are detectable before clinical diagnosis and that its grey matter contents alone are sufficient to distinguish HD gene carriers from control cases. In conclusion, our study shows, for the first time, that alterations in grey matter contents in the hypothalamic region occur at least a decade before clinical diagnosis in HD using MRI.

The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention. Recently, Davatzikos et al. (2009) supported the hypothesis that pathologic atrophy in AD is an accelerated aging process, implying accelerated brain atrophy. In order to recognize faster brain atrophy, a model of healthy brain aging is needed first. Here, we introduce a framework for automatically and efficiently estimating the age of healthy subjects from their T(1)-weighted MRI scans using a kernel method for regression. This method was tested on over 650 healthy subjects, aged 19-86 years, and collected from four different scanners. Furthermore, the influence of various parameters on estimation accuracy was analyzed. Our age estimation framework included automatic preprocessing of the T(1)-weighted images, dimension reduction via principal component analysis, training of a relevance vector machine (RVM; Tipping, 2000) for regression, and finally estimating the age of the subjects from the test samples. The framework proved to be a reliable, scanner-independent, and efficient method for age estimation in healthy subjects, yielding a correlation of r=0.92 between the estimated and the real age in the test samples and a mean absolute error of 5 years. The results indicated favorable performance of the RVM and identified the number of training samples as the critical factor for prediction accuracy. Applying the framework to people with mild AD resulted in a mean brain age gap estimate (BrainAGE) score of +10 years.

Our understanding of how genotype determines phenotype in primary dystonia is limited. Familial young-onset primary dystonia is commonly due to the DYT1 gene mutation. A critical question, given the 30% penetrance of clinical symptoms in DYT1 mutation carriers, is why the same genotype leads to differential clinical expression and whether non-DYT1 adult-onset primary dystonia, with and without family history share pathophysiological mechanisms with DYT1 dystonia. This study examines the relationship between dystonic phenotype and the DYT1 gene mutation by monitoring whole-brain structure using voxel-based morphometry. We acquired magnetic resonance imaging data of symptomatic and asymptomatic DYT1 mutation carriers, of non-DYT1 primary dystonia patients, with and without family history and control subjects with normal DYT1 alleles. By crossing the factors genotype and phenotype we demonstrate a significant interaction in terms of brain anatomy confined to the basal ganglia bilaterally. The explanation for this effect differs according to both gene and dystonia status: non-DYT1 adult-onset dystonia patients and asymptomatic DYT1 carriers have significantly larger basal ganglia compared to healthy subjects and symptomatic DYT1 mutation carriers. There is a significant negative correlation between severity of dystonia and basal ganglia size in DYT1 mutation carriers. We propose that differential pathophysiological and compensatory mechanisms lead to brain structure changes in non-DYT1 primary adult-onset dystonias and DYT1 gene carriers. Given the range of age of onset, there may be differential genetic modulation of brain development that in turn determines clinical expression. Alternatively, a DYT1 gene dependent primary defect of motor circuit development may lead to stress-induced remodelling of the basal ganglia and hence dystonia.

Dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data allows one to make inferences about the architecture of distributed networks in the brain, in terms of effective connectivity. fMRI data are usually acquired using echo planar imaging (EPI). EPI sequences typically acquire slices at different times over a few seconds. DCM, in its original inception, was not informed about these slice timings and assumed that all slices were acquired simultaneously. It has been shown that DCM can cope with slice timing differences of up to 1 s. However, many fMRI studies employ a repetition time (TR) of 3 to 5 s, which precludes a straightforward DCM of these data. We show that this limitation can be overcome easily by including slice timing in the DCM. Using synthetic data we show that the extended DCM furnishes veridical posterior means, even if there are large slice-timing differences. Model comparisons show that, in general, the extended DCM out-performs the original model. We contrast the modeling of slice timing, in the context of DCM, with the less effective approach of ‘slice-timing correction’, prior to modeling. We apply our procedure to real data and show that slice timings are important parameters. We conclude that, generally, one should use DCM with slice timing.

The neuronal correlates of handedness are still poorly understood. Here we used event-related functional magnetic resonance imaging to investigate the impact of handedness on neuronal activation of the primary sensorimotor cortex, supplementary motor area and dorsal premotor cortex during simple unilateral and bilateral finger movements. In 16 right-handed and 16 left-handed individuals, we mapped changes in regional neuronal activity while participants responded to four symbolic cues presented in a pseudorandom order. According to pre-specified cues, they pressed a button with their right, left or both index fingers or withheld a response. For unilateral right index finger button presses, reaction times, motor and premotor activity were the same for both right- and left-handers. Compared with right-handers, left-handers had shorter reaction times with unilateral left index finger button presses, along with greater activation of the supplementary motor area and right frontal opercular cortex. Simultaneous bilateral compared with unilateral button presses led to a relative increase of activity in the right and left dorsal premotor cortex and the right primary sensorimotor cortex in right but not left-handers. Neither right nor left-handers showed any tendency during bilateral button presses towards faster responses with the dominant hand and the reaction times were equal in the two groups. Therefore, we conclude that the relative increase of activity in dorsal premotor and right primary sensorimotor cortices in right-handers represents a genuine difference in bimanual motor control related to handedness.