The preparation and readiness for voluntary movement: a high-field event-related fMRI study of the Bereitschafts-BOLD response

Activity within motor areas of the cortex begins to increase 1 to 2 s prior to voluntary self-initiated movement (termed the Bereitschaftspotential or readiness potential). There has been much speculation and debate over the precise source of this early premovement activity as it is important for understanding the roles of higher order motor areas in the preparation and readiness for voluntary movement. In this study, we use high-field (3-T) event-related fMRI with high temporal sampling (partial brain volumes every 250 ms) to specifically examine hemodynamic response time courses during the preparation, readiness, and execution of purely self-initiated voluntary movement. Five right-handed healthy volunteers performed a rapid sequential finger-to-thumb movement performed at self-determined times (12-15 trials). Functional images for each trial were temporally aligned and the averaged time series for each subject was iteratively correlated with a canonical hemodynamic response function progressively shifted in time. This analysis method identified areas of activation without constraining hemodynamic response timing. All subjects showed activation within frontal mesial areas, including supplementary motor area (SMA) and cingulate motor areas, as well as activation in left primary sensorimotor areas. The time courses of hemodynamic responses showed a great deal of variability in shape and timing between subjects; however, four subjects clearly showed earlier relative hemodynamic responses within SMA/cingulate motor areas compared with left primary motor areas. These results provide further evidence that the SMA and cingulate motor areas are major contributors to early stage premovement activity and play an important role in the preparation and readiness for voluntary movement. (C) 2003 Elsevier Inc. All rights reserved.

Event-related FMRI analysis with maximum correlation modelling

Functional magnetic resonance imaging (FMRI) analysis methods can be quite generally divided into hypothesis-driven and data-driven approaches. The former are utilised in the majority of FMRI studies, where a specific haemodynamic response is modelled utilising knowledge of event timing during the scan, and is tested against the data using a t test or a correlation analysis. These approaches often lack the flexibility to account for variability in haemodynamic response across subjects and brain regions which is of specific interest in high-temporal resolution event-related studies. Current data-driven approaches attempt to identify components of interest in the data, but currently do not utilise any physiological information for the discrimination of these components. Here we present a hypothesis-driven approach that is an extension of Friman's maximum correlation modelling method (Neurolmage 16, 454-464, 2002) specifically focused on discriminating the temporal characteristics of event-related haemodynamic activity. Test analyses, on both simulated and real event-related FMRI data, will be presented.

Mental rotation processes in mathematically gifted boys: An fMRI investigation

The functional brain organisation of mathematically gifted adolescents may be different from those of average mathematical ability. In this study we used fMRI to examine the neural circuitry that mediates the performance of mathematically gifted boys and average ability controls while engaged in mental rotation. Eight math gifted male adolescents and five average ability male adolescents were presented 18 control and 18 mental rotation trials in two separate blocks. Participants selected one of four test stimuli to match the target stimulus by pressing one of four fibreoptic buttons. The control task required a simple 'best match' for the target stimulus. EPI scans were acquired on a 3-T MR scanner and a fixed effects statistical analysis (SPM99) was used to identify areas of significant activation in the rotation tasks, for the two groups. The results indicate that during mental rotation both groups activate the parietal lobes bilaterally, though to different levels. Moreover, the math gifted are uniformly bilateral in their pattern of activation, and engage some anterior regions not found in those of average ability. These regions include bilateral prefrontal cortex and the right anterior cingulate, which may serve to heighten concentration, and to optimise the pre-planning of purposeful actions.

The fMRI response of the LGN and V1 to cardinal red-green, blue-yellow, and achromatic visual stimuli

We compared the responsiveness of the LGN and the early retinotopic cortical areas to stimulation of the two cone-opponent systems (red - green and blue - yellow) and the achromatic system. This was done at two contrast levels to control for any effect of contrast. MR images were acquired on seven subjects with a 4T Bruker MedSpec scanner. The early visual cortical areas were localised by phase encoded retinotopic mapping with a volumetric analysis (Dumoulin et al, 2003 NeuroImage 18 576 - 587). We initially located the LGN in four subjects by using flickering stimuli in a separate scanning session, but subsequently identified it using the experimental stimuli. Experimental stimuli were sine-wave counterphasing rings (2 Hz, 0.5 cycle deg-1), cardinal for the selective activation of the L/M cone-opponent (RG), S cone-opponent (BY), and achromatic (Ach) systems. A region of interest analysis was performed. When presented at equivalent absolute contrasts (cone contrast = 5% - 6%), the BOLD response of the LGN is strongest to isoluminant red - green stimuli and weakest to blue - yellow stimuli, with the achromatic response falling in between. Area V1, on the other hand, responds best to both chromatic stimuli, with the achromatic response falling below. The key change from the LGN to V1 is a dramatic boost in the relative blue - yellow response, which occurred at both contrast levels used. This greatly enhanced cortical response to blue - yellow relative to the red - green and achromatic responses may be due to an increase in cell number and/or cell response between the LGN and V1. We speculate that the effect might reflect the operation of contrast constancy across colour mechanisms at the cortical level.

An fMRI study indicating increased cortical recruitment during a cognitive interference task in Huntington's disease patients

We used an event related fMRI design to study the BOLD response in Huntington’s disease (HD) patients during performance of a Simon interference task. We hypothesised that HD patients will demonstrate significantly slower RTs than controls, and that there will be significant differences in the pattern of brain activation between groups. Seventeen HD patients and 15 age and sex matched controls were scanned using 3T GE scanner (FOV = 24 cm2; TE = 40 ms; TR = 3 s; FA = 60°; slice thickness = 6 mm; in-plane resolution = 1.88x1.88 mm2). The task involved two activation conditions, namely congruent (for example, left pointing arrow appearing on the left side of the screen) and incongruent (for example, left pointing arrow appearing on the right side of the screen), and a baseline condition. Each stimulus was presented for 2500 ms followed by a blank screen for 500 ms. Subjects were instructed to press a button using the same hand as indicated by the direction of the arrow head and were given 3000 ms to respond. Data analysis was performed using SPM2 with a random effects analysis model. For each subject parameter estimates for combined task conditions (congruent and incongruent combined) were calculated. Comparisons such as these, based on block designs, have superior statistical power for detecting subtle changes in the BOLD response anywhere in the brain. The activations reported are significant at PFDR_corr

EEG-based Brain-Computer Interfaces: An Overview of Basic Concepts and Clinical Applications in Neurorehabilitation

Some patients are no longer able to communicate effectively or even interact with the outside world in ways that most of us take for granted. In the most severe cases, tetraplegic or post-stroke patients are literally `locked in` their bodies, unable to exert any motor control after, for example, a spinal cord injury or a brainstem stroke, requiring alternative methods of communication and control. But we suggest that, in the near future, their brains may offer them a way out. Non-invasive electroencephalogram (EEG)-based brain-computer interfaces (BCD can be characterized by the technique used to measure brain activity and by the way that different brain signals are translated into commands that control an effector (e.g., controlling a computer cursor for word processing and accessing the internet). This review focuses on the basic concepts of EEG-based BC!, the main advances in communication, motor control restoration and the down-regulation of cortical activity, and the mirror neuron system (MNS) in the context of BCI. The latter appears to be relevant for clinical applications in the coming years, particularly for severely limited patients. Hypothetically, MNS could provide a robust way to map neural activity to behavior, representing the high-level information about goals and intentions of these patients. Non-invasive EEG-based BCIs allow brain-derived communication in patients with amyotrophic lateral sclerosis and motor control restoration in patients after spinal cord injury and stroke. Epilepsy and attention deficit and hyperactive disorder patients were able to down-regulate their cortical activity. Given the rapid progression of EEG-based BCI research over the last few years and the swift ascent of computer processing speeds and signal analysis techniques, we suggest that emerging ideas (e.g., MNS in the context of BC!) related to clinical neuro-rehabilitation of severely limited patients will generate viable clinical applications in the near future.

An event-related fMRI study of cognitive deficits in Huntington's disease

The BOLD contrast signal history determined by lagged Unear correlation has a significant contribution to functional connectivity in activation data sets. It has been demonstrated that in resting state fMRI data, the major contribution to synchronous correlation between functionally connected areas arises from low frequency contributions (

Premovement activity of the pre-supplementary motor area and the readiness for action: Studies of time-resolved event-related functional MRI

The supplementary motor area (SMA) is thought to play in important role in the preparation and organisation of voluntary movement. It has long been known that cortical activity begins to increase up to 2 s prior to voluntary self-initiated movement. This increasing premovement activity measured in EEG is known as the Bereitschaftspotential or readiness potential. Modern functional brain imaging methods, using event-related and time-resolved functional MRI techniques, are beginning to reveal the role of the SMA, and in particular the more anterior pre-SMA, in premovement activity associated with the readiness for action. In this paper we review recent studies using event-related time-resolved fMRI methods to examine the time-course of activation changes within the SMA throughout the preparation, readiness and execution of action. These studies suggest that the preSMA plays a common role in encoding or representing actions prior to our own voluntary self-initiated movements, during motor imagery, and from the observation of others' actions. We suggest that the pre-SMA generates and encodes motor representations which are then maintained in readiness for action. (c) 2005 Elsevier B.V. All rights reserved.

The selection of intended actions and the observation of others' actions: A time-resolved fMRI study

Whenever we plan, imagine, or observe an action, the motor systems that would be involved in preparing and executing that action are similarly engaged. The way in which such common motor activation is formed, however, is likely to differ depending on whether it arises from our own intentional selection of action or from the observation of another's action. In this study, we use time-resolved event-related functional MRI to tease apart neural processes specifically related to the processing of observed actions, the selection of our own intended actions, the preparation for movement, and motor response execution. Participants observed a finger gesture movement or a cue indicating they should select their own finger gesture to perform, followed by a 5-s delay period; participants then performed the observed or self-selected action. During the preparation and readiness for action, prior to initiation, we found activation in a common network of higher motor areas, including dorsal and ventral premotor areas and the pre-supplementary motor area (pre-SMA); the more caudal SMA showed greater activation during movement execution. Importantly, the route to this common motor activation differed depending on whether participants freely selected the actions to perform or whether they observed the actions performed by another person. Observation of action specifically involved activation of inferior and superior parietal regions, reflecting involvement of the dorsal visual pathway in visuomotor processing required for planning the action. In contrast, the selection of action specifically involved the dorsal lateral prefrontal and anterior cingulate cortex, reflecting the role of these prefrontal areas in attentional selection and guiding the selection of responses. (c) 2005 Elsevier Inc. All rights reserved.

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

Functional magnetic resonance imaging (fMRI) based on BOLD signal has been used to indirectly measure the local neural activity induced by cognitive tasks or stimulation. Most fMRI data analysis is carried out using the general linear model (GLM), a statistical approach which predicts the changes in the observed BOLD response based on an expected hemodynamic response function (HRF). In cases when the task is cognitively complex or in cases of diseases, variations in shape and/or delay may reduce the reliability of results. A novel exploratory method using fMRI data, which attempts to discriminate between neurophysiological signals induced by the stimulation protocol from artifacts or other confounding factors, is introduced in this paper. This new method is based on the fusion between correlation analysis and the discrete wavelet transform, to identify similarities in the time course of the BOLD signal in a group of volunteers. We illustrate the usefulness of this approach by analyzing fMRI data from normal subjects presented with standardized human face pictures expressing different degrees of sadness. The results show that the proposed wavelet correlation analysis has greater statistical power than conventional GLM or time domain intersubject correlation analysis. (C) 2010 Elsevier B.V. All rights reserved.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Neurodynamics of an election

Variables influencing decision-making in real settings, as in the case of voting decisions, are uncontrollable and in many times even unknown to the experimenter. In this case, the experimenter has to study the intention to decide (vote) as close as possible in time to the moment of the real decision (election day). Here, we investigated the brain activity associated with the voting intention declared 1 week before the election day of the Brazilian Firearms Control Referendum about prohibiting the commerce of firearms. Two alliances arose in the Congress to run the campaigns for YES (for the prohibition of firearm commerce) and NO (against the prohibition of firearm commerce) voting. Time constraints imposed by the necessity of studying a reasonable number (here, 32) of voters during a very short time (5 days) made the EEG the tool of choice for recording the brain activity associated with voting decision. Recent fMRI and EEG studies have shown decision-making as a process due to the enrollment of defined neuronal networks. In this work, a special EEG technique is applied to study the topology of the voting decision-making networks and is compared to the results of standard ERP procedures. The results show that voting decision-making enrolled networks in charge of calculating the benefits and risks of the decision of prohibiting or allowing firearm commerce and that the topology of such networks was vote-(i.e., YES/NO-) sensitive. (C) 2010 Elsevier B.V. All rights reserved.