Task-specific Brain Activation Research Articles

Fear-induced brain activations distinguish anxious and trauma-exposed brains

Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

Space-Time-Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography.

Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space–time–frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time–frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger.

Resting-state electroencephalogram in learning-disabled children: power and connectivity analyses.

The neurophysiological underpinnings of learning disabilities remain unknown. In this clinical study, we recorded electroencephalograms for a large sample of children with learning disabilities (LD) and healthy control children (n=216) during resting states in which the eyes were either open or closed. We calculated the power and lagged phase coherence in six main frequency bands (delta, theta, lower and upper alpha, and lower and upper beta) to re-evaluate the question of whether children with LD show frontal theta power increases and posterior alpha band decreases on the basis of patterns of electroencephalogram oscillation, which could then be considered as evidence for the so-called 'maturational delay hypothesis.' We identified a general (not restricted to frontal electrodes) power increase in the theta band and no accompanying concomitant alpha band decrease at the posterior electrode position. In addition, we observed increased beta band power at frontal electrodes for LD children. With respect to lagged phase coherence, which is a coherence measure not influenced by volume conduction, we identified decreased coherence for children with LD in the upper alpha band during the eyes closed condition. We interpret this LD-specific resting-state activation pattern as indicating a suboptimally functioning neural resting-state network that provides a detrimental 'starting point' for task-specific brain activations.

Neuroplastic Changes in Older Adults Performing Cooperative Hand Movements.

The aim of this study was to examine whether older adults use the same task-specific brain activation patterns during two different bimanual hand movement tasks as younger adults. Functional magnetic resonance brain imaging was performed in 18 younger (mean age: 30.3 ± 3.6 years) and 11 older adults (62.6 ± 6.8 years) during the execution of cooperative (mimicking opening a bottle) or non-cooperative (bimanual pro-/supination) hand movements. We expected to see a stronger task-specific involvement of the secondary somatosensory cortex (S2) during cooperative hand movements in older compared to younger adults. However, S2 activation was present in both groups during the cooperative task and was only significantly stronger compared to the non-cooperative task in younger adults. In a whole brain-analysis, the contrast between older and younger adults revealed a hyperactivation of the bilateral dorsal premotor cortex (precentral gyrus), right thalamus, right frontal operculum, anterior cingulate cortex, and supplementary motor areas in older adults (p < 0.001), with some of them being visible after correcting for age. Age was positively associated with fMRI signal changes in these regions across the whole sample. Older adults showed reduced gray matter volume but not in regions showing task-related fMRI group differences. We also found an increase in functional connectivity between SMA, M1, thalamus, and precentral gyri in older adults. In contrast, younger adults showed hyperconnectivity between S2 and S1. We conclude that older compared to younger adults show age-related functional neuroplastic changes in brain regions involved in motor control and performance.

Real-time EEG feedback during simultaneous EEG–fMRI identifies the cortical signature of motor imagery

Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain–computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

Oxytocin's neurochemical effects in the medial prefrontal cortex underlie recovery of task-specific brain activity in autism: a randomized controlled trial.

The neuropeptide oxytocin may be an effective therapeutic strategy for the currently untreatable social and communication deficits associated with autism. Our recent paper reported that oxytocin mitigated autistic behavioral deficits through the restoration of activity in the ventromedial prefrontal cortex (vmPFC), as demonstrated with functional magnetic resonance imaging (fMRI) during a socio-communication task. However, it is unknown whether oxytocin exhibited effects at the neuronal level, which was outside of the specific task examined. In the same randomized, double-blind, placebo-controlled, within-subject cross-over clinical trial in which a single dose of intranasal oxytocin (24 IU) was administered to 40 men with high-functioning autism spectrum disorder (UMIN000002241/000004393), we measured N-acetylaspartate (NAA) levels, a marker for neuronal energy demand, in the vmPFC using 1H-magnetic resonance spectroscopy (1H-MRS). The differences in the NAA levels between the oxytocin and placebo sessions were associated with oxytocin-induced fMRI signal changes in the vmPFC. The oxytocin-induced increases in the fMRI signal could be predicted by the NAA differences between the oxytocin and placebo sessions (P=0.002), an effect that remained after controlling for variability in the time between the fMRI and 1H-MRS scans (P=0.006) and the order of administration of oxytocin and placebo (P=0.001). Furthermore, path analysis showed that the NAA differences in the vmPFC triggered increases in the task-dependent fMRI signals in the vmPFC, which consequently led to improvements in the socio-communication difficulties associated with autism. The present study suggests that the beneficial effects of oxytocin are not limited to the autistic behavior elicited by our psychological task, but may generalize to other autistic behavioral problems associated with the vmPFC.

Neuroimaging predictors of cognitive performance across a standardized neurocognitive battery.

The advent of functional MRI (fMRI) enables the identification of brain regions recruited for specific behavioral tasks. Most fMRI studies focus on group effects in single tasks, which limits applicability where assessment of individual differences and multiple brain systems is needed. We demonstrate the feasibility of concurrently measuring fMRI activation patterns and performance on a computerized neurocognitive battery (CNB) in 212 healthy individuals at 2 sites. Cross-validated sparse regression of regional brain amplitude and extent of activation were used to predict concurrent performance on 6 neurocognitive tasks: abstraction/mental flexibility, attention, emotion processing, and verbal, face, and spatial memory. Brain activation was task responsive and domain specific, as reported in previous single-task studies. Prediction of performance was robust for most tasks, particularly for abstraction/mental flexibility and visuospatial memory. The feasibility of administering a comprehensive neuropsychological battery in the scanner was established, and task-specific brain activation patterns improved prediction beyond demographic information. This benchmark index of performance-associated brain activation can be applied to link brain activation with neurocognitive performance during standardized testing. This first step in standardizing a neurocognitive battery for use in fMRI may enable quantitative assessment of patients with brain disorders across multiple cognitive domains. Such data may facilitate identification of neural dysfunction associated with poor performance, allow for identification of individuals at risk for brain disorders, and help guide early intervention and rehabilitation of neurocognitive deficits.

Variability of Clinical Functional MR Imaging Results: A Multicenter Study

To investigate intersite variability of clinical functional magnetic resonance (MR) imaging, including influence of task standardization on variability and use of various parameters to inform the clinician whether the reliability of a given functional localization is high or low. Local ethics committees approved the study; all participants gave written informed consent. Eight women and seven men (mean age, 40 years) were prospectively investigated at three experienced functional MR sites with 1.5- (two sites) or 3-T (one site) MR. Nonstandardized motor and highly standardized somatosensory versions of a frequently requested clinical task (localization of the primary sensorimotor cortex) were used. Perirolandic functional MR variability was assessed (peak activation variability, center of mass [COM] variability, intraclass correlation values, overlap ratio [OR], activation size ratio). Data quality measures for functional MR images included percentage signal change (PSC), contrast-to-noise ratio (CNR), and head motion parameters. Data were analyzed with analysis of variance and a correlation analysis. Localization of perirolandic functional MR activity differed by 8 mm (peak activity) and 6 mm (COM activity) among sites. Peak activation varied up to 16.5 mm (COM range, 0.4-16.5 mm) and 45.5 mm (peak activity range, 1.8-45.5 mm). Signal strength (PSC, CNR) was significantly lower for the somatosensory task (mean PSC, 1.0% ± 0.5 [standard deviation]; mean CNR, 1.2 ± 0.4) than for the motor task (mean PSC, 2.4% ± 0.8; mean CNR, 2.9 ± 0.9) (P < .001, both). Intersite variability was larger with low signal strength (negative correlations between signal strength and peak activation variability) even if the task was highly standardized (mean OR, 22.0% ± 18.9 [somatosensory task] and 50.1% ± 18.8 [motor task]). Clinical practice and clinical functional MR biomarker studies should consider that the center of task-specific brain activation may vary up to 16.5 mm, with the investigating site, and should maximize functional MR signal strength and evaluate reliability of local results with PSC and CNR.

Adult age-dependent differences in resting-state connectivity within and between visual-attention and sensorimotor networks

Healthy aging is accompanied by structural and functional changes in the brain, among which a loss of neural specificity (i.e., dedifferentiation) is one of the most consistent findings. Little is known, however, about changes in interregional integration underlying a dedifferentiation across different functional systems. In a large sample (n = 399) of healthy adults aged from 18 to 85 years, we analyzed age-dependent differences in resting-state (RS) (task-independent) functional connectivity (FC) of a set of brain regions derived from a previous fMRI study. In that study, these regions had shown an age-related loss of activation specificity in visual-attention (superior parietal area 7A and dorsal premotor cortex) or sensorimotor (area OP4 of the parietal operculum) tasks. In addition to these dedifferentiated regions, the FC analysis of the present study included “task-general” regions associated with both attention and sensorimotor systems (rostral supplementary motor area and bilateral anterior insula) as defined via meta-analytical co-activation mapping. Within this network, we observed both selective increases and decreases in RS-FC with age. In line with regional activation changes reported previously, we found diminished anti-correlated FC for inter-system connections (i.e., between sensorimotor-related and visual attention-related regions). Our analysis also revealed reduced FC between system-specific and task-general regions, which might reflect age-related deficits in top-down control possibly leading to dedifferentiation of task-specific brain activity. Together, our results underpin the notion that RS-FC changes concur with regional activity changes in the healthy aging brain, presumably contributing jointly to age-related behavioral changes.

Age-related changes of task-specific brain activity in normal aging

An important question in healthcare for older patients is whether age-related changes in cortical reorganization can be measured with advancing age. This study investigated the factors behind such age-related changes, using time-frequency analysis of event-related potentials (ERPs). We hypothesized that brain rhythms was affected by age-related changes, which could be reflected in the ERP indices. An oddball task was conducted in two experimental groups, namely young participants ( N = 15; mean age 23.7 ± 2.8 years) and older participants ( N = 15; mean age 70.1 ± 7.9 years). Two types of stimuli were used: the target (1 kHz frequency) and standard (2 kHz frequency). We scrutinized three ERP indices: event-related spectral power (ERPSP), inter-trial phase-locking (ITPL), and event-related cross-phase coherence (ERPCOH). Both groups performed equally well for correct response rate. However, the results revealed a statistically significant age difference for inter-trial comparison. Compared with the young, the older participants showed the following age-related changes: (a) power activity decreased; however, an increase was found only in the late (P3, 280–450 ms) theta (4–7 Hz) component over the bilateral frontal and temporo-frontal areas; (b) low phase-locking in the early (N1, 80–140 ms) theta band over the parietal/frontal (right) regions appeared; (c) the functional connections decreased in the alpha (7–13 Hz) and beta (13–30 Hz) bands, but no difference emerged in the theta band between the two groups. These results indicate that age-related changes in task-specific brain activity for a normal aging population can be depicted using the three ERP indices.

Established Donepezil Treatment Modulates Regional Brain Activation in Early Alzheimers Disease

Neuroimaging studies of cholinesterase inhibitor (ChEI) treatment in Alzheimer's disease (AD) have shown the different short and long term actions of ChEIs. fMRI studies of the ChEI donepezil have focused on its short to medium term action without exploring the effects of established treatment. In this exploratory study the effect of 20 weeks donepezil treatment on regional brain activity was measured with fMRI in patients with mild AD. Twelve patients with probable AD and nine age-matched controls were assessed with a Pyramids and Palm Trees semantic association fMRI paradigm and an n-back working memory fMRI paradigm. In the patient group only, the assessment was repeated after 20 weeks of treatment. After treatment, differences from normal healthy elderly became more pronounced. There was also a spread of deactivation which at retest was detectable in task relevant areas. Behaviourally, however, there were no significant differences between group baseline and retest scores, with a range of performance probably reflecting variation in drug efficacy across patients. Parametric analyses established that increased behavioural scores at retest correlated significantly with higher activation levels in non task relevant areas. Behavioural stability with donepezil treatment was not paralleled by the pattern of improved task specific brain activation reported in similar studies of other ChEIs. This is arguably related to the different mechanisms of action of the ChEIs and might be a clinical correlate of the reported synaptic upregulation following long term donepezil treatment.

Unbiased clustering of true neural components to reveal task specific brain activations

Event Abstract Back to Event Unbiased clustering of true neural components to reveal task specific brain activations Nikhilesh Natraj1*, J. C. Mizelle1 and Lewis Wheaton1 1 Georgia Institute of Technology, School of Applied Physiology, United States We sought to evaluate whether clustering independent components (ICs) of EEG neural activation can be a measure of quantifying the networks involved in planning skilled hand movements. Fifteen normal right-handed subjects were instructed to perform simple movements and complex tool use pantomime movements while recording 64-channel electroencephalography (EEG) and electromyography (EMG) from forearm and hand muscles. Both movements were similar in kinematics but differed in context (naively performing a twist gesture vs. pantomiming a twist screwdriver; naively performing a push gesture vs. pantomiming a novel "yankee" style push screwdriver). Subjects were first instructed to perform the naive movements, and then perform the pantomimes after practicing with the two types of screwdrivers. Trials were marked for EMG onset and epoched 3.5s - EMG -0.5s. We then decomposed the EEG signal into its statistically most independent components, using the popular ICA algorithm. We identified the strongest component that contributed to power of EEG signal just before movement [500ms to EMG onset] in a pantomime twist condition, and sought significant correlations (r>0.9) to other components in all subjects and conditions. The expectation is that we can find unique, condition specific activations in single subjects based on this unbiased, automated technique. Preliminary results revealed a cluster of highly correlated (r>0.9) left hemispheric parietal components for the pantomime twist condition, across all subjects. This is in line with prior research, indicating that the parietal areas store representations of previously learned tool movements. This component was not well correlated in other conditions, across subjects. Neuroanatomical correlates of the identified components were calculated by source localization and estimating dipoles of neural activity. This enabled us to characterize True Neural Components (TNC) in the data. Correlating ICs appears to be a robust method of blindly clustering TNC in multivariable datasets. We will further test this method by correlating template ICs for all conditions to find neural evidence of other exclusive motor related components. Conference: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences, Atlanta , United States, 5 Mar - 7 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Natraj N, Mizelle JC and Wheaton L (2010). Unbiased clustering of true neural components to reveal task specific brain activations. Front. Neurosci. Conference Abstract: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences. doi: 10.3389/conf.fnins.2010.04.00067 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Mar 2010; Published Online: 17 Mar 2010. * Correspondence: Nikhilesh Natraj, Georgia Institute of Technology, School of Applied Physiology, Atlanta, United States, niki@gatech.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Nikhilesh Natraj J. C Mizelle Lewis Wheaton Google Nikhilesh Natraj J. C Mizelle Lewis Wheaton Google Scholar Nikhilesh Natraj J. C Mizelle Lewis Wheaton PubMed Nikhilesh Natraj J. C Mizelle Lewis Wheaton Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Provisional hypotheses for the molecular genetics of cognitive development: Imaging genetic pathways in the anterior cingulate cortex

Brain imaging genetic research involves a multitude of methods and spans many traditional levels of analysis. Given the vast permutations among several million common genetic variants with thousands of brain tissue voxels and a wide array of cognitive tasks that activate specific brain systems, we are prompted to develop specific hypotheses that synthesize converging evidence and state clear predictions about the anatomical sources, magnitude and direction (increases vs. decreases) of allele- and task-specific brain activity associations. To begin to develop a framework for shaping our imaging genetic hypotheses, we focus on previous results and the wider imaging genetic literature. Particular emphasis is placed on converging evidence that links system-level and biochemical studies with models of synaptic function. In shaping our own imaging genetic hypotheses on the development of Attention Networks, we review relevant literature on core models of synaptic physiology and development in the anterior cingulate cortex.

Comment on "Detecting Awareness in the Vegetative State"

In a report of a single patient in a persistent vegetative state, Owen et al. (Brevia, 8 September 2006, p. 1402) claimed that the presence of task-specific brain activation in response to verbal command implies both covert conscious awareness and a capacity for intention. We argue that neither can be securely inferred from the evidence presented.

Dynamic scanning of 15O-butanol with positron emission tomography can identify regional cerebral activations

To determine task-specific activations of the human brain in individual subjects, we applied pixel-by-pixel t-map statistics to the regional cerebral perfusion data obtained sequentially by dynamic scanning of [15O]-butanol with positron emission tomography (PET). The listmode data were binned into frames of 2 sec, and multiple corresponding pixel-by-pixel activation-minus-control subtractions were used for t-map calculation. The subtraction frames covering 10-40 sec after tracer arrival in the brain showed the activation-related increase of regional cerebral perfusion. A mismatch of the activation and control data by 2 sec resulted in a mean error of <5% of the integrated activity increase. To validate these results, we simulated images with a spatial resolution and signal-to-noise ratio equivalent to that of the [15O]-butanol subtraction images. By means of these simulated images, we determined the minimal data requirements for t-map analysis, the degree of spatial correlations in the image matrix, and the distribution of noise in the t-maps. The simulation results provided a measure to estimate the significance of regional cerebral perfusion changes recorded with [15O]-butanol. The location and spatial extent of regional cerebral activations obtained from dynamic data corresponded closely to those obtained with quantitative measurements of regional cerebral blood flow (rCBF). Our results show that statistical parametric mapping of [15O]-butanol scanning data allows the detection of significant, task-specific brain activations in single activation-control comparisons in individual subjects.

Failure at task-specific regional brain activation: new conceptualization of a disease entity.

The current diagnostic system in psychiatry is symptom-based, as described in detail in DSM-III-R. In other branches of medicine the logic of diagnosis is based on knowledge of the normal function of the organ system affected by disease, and direct measures of pathophysiological process play an increasingly important role in clinical practice. New understanding of normal brain function has opened the way for psychiatry to begin to follow this path and develop a brain-based, rather than symptom-based, nosology. Different models of normal brain function, however, suggest quite different theoretical and research approaches. One way in which these models differ is the degree to which they link specific functions (and illnesses) to specific anatomic locations. In a previous paper I discussed the value of adopting a model of brain function that includes important roles for the processes that integrate multiple brain regions into ever-changing functional constellations. In this paper I have proposed the existence of a disease characterized by abnormality in the general physiological mechanism of task-specific regional brain activation. This is not an etiologic diagnosis and therefore presents nothing more than a symptom-based diagnosis about the cause of the disorder. However, I predict that this diagnosis will define a group of patients that is more homogeneous with respect to both clinical course and etiology than do the current symptom-based diagnoses to which these patients would otherwise be assigned. Furthermore, it may be possible to develop new treatments to address this physiological dysfunction.