Motor imagery-induced EEG patterns in individuals with spinal cord injury and their impact on brain–computer interface accuracy

Objective. Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no ‘cure’ at the present time. Brain–computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. Approach. Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. Main results. It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). Significance. The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals

Motor imagery induced EEG patterns in spinal cord injury patients and their impact on Brain-Computer Interface accuracy

OBJECTIVE: Assimilating the diagnosis complete spinal cord injury (SCI) takes time and is not easy, as patients know that there is no 'cure' at the present time. Brain-computer interfaces (BCIs) can facilitate daily living. However, inter-subject variability demands measurements with potential user groups and an understanding of how they differ to healthy users BCIs are more commonly tested with. Thus, a three-class motor imagery (MI) screening (left hand, right hand, feet) was performed with a group of 10 able-bodied and 16 complete spinal-cord-injured people (paraplegics, tetraplegics) with the objective of determining what differences were present between the user groups and how they would impact upon the ability of these user groups to interact with a BCI. APPROACH: Electrophysiological differences between patient groups and healthy users are measured in terms of sensorimotor rhythm deflections from baseline during MI, electroencephalogram microstate scalp maps and strengths of inter-channel phase synchronization. Additionally, using a common spatial pattern algorithm and a linear discriminant analysis classifier, the classification accuracy was calculated and compared between groups. MAIN RESULTS: It is seen that both patient groups (tetraplegic and paraplegic) have some significant differences in event-related desynchronization strengths, exhibit significant increases in synchronization and reach significantly lower accuracies (mean (M) = 66.1%) than the group of healthy subjects (M = 85.1%). SIGNIFICANCE: The results demonstrate significant differences in electrophysiological correlates of motor control between healthy individuals and those individuals who stand to benefit most from BCI technology (individuals with SCI). They highlight the difficulty in directly translating results from healthy subjects to participants with SCI and the challenges that, therefore, arise in providing BCIs to such individuals.

Intolerance of uncertainty predicts fear extinction in amygdala-ventromedial prefrontal cortical circuitry

Background: Coordination of activity between the amygdala and ventromedial prefrontal cortex (vmPFC) is important for fear-extinction learning. Aberrant recruitment of this circuitry is associated with anxiety disorders. Here, we sought to determine if individual differences in future threat uncertainty sensitivity, a potential risk factor for anxiety disorders, underly compromised recruitment of fear extinction circuitry. Twenty-two healthy subjects completed a cued fear conditioning task with acquisition and extinction phases. During the task, pupil dilation, skin conductance response, and functional magnetic resonance imaging were acquired. We assessed the temporality of fear extinction learning by splitting the extinction phase into early and late extinction. Threat uncertainty sensitivity was measured using self-reported intolerance of uncertainty (IU). Results: During early extinction learning, we found low IU scores to be associated with larger skin conductance responses and right amygdala activity to learned threat vs. safety cues, whereas high IU scores were associated with no skin conductance discrimination and greater activity within the right amygdala to previously learned safety cues. In late extinction learning, low IU scores were associated with successful inhibition of previously learned threat, reflected in comparable skin conductance response and right amgydala activity to learned threat vs. safety cues, whilst high IU scores were associated with continued fear expression to learned threat, indexed by larger skin conductance and amygdala activity to threat vs. safety cues. In addition, high IU scores were associated with greater vmPFC activity to threat vs. safety cues in late extinction. Similar patterns of IU and extinction learning were found for pupil dilation. The results were specific for IU and did not generalize to self-reported trait anxiety. Conclusions: Overall, the neural and psychophysiological patterns observed here suggest high IU individuals to disproportionately generalize threat during times of uncertainty, which subsequently compromises fear extinction learning. More broadly, these findings highlight the potential of intolerance of uncertainty-based mechanisms to help understand pathological fear in anxiety disorders and inform potential treatment targets.

Connectivity-based parcellation of the human frontal polar cortex

The frontal pole corresponds to Brodmann area (BA) 10, the largest single architectonic area in the human frontal lobe. Generally, BA10 is thought to contain two or three subregions that subserve broad functions such as multitasking, social cognition, attention, and episodic memory. However, there is a substantial debate about the functional and structural heterogeneity of this large frontal region. Previous connectivity-based parcellation studies have identified two or three subregions in the human frontal pole. Here, we used diffusion tensor imaging to assess structural connectivity of BA10 in 35 healthy subjects and delineated subregions based on this connectivity. This allowed us to determine the correspondence of structurally based subregions with the scheme previously defined functionally. Three subregions could be defined in each subject. However, these three subregions were not spatially consistent between subjects. Therefore, we accepted a solution with two subregions that encompassed the lateral and medial frontal pole. We then examined resting-state functional connectivity of the two subregions and found significant differences between their connectivities. The medial cluster was connected to nodes of the default-mode network, which is implicated in internally focused, self-related thought, and social cognition. The lateral cluster was connected to nodes of the executive control network, associated with directed attention and working memory. These findings support the concept that there are two major anatomical subregions of the frontal pole related to differences in functional connectivity.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Interactions between cocoa flavanols and inorganic nitrate: additive effects on endothelial function at achievable dietary amounts

Dietary intervention studies have shown that flavanols and inorganic nitrate can improve vascular function, suggesting that these two bioactives may be responsible for beneficial health effects of diets rich in fruits and vegetables. We aimed to study interactions between cocoa flavanols (CF) and nitrate, focusing on absorption, bioavailability, excretion, and efficacy to increase endothelial function. In a double-blind randomized, dose-response crossover study, flow-mediated dilation (FMD) was measured in 15 healthy subjects before and at 1, 2, 3, and 4 h after consumption of CF (1.4-10.9 mg/kg bw) or nitrate (0.1-10 mg/kg bw). To study flavanol-nitrate interactions, an additional intervention trial was performed with nitrate and CF taken in sequence at low and high amounts. FMD was measured before (0 h) and at 1h after ingestion of nitrate (3 or 8.5 mg/kg bw) or water. Then subjects received a CF drink (2.7 or 10.9 mg/kg bw) or a micro- and macronutrient-matched CF-free drink. FMD was measured at 1, 2, and 4 h thereafter. Blood and urine samples were collected and assessed for CF and nitric oxide (NO) metabolites with HPLC and gas-phase reductive chemiluminescence. Finally, intragastric formation of NO after CF and nitrate consumption was investigated. Both CF and nitrate induced similar intake-dependent increases in FMD. Maximal values were achieved at 1 h postingestion and gradually decreased to reach baseline values at 4 h. These effects were additive at low intake levels, whereas CF did not further increase FMD after high nitrate intake. Nitrate did not affect flavanol absorption, bioavailability, or excretion, but CF enhanced nitrate-related gastric NO formation and attenuated the increase in plasma nitrite after nitrate intake. Both flavanols and inorganic nitrate can improve endothelial function in healthy subjects at intake amounts that are achievable with a normal diet. Even low dietary intake of these bioactives may exert relevant effects on endothelial function when ingested together.

The effects of acute fluoxetine administration on temporal discounting in youth with ADHD

Background Serotonin is under-researched in attention deficit hyperactivity disorder (ADHD), despite accumulating evidence for its involvement in impulsiveness and the disorder. Serotonin further modulates temporal discounting (TD), which is typically abnormal in ADHD relative to healthy subjects, underpinned by reduced fronto-striato-limbic activation. This study tested whether a single acute dose of the selective serotonin reuptake inhibitor (SSRI) fluoxetine up-regulates and normalizes reduced fronto-striato-limbic neurofunctional activation in ADHD during TD. Method Twelve boys with ADHD were scanned twice in a placebo-controlled randomized design under either fluoxetine (between 8 and 15 mg, titrated to weight) or placebo while performing an individually adjusted functional magnetic resonance imaging TD task. Twenty healthy controls were scanned once. Brain activation was compared in patients under either drug condition and compared to controls to test for normalization effects. Results Repeated-measures whole-brain analysis in patients revealed significant up-regulation with fluoxetine in a large cluster comprising right inferior frontal cortex, insula, premotor cortex and basal ganglia, which further correlated trend-wise with TD performance, which was impaired relative to controls under placebo, but normalized under fluoxetine. Fluoxetine further down-regulated default mode areas of posterior cingulate and precuneus. Comparisons between controls and patients under either drug condition revealed normalization with fluoxetine in right premotor-insular-parietal activation, which was reduced in patients under placebo. Conclusions The findings show that a serotonin agonist up-regulates activation in typical ADHD dysfunctional areas in right inferior frontal cortex, insula and striatum as well as down-regulating default mode network regions in the context of impulsivity and TD.

Corticomuscular coherence modulation with the pattern of finger force coordination

We assess the corticomuscular coherence (CMC) of the contralateral primary motor cortex and the hand muscles during a finger force-tracking task and explore whether the pattern of finger coordination has an impact on the CMC level. Six healthy subjects (three men and three women) were recruited to conduct the force-tracking tasks comprising two finger patterns, i.e., natural combination of index and middle fingers and unnatural combination of index and middle fingers (i.e., simultaneously producing equal force strength in index and middle finger). During the conducting of the tasks with right index and middle finger, MEG and sEMG signals were recorded from left primary motor cortex (M1) and right flexor digitorum superficialis (FDS), respectively; the contralateral CMC was calculated to assess the neuromuscular interaction. Finger force-tracking tasks of Common-IM only induce beta-band CMC, whereas Uncommon-IM tasks produce CMC in both beta and low-gamma band. Compared to the force-tracking tasks of Common-IM, the Uncommon-IM task is associated with the most intensive contralateral CMC. Our study demonstrated that the pattern of finger coordination had significant impact on the CMC between the contralateral M1 and hand muscles, and more corticomuscular interaction was necessary for unnaturally coordinated finger activities to regulate the fixed neural drive of hand muscles.