Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption

Vision from a brief glimpse: the cognitive role of the lowest level of visual cortical activity

In order to generate the rich experience that is visual perception, the brain must accomplish the impressive feat of transforming a continuous stream of light that arrives at the two-dimensional retinal surface into a coherent three-dimensional representation of the objects, colours and scenes that surround us. Although this appears to us to happen quite automatically and effortlessly, a highly evolved and complex system of neural processes are involved in generating it. We are also not simple passive receivers of visual information but rather actively combine visual input with our past experience to create our perception. This interplay between visual input and past experience can provide a great deal of flexibility to calibrate our perceptual processing in line with our surroundings. In some cases, this can have important implications for our survival as when we mistake a tree root for a dangerous snake on a jungle path whereas we wouldn’t give a second’s notice to the garden hose in our back yard (assuming we are fortunate enough to live in a place where garden snakes are uncommon!). Other times, this flexibility can be used for purely leisurely purposes as when we stare at the sky and pick out images of dogs and elephants in the clouds. Yet while there is clearly a great deal of flexibility in our perceptual apparatus, unfettered flexibility would likely not be very adaptive; while we were caught up in our mind’s eye with whatever fantasy we might wish to behold, we might not notice that we were about to become somebody’s dinner. Therefore, the balance between flexibility and rigidity in our visual system is likely to lie at a point that allows for the generality to recognize objects from many different vantage points and in many different environments without permitting us to get carried away with our imaginations. One proposition has been that there is a certain extent of visual processing that proceeds rigidly, without being amenable to top-down influences, and that lays the foundations and sets the limits for our perception. Anatomically, the visual system is divided up into a large number of distinct processing areas and so one candidate area that could provide such veridical processing of information is area V1, the entry point of visual information to the cortical processing suite. However, investigations of the impact of one avenue of top-down influence (spatially directed attention) have yielded mixed results. Results from animal neurophysiology have demonstrated that V1 responses can be modulated by spatial attention under some circumstances but non-invasive electroencephalography (EEG) in humans has most often failed to detect modulation of V1 responses by spatial attention. By contrast, this thesis will argue that V1 responses in humans are amenable to modulation by spatial attention but that these modulations are nuanced and in order to detect them, careful consideration needs to be given to the choice of task paradigm to account for both V1 response properties and the flexibility of visual attention. It will further argue that V1 responses can directly drive visual perceptions that make use of the visual features that V1 extracts. Finally, EEG measurements are coarse and while there is widespread belief that a particular signal, the C1, reflects activity originating in V1, there have also been challenges to this claim. Thus, this thesis will also provide new evidence in support of the claim that V1 activity is reflected in the C1. Taken together, these findings contend that V1 does not simply extract basic visual features to be passed on to cognitive processes further downstream. Rather, V1 is integrally involved in processes of visual cognition, facilitating goal-driven attentional processes and even directly driving perceptual decisions. This challenges the notion that attentional mechanisms are restrained from altering the earliest stages of visual processing

Predicting explorative motor learning using decision-making and motor noise

<div><p>A fundamental problem faced by humans is learning to select motor actions based on noisy sensory information and incomplete knowledge of the world. Recently, a number of authors have asked whether this type of motor learning problem might be very similar to a range of higher-level decision-making problems. If so, participant behaviour on a high-level decision-making task could be predictive of their performance during a motor learning task. To investigate this question, we studied performance during an explorative motor learning task and a decision-making task which had a similar underlying structure with the exception that it was not subject to motor (execution) noise. We also collected an independent measurement of each participant’s level of motor noise. Our analysis showed that explorative motor learning and decision-making could be modelled as the (approximately) optimal solution to a Partially Observable Markov Decision Process bounded by noisy neural information processing. The model was able to predict participant performance in motor learning by using parameters estimated from the decision-making task and the separate motor noise measurement. This suggests that explorative motor learning can be formalised as a sequential decision-making process that is adjusted for motor noise, and raises interesting questions regarding the neural origin of explorative motor learning.</p></div

Experiment 2: Decision-making with motor noise.

<p><b>(A)</b> Learning curves for the DM+noise (red) and MO (green) tasks for each participant. The <i>R</i>² between the DM+noise and MO task is provided. <b>(B)</b> Average learning curves across 6 participants. <b>(C)</b> Error in <i>α</i> and <i>β</i> (y-axis) plotted against the number of attempts (x-axis) in the DM+noise and the MO task. Error bars in all panels represent 95%CI across participants.</p

Behavioural learning performance for the decision-making and the reaching task.

<p><b>(A)</b> Representative participant data showing how a reaching trajectory is gradually updated to match the hidden target trajectory (red). The colours of the lines indicate the sequence of attempts (ranging from green to blue), with later attempts being closer to the target trajectory. <b>(B)</b> Learning curves (positive and negative feedback conditions) for the participants in the decision-making task (DM) and the motor learning/reaching task (MO). Points achieved (y-axis) are plotted against the number of attempts (1-25). The dark red-triangle line and dark green-circle line represent the negative conditions in the DM and MO task respectively, while the light red-triangle line and light green-circle line represent the positive conditions. Error bars indicate 95% confidence intervals (CI) across 24 participants. <b>(C-D)</b> Two representative participants in terms of their curvature exploration. The curvature parameter (x-axis) ranges from −1 to 1, where −1 = ‘curve to the left’, 1 = ‘curve to the right’, and 0 = ‘straight movement’. The participant in (C) evenly explored the curvature dimension, while the participant in (D) concentrated on straight movements with little curvature. <b>(E-F)</b> Two representative participants in terms of their error reduction in both the direction (open circle) and curvature (solid circle) dimensions plotted against the number of attempts. For the participant in (E), the error in both dimensions was reduced to a relatively low level, while for the participant in (F) the error in curvature remained high. The latter was due to the lack of exploration in the curvature dimension as shown in panel (D). <b>(G)</b> Each participant’s mean curvature across all movements during the reaching task (blue circles; the absolute values were used for the movements with negative curvature). Four participants (10,16,18,22) were identified as outliers (red crosses). The blue circles (mean curvature values) were counted as outliers if they were larger than <i>q</i>3 + 0.15(<i>q</i>3 − <i>q</i>1) or smaller than <i>q</i>1 − 0.15(<i>q</i>3 − <i>q</i>1), where <i>q</i>1 and <i>q</i>3 were the 25<i>^th</i> and 75<i>^th</i> percentiles respectively.</p

An illustration of the model for the decision-making task and the explorative motor learning task.

<p>On each trial, a hidden target is chosen (Environment). That is, the environment is in a state, which is not directly observable. The model starts with an initial uniformly distributed belief state (illustrated with the red arrow on the top right). On each time step, given an belief, the model then chooses an action based on the belief-action value function (Action selection). Subsequently, the action is executed (Execution). Decision-making task actions are performed without motor noise; the model is able to choose the selected action accurately. Reaching actions are performed with motor noise; there is uncertainty between the selected and executed action. Once the action is executed, the environment gives observable feedback (<i>o</i>_<i>t</i>−1 = 35 in the figure). The action and observation are then used to update the belief (Bayesian belief update). The update is constrained by the fact that participants were naïve to the score function used. We modelled this uncertainty using the likelihood uncertainty parameter (Γ; <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005503#pcbi.1005503.e016" target="_blank">Eq 3</a>). A new cycle then starts with the new belief state (Bt).</p