We meet regularly to host external speakers, discuss progress of current lab projects and rehearse presentations by lab members in London and elsewhere. Past and upcoming external talks include:
The use of Magnetoelectric Nanoparticles for the study of psychiatric and neurodegenerative diseases
Dr Marta Pardo(University of Miami)
Some thoughts on visual encoding and decoding
Professor Ning Qian (Columbia University)
External stimuli evoke sensory responses in the brain (a process termed encoding), and these responses lead to the subjective perception of the stimuli (decoding). For encoding, a key question is: what do sensory responses represent? Many theories assume that a neuron’s higher firing rate indicates a greater probability of its preferred stimulus in the input. However, this contradicts 1) the adaptation phenomena where prolonged exposure to, and thus increased probability of, a stimulus reduces the firing rates of cells tuned to the stimulus; and 2) the observation that rare, unexpected stimuli capture attention and increase neuronal firing. We propose, based on the Minimum Description Length (MDL) principle, that neurons’ firing rates are proportional to optimal code length, and their spike patterns are the actual code, for useful features in inputs. This hypothesis explains adaptation-induced changes of V1 orientation tuning curves. For decoding, most theories assume that it follows the same low-to-high-level hierarchy established for encoding. However, we show that this assumption contradicts the main results of a simple psychophysics experiment. To explain the data, we propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.
Brain dynamics of auditory pattern recognition
Dr Leonardo Bonetti (Oxford)
Pulse Rate Variability: Assessing cardiovascular and autonomic health from photoplethysmography
Elisa Mejia-Mejia (City, University of London)
Galvanic Stimulation: a New Tool for Driving Populations of Neurons
Cynthia Steinhardt (Johns Hopkins)
Compositional problem solving: Investigating a task general strategy to problem solving in brain data and network models
Jascha Achterberg (Cambridge)
It is widely argued that the power of human cognition rests heavily on compositional problem solving: the ability to break a complex problem down into its simple subcomponents, solving those in separate attentional episodes to then reintegrate the results into the overall solution to the complex problem. Results from patient studies and neuroimaging point us to the conclusion that solving task compositionality is a key component in human general intelligence and is located in the brain’s Multiple Demand (MD) Network. As recent modelling work made progress towards formalising compositionality and creating model based agents with an ability to recognise a task’s compositional structure, there is an exciting perspective to investigate and model the neuronal basis behind compositional problem solving.
This talk will review the basic theory behind compositional problem solving and its link to the MD Network before discussing new modelling approaches and experimental ideas to further understand the brain’s ability to solve task compositionality.
Predictive coding as a consequence of energy efficiency in recurrent neural networks
Nasir Ahmad (Donders)
Dysconnection and the immunological basis of schizophrenia
Anjali Bhat (UCL)
Schizophrenia has been cast, from different neuroscientific perspectives, as a sensory processing disorder, a highly heritable genetic disorder as well as a neurodevelopmental disorder. The dysconnection hypothesis attempts to draw together several of these disparate strands of research to create a coherent picture of how schizophrenia arises, implicating neuromodulatory processes governing synaptic gain control as the aetiological core of psychotic symptoms – i.e., a functional (or perhaps Bayesian) synaptopathy. It calls for studies that empirically ‘close the explanatory gap between pathophysiology at the molecular (synaptic) level and the psychopathology experienced by patients’ (Friston et al, 2016). One strand that has not yet been woven into this tapestry is immunology, which has been overwhelmingly linked with schizophrenia in recent years – the reasons for which are not well understood. In this talk I will overview my recent work, which has used a variety of methods with the aim of ‘closing the explanatory gap’. I begin with an exploration of the genetics of mismatch negativity, a key biomarker for schizophrenia. Next, I present experiments of prenatal immunity in neuronal networks grown out of hair samples from patients with schizophrenia and healthy controls. Finally, I introduce inference from the perspective of the immune system – immunoceptive inference – as a new way of understanding interactions between the immune system and the brain.
We propose a neurally plausible computational model of WM called Memory for Latent Representations (MLR) that represents visual knowledge using a modified Variational Autoencoder and then builds memories out of the latent representations in the model. MLR encodes the visual information by flexibly allocating shared neural resources and retrieves them through pixel-wise reconstructions. Consistent with human behavior, the model shows how familiar items can be encoded more efficiently than unfamiliar items. MLR also captures the behavioral capabilities of humans in WM tasks. These capabilities are 1) representing specific attributes of an item (e.g. shape, color, etc.) with varying degrees of precision according to the task demand (Swan, Collins & Wyble, 2016). 2) representing both categorical and visual attributes of an item. 3) representing novel shape configurations that someone has not been trained on. 4) The ability to rapidly tune encoding parameters to provide flexibility of encoding in an uncertain task.
Vineet Tiruvadi ( Emory)
Deep brain stimulation (DBS) has shown promise as a therapy for psychiatric depression. DBS in the subcallosal cingulate cortex (SCC) is the most well studied target but has demonstrated inconsistent results between open-label and clinical studies. Objective signatures of disease and therapy are needed to more systematically tune and improve SCC-DBS. These signatures could then be used to study (a) the neural dynamics underlying the disease and (b) the influence effected by DBS on those dynamics.
Recent refinements of therapy have narrowed the therapeutic target to specific white matter tracts in the SCC (SCCwm), leading to improvements in treatment response when targeted with per-patient tractography. Additionally, advances in DBS hardware and machine-learning analyses enable chronic intracranial recordings and meaningful inference with sparse, noisy data. Together, these advances enable unprecedented study of antidepressant DBS directly in patients over months of recovery.
Today, I'll be presenting my dissertation work in identifying neural oscillations in the SCC predictive of depression state and in characterizing the direct effects of SCCwm-DBS on these oscillations. Using a prototype DBS device capable of simultaneous stimulation and recording (Activa PC+S; Medtronic PLC) our group collected (a) chronic SCC-LFP multiple times a day over seven months, and (b) combined SCC-LFP and dense-array EEG at therapy onset under various DBS parameters in a set of six TRD patients treated with SCCwm-DBS. First, we characterized and corrected for mismatch compression in the differentially recorded SCC-LFP. We then developed a linear decoding model of depression state from SCC-LFP oscillations and identified a candidate readout that achieved significant correlation with empirical depression measures and significant classifier performance. Finally, we show that SCCwm-DBS evokes specific changes across primarily EEG recordings in oscillatory patterns consistent with chronic SCC-LFP changes.
The results of the work enable reliable measurements of oscillations over chronic timeperiods, including compound oscillations in the SCC predictive of depression state that can be used to inform DBS parameter management. These oscillations are not directly modulated by SCCwm-DBS, suggesting antidepressant DBS in the SCC requires precise targeting of patient-specific SCCwm through individualized tractography. Future work will focus on the development of control-theoretic models for systematic engineering of adaptive antidepressant DBS and the reverse-engineering of neural dynamics underlying emotion.
Dmitri Laptev ( UCL)
Michael Clayton ( University of Oxford)
The present talk will discuss two recent applications of graphical models. First, it will focus on modelling social networks through simple graphs to investigate how people deal with statistical dependencies while integrating their direct observations with the communicated beliefs of their social environment (see e.g., Whalen, Griffiths, & Buchsbaum, 2018; Madsen, Bailey, & Pilditch, 2018, for related work). Here, we will discuss results of ongoing work exploring to what extent people can be characterised as Bayesian reasoners when confronted with statistical dependencies in their social environment. Second, the talk will explore recent applications of graphical modelling to brain networks in rodents to investigate effective connectivity between regions of interest (see e.g., Zeidman et al., 2019, for a tutorial). Preliminary results from a collaborative project between City and MIT will be presented where we will explore the potential benefits of using graphical modelling and Bayesian inference for understanding changes in resting state activity in the somatosensory system of rats.
Eva Feredoes (Reading)
Current models of attention and working memory suggest many shared cognitive processes and neural mechanisms, and I will present causal evidence using TMS and concurrent TMS-fMRI that contributes to this view. Specifically, across a series of behavioural TMS studies, we have shown that visual working memory items are in a flexible state determined by the allocation of attention, and which requires the involvement of visual brain areas. I will also present results from several concurrent TMS-fMRI studies suggesting that enhancement of neural representations is a general mechanism by which attention might protect relevant information in the face of competing irrelevant information. This work contributes to evidence showing that short-term information representation and retention is neurally more complex and dynamic than previously thought.
Chris G. Antonopoulos (Essex)
In this talk, I will present a review of my recent work on the study of the brain, aiming to reveal relations between neural synchronisation patterns and information flow capacity, namely the largest amount of information per time unit that can be transmitted between the different parts of the brain networks considered. I will start with the working hypothesis, that brains might evolve based on the principle of the maximisation of their internal information flow
capacity. In this regard, we have found that synchronous behaviour and information flow capacity of the evolved networks reproduce well the same behaviours observed in the brain dynamical networks of the Caenorhabditis elegans (C. elegans) soil worm and humans. Then, I will talk about the verification of our hypothesis by showing that Hindmarsh-Rose (HR) neural networks evolved with coupling strengths that maximise the information flow capacity are those with the closest graph distance to the brain networks of C. elegans and humans. Then, I will present results from a recently published paper on spectacular neural synchronisation phenomenon observed in modular neural networks such as in the C. elegans brain network, called chimera-like states. I will show that, under some assumptions, neurons of different communities of the brain network of the C. elegans soil worm equipped with HR dynamics are able to synchronise with themselves whereas others, belonging to other communities, remain essentially desynchronised, a situation that alternates dynamically in time. Finally, I will
discuss results on the dynamic range in the C. elegans brain network that corroborate the above findings from our earlier studies.
A computational study of Major Depressive Disorder biomarkers
Anna Anissimova (City-University of London)
Electroencephalogram recordings have been used in multiple studies of psychiatric disorders. In this study, a set of biomarkers extracted from EEG data from patient is shown to be able to distinguish between patients and healthy controls. We used dynamic causal modelling (DCM) to obtain a set of interpretable features for a supervised learning algorithm. We analysed the EEG signal recorded from 15 depressed and 35 healthy participants during multi-source interference task. The best interpretable results (using linear SVM) were achieved after feature selection and dataset balancing steps. Testing the final model on unseen data demonstrated a balanced accuracy of approximately 64%, recall of 50% and specificity of 78%. These findings suggest that using DCM, it is possible to build a reliable and interpretable classifier and distinguish between MDD patients and healthy controls. This can have important applications in clinical practice.
Neural couplings and the time-on-task effect
Katharina Wagner (University of Gent)
Frontal beta band power has been shown to increase both as a consequence of elevated task demands and with time on task. Beta band power may thus be a neural correlate of cognitive effort. The present study reports effective connections between the prefrontal and premotor cortex, areas known to be involved in cognitive control, and shows that effective connections change in line with frontal beta band power. We applied Dynamic causal modeling (DCM) to electrocorticographic recordings of two monkeys that are performing a cognitive task. Among model architectures with varying presence and direction of effective connections in each hemisphere, we found a fully connected model in the left hemisphere and a model containing a forward connection from the prefrontal to the premotor region and a backward connection in opposite direction. Using beta band power of each electrode as a predictor for connectivity strength within the parametrical empirical Bayes (PEB) framework, we found that in both hemispheres the strength of the forward connections from the superficial pyramidal cells of the premotor to the spiny stellate cells of the prefrontal area changed in line with prefrontal beta power both when task demands increased and as the session progressed. The consistent change between task and time and between hemispheres was also seen in the self-connection of the prefrontal deep pyramidal cells and the intrinsic connection of premotor spiny stellate to superficial cells. Thus, the increase in cognitive effort may be related to changes in the feedforward connections and other intrinsic connections.
April 15, 2019:
Representations of touch in the somatosensory cortices
Luigi Tamè (Kent)
Detecting and discriminating sensory stimuli are fundamental functions of the nervous system. Electrophysiological and lesion studies suggest that macaque primary somatosensory cortex (SI) is critically involved in discriminating between stimuli, but is not required simply for detecting stimuli. By contrast, transcranial magnetic stimulation (TMS) studies in humans have shown near-complete disruption of somatosensory detection when a single pulse of TMS is delivered over SI. In my presentation, in accordance with macaque studies, I will provide empirical evidence suggesting that human SI is required for discriminating between tactile stimuli and for maintaining stimulus representations over time, or under high task demand, but may not be required for simple tactile detection. Moreover, I will provide empirical evidence showing that human SI, rather than higher level brain areas, is critically involved in the estimation of tactile distance perception as well as bilateral integration of touch.