Winter 2020/2021

All Neural Dynamics Forum talks during Winter 2021 will take place online through Zoom, details as below.

​Meeting ID: 932 7630 6396
Password: 815933
https://zoom.us/s/93276306396


1PM March 26th – Steve Brown (University of Zurich)

Cellular and circuit-based mechanisms underlying the daily regulation of sleep

“Mammalian sleep and wake follows a complex daily pattern influenced by both a circadian clock controlling vigilance according to time of day, and a sleep homeostat controlling vigilance according to prior wake history.  In this lecture, we shall consider mechanisms underlying both central and local control of these processes, and how they in turn intimately control global metabolism.”


1PM March 19th – Yaara Erez (University of Cambridge)

Towards personalised neuroimaging in neurosurgery: linking brain structure and function

The importance of quality of life of patients following neurosurgery for brain tumors has been increasingly recognized in recent years. Emphasizing the balance between oncological and functional outcome, an emerging discipline at the forefront of research and patient care focuses on cognitive function. In current surgical standard practice, focal electrical stimulation on the exposed brain while patients are awake is used for mapping areas critical for motor function as well as language to prevent irreversible damage as a result of tissue removal. However, some cognitive functions are harder to map with standard stimulation alone. In the talk, I will present my work aimed at developing techniques and tools for mapping cognitive function in neurosurgery. I will focus on a particularly challenging aspect of cognition – executive functions – how we set and achieve goals, make plans, and prioritize tasks, which are essential to all aspects of our everyday life. Because of the complex nature of these functions and the distributed neural systems that support them, there are currently no established techniques for their functional mapping in neurosurgery. I will introduce a novel method for mapping executive function during awake neurosurgery using electrocorticography (ECOG) – recording directly from the surface of the brain – while patients perform cognitive tasks. I will show evidence for the feasibility and utility of this method as a first step towards establishing its foundations. Critical to bridging the translational gap and bringing neuroimaging into use in neurosurgery is our understanding of the functional role of the neural networks associated with cognitive functions and our ability to identify them in individuals. I will therefore present supporting findings for these using functional MRI (fMRI) data in healthy human volunteers. Finally, I will discuss some open questions related to developing neuroimaging tools for personalised medicine in neurosurgery.


1PM March 12th – Alexandra Constantinescu (UCL)

How do our brains form maps of the world?

Navigating our mental world is thought to be similar to navigating in the real world. In this talk, I will present behavioural and fMRI studies investigating how spatial and non-spatial memories are organized into 2D cognitive maps using grid cell-like codes in the entorhinal and medial prefrontal cortices. First, I will show a paradigm for navigation in an abstract “bird space”. Second, I will present how humans can learn long lists of words using the memory palace technique and a virtual reality task inspired by Harry Potter. And third, I will talk about a new method we’re developing for analysing human grid-like codes in more detail, using a big data approach and 7T submillimeter fMRI. Our findings have implications in understanding the remarkable capacity of humans to generalize experiences to novel situations.


1PM March 5th – Narender Ramnani (Royal Holloway)

Cerebellum and Cognition

The cerebellum is well-known for its contribution to the control of skilled movement. The mechanisms include connectivity with the motor system and the ability of it’s remarkable circuitry to store motor memories, including those relating to simple conditioned motor responses acquired through Pavlovian conditioning. However, some cerebellar circuitry communicates with the prefrontal cortex – including areas of that have important roles in cognitive function but little to do with motor control. In this lecture I draw from theoretical neurobiology, anatomy, brain evolution and neuroimaging to address the ways in which cerebellar circuits might contribute to the skilled execution of cognitive operations, such as the instrumental learning of contingencies that link decisions with their antecedents and consequences.


2PM February 26th – Keith Doelling (Institut Pasteur in Paris)

Temporal prediction of natural rhythms in speech and music

The ability to predict the onset of future events is a critical feature for survival. Knowing in advance when some stimulus might occur improves our ability to detect, process and react to it. The neuroscientific field has broken down temporal prediction into two separate and distinct mechanisms: interval timing, the measurement and prediction of single time intervals, and rhythmic timing, the synchronization with repeated sequential intervals. This talk will probe this formulation by asking how far it can get us when dealing with realistic stimuli. Rarely in the natural world (even in music) are rhythms perfectly isochronous and rarer still are temporal intervals presented in isolation. Here we test the extension, particularly, of rhythmic processing models into more naturalistic settings in two parts. First, I will show in a series of studies that neural responses to naturalistic stimuli like speech and music are well modeled as an oscillator synchronized to quasi-rhythmic input. Second, I will present work comparing such a model with behavioral responses of participants to ambiguous rhythms, suggesting that a neural oscillator may act as a kind of rhythmic prior to improve sensory perception of quasi-rhythmic stimuli. Together, the work will present a clear direction for the study of temporal prediction in more realistic environments. It will highlight computational modeling as well as behavioral research as a critical avenue for the elucidation of neural mechanisms underlying the temporal prediction of music and of the environment at-large.


12 PM February 12th – Oliver J Robinson (UCL)

The translational cognitive neuroscience of anxiety.

Anxiety can be a normal adaptive process, but it can also become a clinical state. At both ends of the spectrum anxiety significantly alters the ways individuals make decisions and behave. However, our understanding of the mechanisms underlying such symptoms is at present limited and does not contribute to treatment development or clinical decision-making. In this talk I will outline our recent work which attempts to better understand anxiety through a combination of computational modelling of behaviour and neuroimaging of adaptive and pathological anxiety.


1PM January 29th – Anil Seth (University of Sussex)

Consciousness, complexity, and hallucination

What happens in the brain during hallucination, and how can the study of hallucination shed light on ‘normal’ conscious perception. I will describe a number of research projects applying neurodynamical analyses (e.g., complexity, Granger causality) and computational models to shed light on the brain basis of hallucinatory perception. These projects include analyses of human neuroimaging data recorded during the psychedelic state, stroboscopic-induced hallucinations, and the use of computational models of predictive perception to model diverse hallucinatory forms. I will contextualise these analyses within the framework of ‘computational neurophenomenology’ – the attempt to account for phenomenological properties of perceptual experience in terms of (models) of their underlying neural mechanisms.


1PM January 22nd – Daniel Bush (UCL)

Theta Oscillations and Phase Coding in the Mammalian Hippocampus

The mammalian hippocampus is implicated in spatial and episodic memory function. In the rodent, hippocampal network dynamics can be characterised by oscillatory activity in the 6-12Hz theta band during active behaviour, and in the 150-250Hz ripple band during quiescent waking and sleep. During these periods, hippocampal place cells encode behavioural trajectories on a compressed timescale as theta sweeps and replay events, respectively. I will present a series of MEG and intracranial EEG experiments showing that human hippocampal theta oscillations also play a role in spatial coding, functional connectivity and memory. Next, I will present theoretical work that describes how oscillatory activity can support the phase coding of information in the central nervous system. Finally, using rodent place cell recordings, I will demonstrate that the temporal code for location within a place field is preserved across different network states. In sum, these results indicate that the mammalian hippocampus consistently uses phase coding in the service of memory encoding and retrieval.


2PM  January 15th – Robb Rutledge (Yale University)

A Computational and Neural Model for Mood Dynamics

The happiness of individuals is an important metric for societies, but we know little about how daily life events are aggregated into subjective feelings. We have shown that happiness depends on the history of rewards and expectations, a result we have now replicated in thousands of individuals using smartphone-based data collection and quantified in relation to major depression (including in our new smartphone app https://thehappinessproject.app). Using fMRI, we show how happiness relates to neural activity in the ventral striatum and ventromedial prefrontal cortex. Computational modelling shows precisely how feelings vary across individuals in relation to a wide variety of factors including expectations, intrinsic reward, social comparison, and reinforcement learning.


 

Autumn 2020

All Neural Dynamics Forum talks during Autumn 2020 will take place online through Zoom, details as below.

​Meeting ID: 932 7630 6396
Password: 815933
https://zoom.us/s/93276306396


1PM December 18th Tobias U. Hauser (UCL)

Do we need a developmental computational psychiatry?

Many psychiatric disorders arise during adolescence, a time when the
brain undergoes fundamental reorganisation. However, it is unclear
whether and how the emergence of mental health problems is linked to
aberrant neurocognitive development. In my talk, I will discuss why it
is critical to understand (aberrant) cognitive and brain development if
we want to better understand how mental health problems arise. I will
present findings showing how psychiatric traits are linked to adolescent
brain myelination, and illustrate why computational neuroscience
approaches could be important in understanding psychiatric disorders.


1PM December 11th Aleks Domanski (University of Bristol)

Investigating population-level multisensory integration for predictive coding in the primary visual cortex
Why can you hear your friend more clearly in a noisy bar by watching their lips move as they speak?
During sensory processing under ambiguous conditions, integration of multisensory information may improve the extraction of input statistics and increase the accuracy of predictions about upcoming information. The computational principles underlying such a boost in predictive coding and their vulnerabilities to disruption, notably in Autistic individuals, remain poorly understood. Alongside primary sensory afferents, the recurrently connected network of primary visual cortex (V1) receives modulatory input from other sensory and frontal brain structures. Indeed, previous work demonstrates that visually tuned neurons in V1 also respond to tones and noise bursts. Recurrently connected ensembles of neurons conveying mixed combinations of audio-visual variables could, as demonstrated in higher-order brain circuits, provide a powerful computational substrate to facilitate the nonlinear classification of ambiguous sensory input. However, this is unexplored in the context of viewing more dynamically complex naturalistic scenes.
Here, I will examine a large population calcium imaging dataset (1000~2000 cells) from mouse V1 to study how past and current multisensory input statistics are integrated by the circuit during ambiguous natural movie viewing to improve the fidelity of predictive coding.

9AM December 4th Jihwan Myung (Taipei Medical University)

Multiple circadian clocks that are not always synchronized
 
Circadian clocks are biological clocks that maintain near 24-h periodicity with high precision. These clocks synchronize and make a robust clock when coupled. An interesting but often ignored feature of these clocks is that they do not always synchronize completely—sometimes by design. A functionally relevant case of close-to-synchronization can be found in the central clock called the suprachiasmatic nucleus (SCN), where its subpopulations deviate from phase-locking as the day-length increases as if the degree of synchrony served as a mechanism of seasonal time encoding. We then discovered that robust circadian clocks exist outside the SCN and they are not phase-locked with the SCN clock. Since the datasets needed to make these observations have high fidelity in time, i.e., the variables are dynamic and not static, experimenters need to understand the theoretical background on oscillator systems when designing experiments and interpreting data. Conversely, theoreticians equally need to appreciate the complexity of the biological system and imperfections in experimental approaches. We discuss some cases of circadian phase coordination and close-to-synchronization behaviors in the molecular, cellular, and tissue levels, and how these can be studied by experimental and theoretical approaches.

9AM November 20th – Woo-Young Ahn (Seoul National University)

Deep digital phenotypes using computational modeling, machine learning, and mobile technology

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning, and adaptive design optimization (ADO) is a promising machine-learning method that might lead to rapid, precise, and reliable markers of individual differences. In this talk, I will present a series of studies that utilized ADO in the area of decision-making and for the development of ADO-based digital phenotypes for addictive behaviors. Lastly, I will introduce an open-source Python package, ADOpy, which we developed to increase the accessibility of ADO to even researchers who have limited background in Bayesian statistics or cognitive modeling.

https://ccs-lab.github.io/team/young-ahn/


1PM November 13th – Naoki Masuda (University at Buffalo)

Recurrence analysis of dynamic functional brain networks of individuals with epilepsy

Functional brain networks have been suggested to vary over time. We propose a new method to characterize dynamics of functional brain networks using the so-called recurrence plots (RPs) and their quantification. RPs and recurrence quantification analysis (RQA) were originally proposed for single nonlinear time series and have been applied to a range of dynamical systems and empirical data including neural signals. Here we propose these methods for dynamic networks, where recurrence is defined at the level of the functional networks, i.e., a network recurs to a past network if the distance between the two networks is sufficiently small. For resting-state magnetoencephalographic dynamic functional networks, we found that functional networks tended to recur more rapidly in people with epilepsy than healthy controls. For stereo electroencephalography (sEEG) data, we found that dynamic functional networks involved in epileptic seizures emerged before seizure onset, and RQA allowed us to detect seizures. The proposed methods can also be used for trying to understand dynamic functional networks in brain function in health and other neurological disorders.

http://www.buffalo.edu/cas/math/people/faculty/naoki-masuda.html


1PM November 6th – Rick Adams (UCL)

Beyond E/I imbalance – clarifying the fundamental circuit dysfunction in schizophrenia using biophysical modelling of multiple imaging paradigms.

Subjects with a diagnosis of schizophrenia show consistent differences from controls in neuroimaging paradigms such as resting state (rsEEG and rsfMRI), mismatch negativity (MMN) and 40 Hz auditory steady state response (ASSR). The underlying circuit changes causing these group differences are unclear, however, and it is not known whether the same abnormalities could underlie group differences in all paradigms. Nevertheless, it is widely hypothesised that schizophrenia involves a loss of synaptic gain – e.g. due to NMDA receptor dysfunction – and disrupted ‘balance’ between excitatory and inhibitory transmission in cortical circuits. Here we analyse a neuroimaging dataset containing data from controls (n=107), subjects diagnosed with schizophrenia (Scz, n=108) and their first degree relatives (n=57) each undergoing rsEEG, rsfMRI, MMN and 40 Hz ASSR paradigms. We use a variety of dynamic causal modelling approaches to estimate synaptic gain and other circuit parameters in auditory and frontal areas. We find some striking commonalities across paradigms, not just in synaptic gain in the Scz group, but also in relationships with symptoms and cognitive function. The potential for development of a model-based biomarker of synaptic gain is discussed.

https://iris.ucl.ac.uk/iris/browse/profile?upi=RAADA06


1PM October 30th – Michael J. Frank (Brown University)

Striatal dopamine computations in learning about agency

The basal ganglia and dopaminergic systems are well studied for their roles in reinforcement learning and reward-based decision making. Much work focuses on “reward prediction error” (RPE) signals conveyed by dopamine and used for learning. Computational considerations suggest that such signals may be enriched beyond the classical global and scalar RPE computation, to support more structured learning in distinct sub-circuits (“vector RPEs”). Such signals allow an agent to assign credit to the level of action selection most likely responsible for the outcomes, and hence to enhance learning depending on the generative task statistics. I will present experimental data from mice showing spatiotemporal dynamics of dopamine terminal activity and release across the dorsal striatum in the form of traveling waves that support learning about agency.

http://ski.cog.brown.edu/