Memory Decoding Journal Club: Functional connectomics reveals general wiring rule in mouse visual cortex

Functional connectomics reveals general wiring rule in mouse visual cortex

Memory Decoding Journal Club: A combinatorial neural code for long-term motor memory

A combinatorial neural code for long-term motor memory

Memory Decoding Journal Club: Systems consolidation reorganizes hippocampal engram circuitry

Systems consolidation reorganizes hippocampal engram circuitry

Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Join Us for the Memory Decoding Journal Club! A collaboration of the Carboncopies Foundation and BPF Aspirational Neuroscience. This time, we’re diving into a groundbreaking paper: "Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

Development of a small molecule to promote neuroprotection and repair in progressive multiple sclerosis

Consciousness in the cradle: on the emergence of infant experience

Although each of us was once a baby, infant consciousness remains mysterious and there is no received view about when, and in what form, consciousness first emerges. Some theorists defend a ‘late-onset’ view, suggesting that consciousness requires cognitive capacities which are unlikely to be in place before the child’s first birthday at the very earliest. Other theorists defend an ‘early-onset’ account, suggesting that consciousness is likely to be in place at birth (or shortly after) and may even arise during the third trimester. Progress in this field has been difficult, not just because of the challenges associated with procuring the relevant behavioral and neural data, but also because of uncertainty about how best to study consciousness in the absence of the capacity for verbal report or intentional behavior. This review examines both the empirical and methodological progress in this field, arguing that recent research points in favor of early-onset accounts of the emergence of consciousness.

Integrating theory-guided and data-driven approaches for measuring consciousness

Clinical assessment of consciousness is a significant issue, with recent research suggesting some brain-damaged patients who are assessed as unconscious are in fact conscious. Misdiagnosis of consciousness can also be detrimental when it comes to general anaesthesia, causing numerous psychological problems, including post-traumatic stress disorder. Avoiding awareness with overdose of anaesthetics, however, can also lead to cognitive impairment. Currently available objective assessment of consciousness is limited in accuracy or requires expensive equipment with major barriers to translation. In this talk, we will outline our recent theory-guided and data-driven approaches to develop new, optimized consciousness measures that will be robustly evaluated on an unprecedented breadth of high-quality neural data, recorded from the fly model system. We will overcome the subjective-choice problem in data-driven and theory-guided approaches with a comprehensive data analytic framework, which has never been applied to consciousness detection, integrating previously disconnected streams of research in consciousness detection to accelerate the translation of objective consciousness measures into clinical settings.

Linking GWAS to pharmacological treatments for psychiatric disorders

Genome-wide association studies (GWAS) have identified multiple disease-associated genetic variations across different psychiatric disorders raising the question of how these genetic variants relate to the corresponding pharmacological treatments. In this talk, I will outline our work investigating whether functional information from a range of open bioinformatics datasets such as protein interaction network (PPI), brain eQTL, and gene expression pattern across the brain can uncover the relationship between GWAS-identified genetic variation and the genes targeted by current drugs for psychiatric disorders. Focusing on four psychiatric disorders---ADHD, bipolar disorder, schizophrenia, and major depressive disorder---we assess relationships between the gene targets of drug treatments and GWAS hits and show that while incorporating information derived from functional bioinformatics data, such as the PPI network and spatial gene expression, can reveal links for bipolar disorder, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeds null expectations. This relatively low degree of correspondence across modalities suggests that the genetic mechanisms driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms used for targeting symptom manifestations through pharmacological treatments and that novel approaches for understanding and treating psychiatric disorders may be required.

Learning with less labels for medical image segmentation

Accurate segmentation of medical images is a key step in developing Computer-Aided Diagnosis (CAD) and automating various clinical tasks such as image-guided interventions. The success of state-of-the-art methods for medical image segmentation is heavily reliant upon the availability of a sizable amount of labelled data. If the required quantity of labelled data for learning cannot be reached, the technology turns out to be fragile. The principle of consensus tells us that as humans, when we are uncertain how to act in a situation, we tend to look to others to determine how to respond. In this webinar, Dr Mehrtash Harandi will show how to model the principle of consensus to learn to segment medical data with limited labelled data. In doing so, we design multiple segmentation models that collaborate with each other to learn from labelled and unlabelled data collectively.

The neuroscience of lifestyle interventions for mental health: the BrainPark approach

Our everyday behaviours, such as physical activity, sleep, diet, meditation, and social connections, have a potent impact on our mental health and the health of our brain. BrainPark is working to harness this power by developing lifestyle-based interventions for mental health and investigating how they do and don’t change the brain, and for whom they are most effective. In this webinar, Dr Rebecca Segrave and Dr Chao Suo will discuss BrainPark’s approach to developing lifestyle-based interventions to help people get better control of compulsive behaviours, and the multi-modality neuroimaging approaches they take to investigating outcomes. The webinar will explore two current BrainPark trials: 1. Conquering Compulsions - investigating the capacity of physical exercise and meditation to alter reward processing and help people get better control of a wide range of unhelpful habits, from drinking to eating to cleaning. 2. The Brain Exercise Addiction Trial (BEAT) - an NHMRC funded investigation into the capacity of physical exercise to reverse the brain harms caused by long-term heavy cannabis use. Dr Rebecca Segrave is Deputy Director and Head of Interventions Research at BrainPark, the David Winston Turner Senior Research Fellow within the Turner Institute for Brain and Mental Health, and an AHRPA registered Clinical Neuropsychologist. Dr Chao Suo is Head of Technology and Neuroimaging at BrainPark and a Research Fellow within the Turner Institute for Brain and Mental Health.

Free will beyond spontaneous volition: Conscious control processes of inhibition and attention in self-control and free will

Polaris Koi (Philosophy) and Jake Gavenas (Neuroscience) begin the seminar by arguing that agentive control is the key requirement for free will, drawing on folk-philosophy findings to support this claim (Gavenas et al., in prep). They explore how two executive control processes that functionally involve consciousness—inhibition and top-down control of attention—connect self-control and free will.

Inferring informational structures in neural recordings of drosophila with epsilon-machines

Measuring the degree of consciousness an organism possesses has remained a longstanding challenge in Neuroscience. In part, this is due to the difficulty of finding the appropriate mathematical tools for describing such a subjective phenomenon. Current methods relate the level of consciousness to the complexity of neural activity, i.e., using the information contained in a stream of recorded signals they can tell whether the subject might be awake, asleep, or anaesthetised. Usually, the signals stemming from a complex system are correlated in time; the behaviour of the future depends on the patterns in the neural activity of the past. However these past-future relationships remain either hidden to, or not taken into account in the current measures of consciousness. These past-future correlations are likely to contain more information and thus can reveal a richer understanding about the behaviour of complex systems like a brain. Our work employs the "epsilon-machines” framework to account for the time correlations in neural recordings. In a nutshell, epsilon-machines reveal how much of the past neural activity is needed in order to accurately predict how the activity in the future will behave, and this is summarised in a single number called "statistical complexity". If a lot of past neural activity is required to predict the future behaviour, then can we say that the brain was more “awake" at the time of recording? Furthermore, if we read the recordings in reverse, does the difference between forward and reverse-time statistical complexity allow us to quantify the level of time asymmetry in the brain? Neuroscience predicts that there should be a degree of time asymmetry in the brain. However, this has never been measured. To test this, we used neural recordings measured from the brains of fruit flies and inferred the epsilon-machines. We found that the nature of the past and future correlations of neural activity in the brain, drastically changes depending on whether the fly was awake or anaesthetised. Not only does our study find that wakeful and anaesthetised fly brains are distinguished by how statistically complex they are, but that the amount of correlations in wakeful fly brains was much more sensitive to whether the neural recordings were read forward vs. backwards in time, compared to anaesthetised brains. In other words, wakeful fly brains were more complex, and time asymmetric than anaesthetised ones.

The Unfolding Argument: theoretical and methodological implications

In the first part of this talk, I will briefly present the unfolding argument by Doerig et al. (2019) and the various replies in the philosophical and neuroscientific literature. In the second part of the talk, I will explore the ramifications that this debate has for the science of consciousness and its philosophy, with particular focus on these questions: (i) which type of explanation should a theory of consciousness provide? (ii) what is the evidential basis for theories of consciousness?

Generative models of brain function: Inference, networks, and mechanisms

This talk will focus on the generative modelling of resting state time series or endogenous neuronal activity. I will survey developments in modelling distributed neuronal fluctuations – spectral dynamic causal modelling (DCM) for functional MRI – and how this modelling rests upon functional connectivity. The dynamics of brain connectivity has recently attracted a lot of attention among brain mappers. I will also show a novel method to identify dynamic effective connectivity using spectral DCM. Further, I will summarise the development of the next generation of DCMs towards large-scale, whole-brain schemes which are computationally inexpensive, to the other extreme of the development using more sophisticated and biophysically detailed generative models based on the canonical microcircuits.

How to Construct A Consciousness Meter

One of the central challenges facing the science of consciousness is that of identifying ways of measuring consciousness. Can we go beyond our pre-theoretical ways of detecting consciousness and develop independently validated measures of consciousness. Some theorists think not, arguing that we cannot go beyond the everyday markers of consciousness with which we begin. Others are more optimistic, and hold that we will be able to develop independent measures of consciousness, or "consciousness meters". This talk discusses various ways in which one might attempt to validate a consciousness meter, arguing that the most promising approach involves treating consciousness as a natural kind.

Using marmosets for the study of the visual cortex: unique opportunities, and some pitfalls

Marmosets (Callithrix jacchus) are small South American monkeys which are being increasingly becoming adopted as animal models in neuroscience. Knowledge about the marmoset visual system has developed rapidly over the last decade. But what are the comparative advantages, and disadvantages involved in adopting this emerging model, as opposed to the more traditionally used macaque monkey? In this talk I will present case studies where the simpler brain morphology and short developmental cycle of the marmoset have been key factors in facilitating discoveries about the anatomy and physiology of the visual system. Although no single species provides the “ideal” animal model for invasive studies of the neural bases of visual processing, I argue that the development of robust methodologies for the study of the marmoset brain provides exciting opportunities to address long-standing problems in neuroscience.

Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example

This webinar is mainly focused on “Biomarkers for Addiction Treatment Development: fMRI Drug Cue Reactivity as an Example”. Biomarkers and Biotypes of Drug Addiction: funding opportunities at NIDA, Tanya Ramey (NIDA, US) Neuroimaging-based Biomarker Development for Clinical Trials, Owen Carmicheal (Pennington Biomedical Research Center, USA) ENIGMA-Addiction Cue Reactivity Initiative (ACRI) and Checklist, Hamed Ekhtiari (Laureate Institute for Brain Research, USA) ENIGMA-ACRI Checklist: Participant Characteristics, General fMRI Information, General Task Information, Cue Information, Task-related Assessments, Pre-Post Scanning Consideration (James Prisciandaro, Medical University of South Carolina, USA; Marc Kaufman, McLean Hospital/Harvard Medical School, USA; Anna Zilverstand, University of Minnesota; Torsten Wüstenberg, Charité Medical University Berlin, Germany; Falk Kiefer, University of Heidelberg, Germany; Amy Janes, Harvard Medical School, USA) How to Add fMRI Drug Cue Reactivity to the ENIGMA Consortium: Road Ahead, Hugh Garavan, University of Vermont)

Delineating Reward/Avoidance Decision Process in the Impulsive-compulsive Spectrum Disorders through a Probabilistic Reversal Learning Task

Impulsivity and compulsivity are behavioural traits that underlie many aspects of decision-making and form the characteristic symptoms of Obsessive Compulsive Disorder (OCD) and Gambling Disorder (GD). The neural underpinnings of aspects of reward and avoidance learning under the expression of these traits and symptoms are only partially understood. " "The present study combined behavioural modelling and neuroimaging technique to examine brain activity associated with critical phases of reward and loss processing in OCD and GD. " "Forty-two healthy controls (HC), forty OCD and twenty-three GD participants were recruited in our study to complete a two-session reinforcement learning (RL) task featuring a “probability switch (PS)” with imaging scanning. Finally, 39 HC (20F/19M, 34 yrs +/- 9.47), 28 OCD (14F/14M, 32.11 yrs ±9.53) and 16 GD (4F/12M, 35.53yrs ± 12.20) were included with both behavioural and imaging data available. The functional imaging was conducted by using 3.0-T SIEMENS MAGNETOM Skyra syngo MR D13C at Monash Biomedical Imaging. Each volume compromised 34 coronal slices of 3 mm thickness with 2000 ms TR and 30 ms TE. A total of 479 volumes were acquired for each participant in each session in an interleaved-ascending manner. " " The standard Q-learning model was fitted to the observed behavioural data and the Bayesian model was used for the parameter estimation. Imaging analysis was conducted using SPM12 (Welcome Department of Imaging Neuroscience, London, United Kingdom) in the Matlab (R2015b) environment. The pre-processing commenced with the slice timing, realignment, normalization to MNI space according to T1-weighted image and smoothing with a 8 mm Gaussian kernel. " " The frontostriatal brain circuit including the putamen and medial orbitofrontal (mOFC) were significantly more active in response to receiving reward and avoiding punishment compared to receiving an aversive outcome and missing reward at 0.001 with FWE correction at cluster level; While the right insula showed greater activation in response to missing rewards and receiving punishment. Compared to healthy participants, GD patients showed significantly lower activation in the left superior frontal and posterior cingulum at 0.001 for the gain omission. " " The reward prediction error (PE) signal was found positively correlated with the activation at several clusters expanding across cortical and subcortical region including the striatum, cingulate, bilateral insula, thalamus and superior frontal at 0.001 with FWE correction at cluster level. The GD patients showed a trend of decreased reward PE response in the right precentral extending to left posterior cingulate compared to controls at 0.05 with FWE correction. " " The aversive PE signal was negatively correlated with brain activity in regions including bilateral thalamus, hippocampus, insula and striatum at 0.001 with FWE correction. Compared with the control group, GD group showed an increased aversive PE activation in the cluster encompassing right thalamus and right hippocampus, and also the right middle frontal extending to the right anterior cingulum at 0.005 with FWE correction. " " Through the reversal learning task, the study provided a further support of the dissociable brain circuits for distinct phases of reward and avoidance learning. Also, the OCD and GD is characterised by aberrant patterns of reward and avoidance processing.