CamBRAIN Virtual Journal Club

COVID-19 lockdown measures have caused severe disruptions to work and education and prevented people from engaging in many rewarding activities. Cannabis users may be especially vulnerable, having been previously shown to have higher levels of apathy and anhedonia than non-users. In this survey study, we measured apathy and anhedonia, before and after lockdown measures were implemented, in n = 256 adult and n = 200 adolescent cannabis users and n = 170 adult and n = 172 adolescent controls. Scores on the Apathy Evaluation Scale (AES) and Snaith-Hamilton Pleasure Scale (SHAPS) were investigated with mixed-measures ANCOVA, with factors user group, age group, and time, controlling for depression, anxiety, and other drug use. Adolescent cannabis users had significantly higher SHAPS scores before lockdown, indicative of greater anhedonia, compared with adolescent controls (P = .03, η p2 = .013). Contrastingly, adult users had significantly lower scores on both the SHAPS (P < .001, η p2 = .030) and AES (P < .001, η p2 = .048) after lockdown compared with adult controls. Scores on both scales increased during lockdown across groups, and this increase was significantly smaller for cannabis users (AES: P = .001, η p2 = .014; SHAPS: P = .01, η p2 = .008). Exploratory analyses revealed that dependent cannabis users had significantly higher scores overall (AES: P < .001, η p2 = .037; SHAPS: P < .001, η p2 = .029) and a larger increase in scores (AES: P = .04, η p2 =.010; SHAPS: P = .04, η p2 = .010), compared with non-dependent users. Our results suggest that adolescents and adults have differential associations between cannabis use as well as apathy and anhedonia. Within users, dependence may be associated with higher levels of apathy and anhedonia regardless of age and a greater increase in levels during the COVID-19 lockdown.

Normative development in adolescence indicates that the prefrontal cortex is still under development thereby unable to exert efficient top-down inhibitory control on subcortical regions such as the basolateral amygdala and the nucleus accumbens. This imbalance in the developmental trajectory between cortical and subcortical regions is implicated in expression of the prototypical impulsive, compulsive, reward seeking and risk-taking adolescent behavior. Here we demonstrate that a chronic mild unpredictable stress procedure during adolescence in male Wistar rats arrests the normal behavioral maturation such that they continue to express adolescent-like impulsive, hyperactive, and compulsive behaviors into late adulthood. This arrest in behavioral maturation is associated with the hypoexcitability of prelimbic cortex (PLC) pyramidal neurons and reduced PLC-mediated synaptic glutamatergic control of BLA and nucleus accumbens core (NAcC) neurons that lasts late into adulthood. At the same time stress exposure in adolescence results in the hyperexcitability of the BLA pyramidal neurons sending stronger glutamatergic projections to the NAcC. Chemogenetic reversal of the PLC hypoexcitability decreased compulsivity and improved the expression of goal-directed behavior in rats exposed to stress during adolescence, suggesting a causal role for PLC hypoexcitability in this stress-induced arrested behavioral development. (https://www.biorxiv.org/content/10.1101/2021.11.21.469381v1.abstract)

People with chronic schizophrenia die on average 10-15 years sooner than the general population, mostly due to physical comorbidity. While sociodemographic, chronic lifestyle and iatrogenic factors are important contributors to this comorbidity, a growing body of research is beginning to suggest that early signs of cardiometabolic dysfunction may be present from the onset of psychosis in some young adults, and may even be detectable before the onset of psychosis. Given that primary prevention is the best means to prevent the onset of more chronic and severe cardiometabolic phenotypes such as CVD, there is clear need to be able to identify young adults with psychosis who are most at risk of future adverse cardiometabolic outcomes, such that the most intensive interventions can be directed in an informed way to attenuate the risk or even prevent those adverse outcomes from occurring.In this talk, Ben will first outline some recent advances in our understanding of the association between cardiometabolic and schizophrenia spectrum disorders. He will then introduce the field of cardiometabolic risk prediction, and highlight how existing tools developed for older general population adults are unlikely to be suitable for young people with psychosis. Finally, he will discuss the current state of play and the future of the Psychosis Metabolic Risk Calculator (PsyMetRiC), a novel clinically useful cardiometabolic risk prediction algorithm tailored for young people with psychosis, which has been developed and externally validated using data from three psychosis early intervention services in the UK.

In modern neuroscience, we often want to extract information from recordings of many neurons in the brain. Unfortunately, the activity of individual neurons is very noisy, making it difficult to relate to cognition and behavior. Thankfully, we can use the correlations across time and neurons to denoise the data we record. In particular, using recent advances in machine learning, we can build models which harness this structure in the data to extract more interpretable signals. In this talk, we present two such methods as well as examples of how they can help us gain further insights into the neural underpinnings of behavior.

Behavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child’s profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample, and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome. (https://www.medrxiv.org/content/10.1101/2021.09.15.21262637v1)

In our everyday lives, we form connections and build up social networks that allow us to function successfully as individuals and as a society. Our social networks tend to include well-connected individuals who link us to other groups of people that we might otherwise have limited access to. In addition, we are more likely to befriend individuals who a) live nearby and b) have mutual friends. Interestingly, neurons tend to do the same…until development is perturbed. Just like social networks, neuronal networks require highly connected hubs to elicit efficient communication at minimal cost (you can’t befriend everybody you meet, nor can every neuron wire with every other!). This talk will cover some of Alex’s work showing that microscopic (cellular scale) brain networks inferred from spontaneous activity show similar complex topology to that previously described in macroscopic human brain scans. The talk will also discuss what happens when neurodevelopment is disrupted in the case of a monogenic disorder called Rett Syndrome. This will include simulations of neuronal activity and the effects of manipulation of model parameters as well as what happens when we manipulate real developing networks using optogenetics. If functional development can be restored in atypical networks, this may have implications for treatment of neurodevelopmental disorders like Rett Syndrome.

Activation of pro-degenerative protein SARM1 in response to diverse physical and disease-relevant injuries triggers programmed axon death. Original studies indicated substantially decreased levels of SARM1 were required for neuroprotection. However, we demonstrate that lowering SARM1 levels by 50% in Sarm1 haploinsufficient mice delays axon degeneration in vivo (after sciatic nerve transection), in vitro (in response to diverse traumatic, neurotoxic, and genetic triggers), and partially prevents neurite outgrowth defects in mice lacking pro-survival factor NMNAT2. We also demonstrate the capacity for Sarm1 antisense oligonucleotides to decrease SARM1 levels by more than 50% which delays or prevents programmed axon degeneration in vitro. Combining Sarm1 haploinsufficiency with antisense oligonucleotides further decreases SARM1 levels and prolongs protection after neurotoxic injuries. These data demonstrate that axon protection occurs in a Sarm1 gene-dose responsive manner and that SARM1 lowering agents have therapeutic potential. Thus, antisense oligonucleotide targeting of Sarm1 is a promising therapeutic strategy against diverse triggers of axon degeneration.

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.

Neural responses are variable: even under identical experimental conditions, single neuron and population responses typically differ from trial to trial and across time. Recent work has demonstrated that this variability has predictable structure, can be modulated by sensory input and behaviour, and bears critical signatures of the underlying network dynamics and computations. However, current methods for characterising neural variability are primarily geared towards sensory coding in the laboratory: they require trials with repeatable experimental stimuli and behavioural covariates. In addition, they make strong assumptions about the parametric form of variability, rely on assumption-free but data-inefficient histogram-based approaches, or are altogether ill-suited for capturing variability modulation by covariates. Here we present a universal probabilistic spike count model that eliminates these shortcomings. Our method uses scalable Bayesian machine learning techniques to model arbitrary spike count distributions (SCDs) with flexible dependence on observed as well as latent covariates. Without requiring repeatable trials, it can flexibly capture covariate-dependent joint SCDs, and provide interpretable latent causes underlying the statistical dependencies between neurons. We apply the model to recordings from a canonical non-sensory neural population: head direction cells in the mouse. We find that variability in these cells defies a simple parametric relationship with mean spike count as assumed in standard models, its modulation by external covariates can be comparably strong to that of the mean firing rate, and slow low-dimensional latent factors explain away neural correlations. Our approach paves the way to understanding the mechanisms and computations underlying neural variability under naturalistic conditions, beyond the realm of sensory coding with repeatable stimuli.

Monetary incentive delay task (MID) is a well-validated human functional MRI task widely used in probing affective-motivational processes in psychiatric disorders. We are developing a mouse version of the MID task in order to facilitate translations of findings from the wealth of human imaging studies. This talk presents our task design and behavioural data from the ongoing work.

Optimally interacting with a changeable and uncertain world requires estimating and representing uncertainty. Psychiatric and neurodevelopmental conditions such as anxiety and autism are characterized by an altered response to uncertainty. I will review the evidence for these phenomena from computational modelling, and outline the planned experiments from our lab to add further weight to these ideas. If time allows, I will present results from a control sample in a novel task interrogating a particular type of uncertainty and their associated transdiagnostic psychiatric traits.

Alzheimer’s disease is the most common age-related neurodegenerative disorder. Familial forms of Alzheimer’s disease associated with the accumulation of a toxic form of amyloid-β (Aβ) peptides are linked to mitochondrial impairment. The coenzyme nicotinamide adenine dinucleotide (NAD+) is essential for both mitochondrial bioenergetics and nuclear DNA repair through NAD+-consuming poly (ADP-ribose) polymerases (PARPs). Here, we analysed the metabolomic changes in flies over-expressing Aβ and showed a decrease of metabolites associated with nicotinate and nicotinamide metabolism, which is critical for mitochondrial function in neurons. We show that increasing the bioavailability of NAD+ protects against Aβ toxicity. Pharmacological supplementation using NAM, a form of vitamin B that acts as a precursor for NAD+ or a genetic mutation of PARP rescues mitochondrial defects, protects neurons against degeneration and reduces behavioural impairments in a fly model of Alzheimer’s disease. Next, we looked at links between PARP polymorphisms and vitamin B intake in patients with Alzheimer’s disease. We show that polymorphisms in the human PARP1 gene or the intake of vitamin B, are associated with a decrease in the risk and severity of Alzheimer’s disease. We suggest that enhancing the availability of NAD+ by either vitamin B supplements or the inhibition of NAD+-dependent enzymes, such as PARPs are potential therapies for Alzheimer’s disease.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g. specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N=805; cortical volume, N=246; fractional anisotropy, N=165), developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade both our cognitive and neural networks. Moreover, calculating node centrality (absolute strength and bridge strength) and using two separate community detection algorithms (Walktrap and Clique Percolation), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role between brain and behavior. We discuss implications and possible avenues for future studies.

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans – which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

The inability to observe relevant biological processes in vivo significantly restricts human neurodevelopmental research. Advances in appropriate in vitro model systems, including patient-specific human brain organoids and human cortical spheroids (hCSs), offer a pragmatic solution to this issue. In particular, hCSs are an accessible method for generating homogenous organoids of dorsal telencephalic fate, which recapitulate key aspects of human corticogenesis, including the formation of neural rosettes—in vitro correlates of the neural tube. These neurogenic niches give rise to neural progenitors that subsequently differentiate into neurons. Studies differentiating induced pluripotent stem cells (hiPSCs) in 2D have linked atypical formation of neural rosettes with neurodevelopmental disorders such as autism spectrum conditions. Thus far, however, conventional methods of tissue preparation in this field limit the ability to image these structures in three-dimensions within intact hCS or other 3D preparations. To overcome this limitation, we have sought to optimise a methodological approach to process hCSs to maximise the utility of a novel Airy-beam light sheet microscope (ALSM) to acquire high resolution volumetric images of internal structures within hCS representative of early developmental time points.

Can alterations in experience trigger different plastic modifications in neuronal structure and function, and if so, how do they integrate at the cellular level? To address this question, we interrogated circuitry in the mouse olfactory bulb responsible for the earliest steps in odor processing. We induced experience-dependent plasticity in mice of either sex by blocking one nostril for one day, a minimally invasive manipulation that leaves the sensory organ undamaged and is akin to the natural transient blockage suffered during common mild rhinal infections. We found that such brief sensory deprivation produced structural and functional plasticity in one highly specialized bulbar cell type: axon-bearing dopaminergic neurons in the glomerular layer. After 24 h naris occlusion, the axon initial segment (AIS) in bulbar dopaminergic neurons became significantly shorter, a structural modification that was also associated with a decrease in intrinsic excitability. These effects were specific to the AIS-positive dopaminergic subpopulation because no experience-dependent alterations in intrinsic excitability were observed in AIS-negative dopaminergic cells. Moreover, 24 h naris occlusion produced no structural changes at the AIS of bulbar excitatory neurons, mitral/tufted and external tufted cells, nor did it alter their intrinsic excitability. By targeting excitability in one specialized dopaminergic subpopulation, experience-dependent plasticity in early olfactory networks might act to fine-tune sensory processing in the face of continually fluctuating inputs. (https://www.jneurosci.org/content/41/10/2135)

Background: Recent discovery of hundreds of common gene variants associated with schizophrenia has enabled polygenic risk scores (PRS) to be measured in the population. It is hypothesized that normal variation in genetic risk of schizophrenia should be associated with MRI changes in brain morphometry and tissue composition. Methods: We used the largest extant genome-wide association dataset (N = 69,369 cases and N = 236,642 healthy controls) to measure PRS for schizophrenia in a large sample of adults from the UK Biobank (Nmax = 29,878) who had multiple micro- and macro-structural MRI metrics measured at each of 180 cortical areas and seven subcortical structures. Linear mixed effect models were used to investigate associations between schizophrenia PRS and brain structure at global and regional scales, controlled for multiple comparisons. Results: Micro-structural phenotypes were more robustly associated with schizophrenia PRS than macro-structural phenotypes. Polygenic risk was significantly associated with reduced neurite density index (NDI) at global brain scale, at 149 cortical regions, and five subcortical structures. Other micro-structural parameters, e.g., fractional anisotropy, that were correlated with NDI were also significantly associated with schizophrenia PRS. Genetic effects on multiple MRI phenotypes were co-located in temporal, cingulate and prefrontal cortical areas, insula, and hippocampus. (Preprint: https://www.medrxiv.org/content/10.1101/2021.02.06.21251073v1)

Constipation is a common but not a universal feature in early PD, suggesting that gut involvement is heterogeneous and may be part of a distinct PD subtype with prognostic implications. We analysed data from the Parkinson’s Incidence Cohorts Collaboration, composed of incident community-based cohorts of PD patients assessed longitudinally over 8 years. Constipation was assessed with the MDS-UPDRS constipation item or a comparable categorical scale. Primary PD outcomes of interest were dementia, postural instability and death. PD patients were stratified according to constipation severity at diagnosis: none (n=313, 67.3%), minor (n=97, 20.9%) and major (n=55, 11.8%). Clinical progression to all 3 outcomes was more rapid in those with more severe constipation at baseline (Kaplan Meier survival analysis). Cox regression analysis, adjusting for relevant confounders, confirmed a significant relationship between constipation severity and progression to dementia, but not postural instability or death. Early constipation may predict an accelerated progression of neurodegenerative pathology. Conclusions: We show widespread cortical and subcortical grey matter micro-structure associations with schizophrenia PRS. Across all investigated phenotypes NDI, a measure of the density of myelinated axons and dendrites, showed the most robust associations with schizophrenia PRS. We interpret these results as indicative of reduced density of myelinated axons and dendritic arborization in large-scale cortico-subcortical networks mediating the genetic risk for schizophrenia.

Studies of neural circuit plasticity focus almost exclusively on functional and structural changes of neuronal synapses. In recent years, however, myelin plasticity has emerged as a potential modulator of neuronal networks. Myelination of previously unmyelinated axons and changes in the structure on already-myelinated axons can have large effects on the function of neuronal networks. Yet myelination has been mostly studied in relation to its functional and metabolic activity. Myelin modifications are increasingly being implicated as a mechanism for sensory-motor learning and unpublished data from our lab indicate that myelination also occurs during cognitive non-motor learning. It is, however, unclear how specific these myelin changes are and even less is known of the underlying mechanisms of learning-evoked myelin plasticity. In this journal club, Dr Giulia Bonetto will provide a general overview on myelin plasticity. Additionally, she will present new data addressing the role of myelin plasticity in cognitive non-motor learning.