Metabolic regulation of the tumour and its microenvironment: The role of Epstein-Barr virus.

The Epstein-Barr virus (EBV), the first identified human tumour virus, infects over 95% of the individuals globally and has the potential to induce different types of cancers. It is increasingly recognised that EBV infection not only alters cellular metabolism, contributing to neoplastic transformation, but also utilises several non-cell autonomous mechanisms to shape the metabolic milieu in the tumour microenvironment (TME) and its constituent stromal and immune cells. In this review, we explore how EBV modulates metabolism to shape the interactions between cancer cells, stromal cells, and immune cells within a hypoxic and acidic TME. We highlight how metabolites resulting from EBV infection act as paracrine factors to regulate the TME, and how targeting them can disrupt barriers to immunotherapy.

Target engagement of the subgenual anterior cingulate cortex with transcranial temporal interference stimulation in major depressive disorder: a protocol for a randomized sham-controlled trial.

Background: Transcranial temporal interference stimulation (tTIS) is a new, emerging neurostimulation technology that utilizes two or more electric fields at specific frequencies to modulate the oscillations of neurons at a desired spatial location in the brain. The physics of tTIS offers the advantage of modulating deep brain structures in a non-invasive fashion and with minimal stimulation of the overlying cortex outside of a selected target. As such, tTIS can be effectively employed in the context of therapeutics for the psychiatric disease of disrupted brain connectivity, such as major depressive disorder (MDD). The subgenual anterior cingulate cortex (sgACC), a key brain center that regulates human emotions and influences negative emotional states, is a plausible target for tTIS in MDD based on reports of its successful neuromodulation with invasive deep brain stimulation. Methods: This pilot, single-site, double-blind, randomized, sham-controlled interventional clinical trial will be conducted at St. Michael's Hospital - Unity Health Toronto in Toronto, ON, Canada. The primary objective is to demonstrate target engagement of the sgACC with 130 Hz tTIS using resting-state magnetic resonance imaging (MRI) techniques. The secondary objective is to estimate the therapeutic potential of tTIS for MDD by evaluating the change in clinical characteristics of participants and electrophysiological outcomes and providing feasibility and tolerability estimates for a large-scale efficacy trial. Thirty participants (18-65 years) with unipolar, non-psychotic MDD will be recruited and randomized to receive 10 sessions of 130 Hz tTIS or sham stimulation (n = 15 per arm). The trial includes a pre- vs. post-treatment 3T MRI scan of the brain, clinical evaluation, and electroencephalography (EEG) acquisition at rest and during the auditory mismatch negativity (MMN) paradigm. Discussion: This study is one of the first-ever clinical trials among patients with psychiatric disorders examining the therapeutic potential of repetitive tTIS and its neurobiological mechanisms. Data obtained from this trial will be used to optimize the tTIS approach and design a large-scale efficacy trial. Research in this area has the potential to provide a novel treatment option for individuals with MDD and circuitry-related disorders and may contribute to the process of obtaining regulatory approval for therapeutic applications of tTIS. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT05295888.

N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression.

BACKGROUND AND OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) is a safe and effective treatment for major depressive disorder (MDD); however, this treatment currently lacks reliable biomarkers of treatment response. TMS-evoked potentials (TEPs), measured using TMS-electroencephalography (TMS-EEG), have been suggested as potential biomarker candidates, with the N100 peak being one of the most promising. This study investigated the association between baseline N100 amplitude and 1 Hz right dorsolateral prefrontal cortex (R-DLPFC) accelerated rTMS (arTMS) treatment in MDD. METHODS: Baseline TMS-EEG sessions were performed for 23 MDD patients. All patients then underwent 40 sessions of 1 Hz R-DLPFC (F4) arTMS over 5 days and a follow-up TMS-EEG session one week after the end of theses arTMS sessions. RESULTS: Baseline N100 amplitude at F4 showed a strong positive association (p < .001) with treatment outcome. The association between the change in N100 amplitude (baseline to follow-up) and treatment outcome did not remain significant after Bonferroni correction (p = .06, corrected; p = .03, uncorrected). Furthermore, treatment responders had a significantly larger mean baseline F4 TEP amplitude during the N100 time frame compared to non-responders (p < .001). Topographically, after Bonferroni correction, F4 is the only electrode at which its baseline N100 amplitude showed a significant positive association (p < .001) with treatment outcome. LIMITATIONS: Lack of control group and auditory masking. CONCLUSION: Baseline N100 amplitude showed a strong association with treatment outcome and thus demonstrated great potential to be utilized as a cost-effective and widely adoptable biomarker of rTMS treatment in MDD.

Cardiopulmonary exercise variables and their association with postoperative morbidity and mortality after major oesophagogastric cancer surgery-a multicentre observational study.

Background: Outcomes after oesophagogastric cancer surgery remain poor. Cardiopulmonary exercise testing (CPET) used for risk stratification before oesophagogastric cancer surgery is based on conflicting evidence. This study explores the relationship between CPET and postoperative outcomes, specifically for patients undergoing neoadjuvant treatment. Methods: Patients undergoing oesophagogastric cancer resection and CPET (pre- or post-neoadjuvant treatment, or both) were retrospectively enrolled into a multicentre pooled cohort study. Oxygen uptake at peak exercise (VO2 peak) was compared with 1-yr postoperative survival. Secondary analyses explored relationships between patient characteristics, tumour pathology characteristics, CPET variables (absolute, relative to weight, ideal body weight, and body surface area), and postoperative outcomes (morbidity, 1-yr and 3-yr survival) were assessed using logistic regression analyses. Results: Seven UK centres recruited 611 patients completing a 3-yr postoperative follow-up period. Oesophagectomy was undertaken in 475 patients (78%). Major complications occurred in 25%, with 18% 1-yr and 43% 3-yr mortality. No association between VO2 peak or other selected CPET variables and 1-yr survival was observed in the overall cohort. In the overall cohort, the anaerobic threshold relative to ideal body weight was associated with 3-yr survival (P=0.013). Tumour characteristics (ypT/ypN/tumour regression/lymphovascular invasion/resection margin; P<0.001) and Clavien-Dindo ≥3a (P<0.001) were associated with 1-yr and 3-yr survival. On subgroup analyses, pre-neoadjuvant treatment CPET; anaerobic threshold (absolute; P=0.024, relative to ideal body weight; P=0.001, body surface area; P=0.009) and VE/VCO2 at anaerobic threshold (P=0.026) were associated with 3-yr survival. No other CPET variables (pre- or post-neoadjuvant treatment) were associated with survival. Conclusions: VO2 peak was not associated with 1-yr survival after oesophagogastric cancer resection. Tumour characteristics and major complications were associated with survival; however, only some selected pre-neoadjuvant treatment CPET variables were associated with 3-yr survival. CPET in this cohort of patients demonstrates limited outcome predictive precision. Clinical trial registration: NCT03637647.

Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data.

BACKGROUND: The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far. METHODS: The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia. RESULTS: Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%). CONCLUSIONS: The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.

Computational modeling of whole-brain dynamics: a review of neurosurgical applications.

A major goal of modern neurosurgery is the personalization of treatment to optimize or predict individual outcomes. One strategy in this regard has been to create whole-brain models of individual patients. Whole-brain modeling is a subfield of computational neuroscience that focuses on simulations of large-scale neural activity patterns across distributed brain networks. Recent advances allow for the personalization of these models by incorporating distinct connectivity architecture obtained from noninvasive neuroimaging of individual patients. Local dynamics of each brain region are simulated with neural mass models and subsequently coupled together, considering the subject's empirical structural connectome. The parameters of the model can be optimized by comparing model-generated and empirical data. The resulting personalized whole-brain models have translational potential in neurosurgery, allowing investigators to simulate the effects of virtual therapies (such as resections or brain stimulations), assess the effect of brain pathology on network dynamics, or discern epileptic networks and predict seizure propagation in silico. The information gained from these simulations can be used as clinical decision support, guiding patient-specific treatment plans. Here the authors provide an overview of the rapidly advancing field of whole-brain modeling and review the literature on neurosurgical applications of this technology.

Opposing brain signatures of sleep in task-based and resting-state conditions.

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyze the correlations between brainwide neural signatures of sleep, cognition, and depression in task and resting-state data from over 30,000 individuals from the UK Biobank and Human Connectome Project. Neural signatures of insomnia and depression are negatively correlated with those of sleep duration measured by accelerometer in the task condition but positively correlated in the resting-state condition. Our results show that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This is further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression. These observations dispute conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity.

BACKGROUND: Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia. METHODS: Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time. RESULTS: Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters. CONCLUSIONS: Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

TMS-evoked responses are driven by recurrent large-scale network dynamics.

A compelling way to disentangle the complexity of the brain is to measure the effects of spatially and temporally synchronized systematic perturbations. In humans, this can be non-invasively achieved by combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG). Spatiotemporally complex and long-lasting TMS-EEG evoked potential (TEP) waveforms are believed to result from recurrent, re-entrant activity that propagates broadly across multiple cortical and subcortical regions, dispersing from and later re-converging on, the primary stimulation site. However, if we loosely understand the TEP of a TMS-stimulated region as the impulse response function of a noisy underdamped harmonic oscillator, then multiple later activity components (waveform peaks) should be expected even for an isolated network node in the complete absence of recurrent inputs. Thus emerges a critically important question for basic and clinical research on human brain dynamics: what parts of the TEP are due to purely local dynamics, what parts are due to reverberant, re-entrant network activity, and how can we distinguish between the two? To disentangle this, we used source-localized TMS-EEG analyses and whole-brain connectome-based computational modelling. Results indicated that recurrent network feedback begins to drive TEP responses from 100 ms post-stimulation, with earlier TEP components being attributable to local reverberatory activity within the stimulated region. Subject-specific estimation of neurophysiological parameters additionally indicated an important role for inhibitory GABAergic neural populations in scaling cortical excitability levels, as reflected in TEP waveform characteristics. The novel discoveries and new software technologies introduced here should be of broad utility in basic and clinical neuroscience research.

Identifying Neurophysiological Markers of Intermittent Theta Burst Stimulation in Treatment-Resistant Depression Using Transcranial Magnetic Stimulation-Electroencephalography.

BACKGROUND: Intermittent theta burst stimulation (iTBS) targeting the left dorsolateral prefrontal cortex is effective for treatment-resistant depression, but the effects of iTBS on neurophysiological markers remain unclear. Here, we indexed transcranial magnetic stimulation-electroencephalography (TMS-EEG) markers, specifically, the N45 and N100 amplitudes, at baseline and post-iTBS, comparing separated and contiguous iTBS schedules. TMS-EEG markers were also compared between iTBS responders and nonresponders. METHODS: TMS-EEG was analyzed from a triple-blind 1:1 randomized trial for treatment-resistant depression, comparing a separated (54-minute interval) and contiguous (0-minute interval) schedule of 2 × 600-pulse iTBS for 30 treatments. Participants underwent TMS-EEG over the left dorsolateral prefrontal cortex at baseline and posttreatment. One hundred fourteen participants had usable TMS-EEG at baseline, and 98 at posttreatment. TMS-evoked potential components (N45, N100) were examined via global mean field analysis. RESULTS: The N100 amplitude decreased from baseline to posttreatment, regardless of the treatment group (F1,106 = 5.20, p = .02). There were no changes in N45 amplitude in either treatment group. In responders, the N100 amplitude decreased after iTBS (F1,102 = 11.30, p = .001, pcorrected = .0004). Responders showed higher posttreatment N45 amplitude than nonresponders (F1,94 = 4.11, p = .045, pcorrected = .016). Higher baseline N100 amplitude predicted lower post-iTBS depression scores (F4,106 = 6.28, p = .00014). CONCLUSIONS: These results provide further evidence for an association between the neurophysiological effects of iTBS and treatment efficacy in treatment-resistant depression. Future studies are needed to test the predictive potential for clinical applications of TMS-EEG markers.

In-silico EEG biomarkers of reduced inhibition in human cortical microcircuits in depression.

Reduced cortical inhibition by somatostatin-expressing (SST) interneurons has been strongly associated with treatment-resistant depression. However, due to technical limitations it is impossible to establish experimentally in humans whether the effects of reduced SST interneuron inhibition on microcircuit activity have signatures detectable in clinically-relevant brain signals such as electroencephalography (EEG). To overcome these limitations, we simulated resting-state activity and EEG using detailed models of human cortical microcircuits with normal (healthy) or reduced SST interneuron inhibition (depression), and found that depression microcircuits exhibited increased theta, alpha and low beta power (4-16 Hz). The changes in depression involved a combination of an aperiodic broadband and periodic theta components. We then demonstrated the specificity of the EEG signatures of reduced SST interneuron inhibition by showing they were distinct from those corresponding to reduced parvalbumin-expressing (PV) interneuron inhibition. Our study thus links SST interneuron inhibition level to distinct features in EEG simulated from detailed human microcircuits, which can serve to better identify mechanistic subtypes of depression using EEG, and non-invasively monitor modulation of cortical inhibition.

Magnetic resonance spectroscopy ex vivo: A short historical review.

Over the last half century, there have been several periods during which magnetic resonance spectroscopy (MRS) has been used ex vivo, for a variety of reasons, on samples such as microorganisms, cells, animal or human tissue, tissue extracts or biological fluids. These studies began in the days before the acronym MRS had been invented, when all such methods were still called nuclear magnetic resonance (NMR), and have extended to the present day. I will describe the historical development of NMR methods used ex vivo, their influences on the development of MRS in vivo, and their longer-term uses. All the interpretations will be personal, based on what I saw, or discussed with colleagues at the time.

Survival Pathways of HIF-Deficient Tumour Cells: TCA Inhibition, Peroxisomal Fatty Acid Oxidation Activation and an AMPK-PGC-1α Hypoxia Sensor.

The HIF-1 and HIF-2 (HIF1/2) hypoxia responses are frequently upregulated in cancers, and HIF1/2 inhibitors are being developed as anticancer drugs. How could cancers resist anti-HIF1/2 therapy? We studied metabolic and molecular adaptations of HIF-1ß-deficient Hepa-1c4, a hepatoma model lacking HIF1/2 signalling, which mimics a cancer treated by a totally effective anti-HIF1/2 agent. [1,2-13C2]-D-glucose metabolism was measured by SiDMAP metabolic profiling, gene expression by TaqMan, and metabolite concentrations by 1H MRS. HIF-1ß-deficient Hepa-1c4 responded to hypoxia by increasing glucose uptake and lactate production. They showed higher glutamate, pyruvate dehydrogenase, citrate shuttle, and malonyl-CoA fluxes than normal Hepa-1 cells, whereas pyruvate carboxylase, TCA, and anaplerotic fluxes decreased. Hypoxic HIF-1ß-deficient Hepa-1c4 cells increased expression of PGC-1α, phospho-p38 MAPK, and PPARα, suggesting AMPK pathway activation to survive hypoxia. They had higher intracellular acetate, and secreted more H2O2, suggesting increased peroxisomal fatty acid ß-oxidation. Simultaneously increased fatty acid synthesis and degradation would have "wasted" ATP in Hepa-1c4 cells, thus raising the [AMP]:[ATP] ratio, and further contributing to the upregulation of the AMPK pathway. Since these tumour cells can proliferate without the HIF-1/2 pathways, combinations of HIF1/2 inhibitors with PGC-1α or AMPK inhibitors should be explored.

Cardiovascular biomarkers of response to accelerated low frequency repetitive transcranial magnetic stimulation in major depression.

BACKGROUND AND OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) is an effective and safe treatment for major depressive disorder (MDD). rTMS is in need of a reliable biomarker of treatment response. High frequency (HF) dorsolateral prefrontal cortex (DLPFC) rTMS has been reported to induce significant changes in the cardiac activity of MDD patients. Low frequency DLPFC rTMS has many advantages over HF-DLPFC rTMS and thus this study aims to further investigate the effect of low frequency 1 Hz right hemisphere (R)-DLPFC rTMS on the cardiac activity of MDD patients, as well as the potential of using electrocardiogram (ECG) parameters as biomarkers of treatment outcome. METHODS: Baseline ECG sessions were performed for 19 MDD patients. All patients then underwent 40 sessions of accelerated 1 Hz R-DLPFC rTMS one week after the baseline session. RESULTS: Heart rate (HR) significantly decreased from the resting period to the first and third minute of the 1 Hz R-DLPFC rTMS period. Resting HR was found to have a significant negative association with treatment outcome. Prior to Bonferroni correction, HR during stimulation and the degree of rTMS-induced HR reduction were significantly negatively associated with treatment outcome. No significant changes were observed for the heart rate variability (HRV) parameters. LIMITATIONS: Sample size (n = 19); the use of electroencephalography equipment for ECG; lack of respiration monitoring; relatively short recording duration for HRV parameters. CONCLUSION: This novel study provides further preliminary evidence that ECG may be utilized as a biomarker of rTMS treatment response in MDD. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04376697.

Augmenting Human Selves Through Artificial Agents - Lessons From the Brain.

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI - but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called "strong AI" or "artificial consciousness." In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the "self," which we define as the "environment-agent nexus"; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD - in the implementation of artificial agents - offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA's) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

Mapping Inter-individual Functional Connectivity Variability in TMS Targets for Major Depressive Disorder.

Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.

Whole-Brain Modelling: Past, Present, and Future.

Whole-Brain Modelling is a scientific field with a short history and a long past. Its various disciplinary roots and conceptual ingredients extend back to as early as the 1940s. It was not until the late 2000s, however, that a nascent paradigm emerged in roughly its current form-concurrently, and in many ways joined at the hip, with its sister field of macro-connectomics. This period saw a handful of seminal papers authored by a certain motley crew of notable theoretical and cognitive neuroscientists, which have served to define much of the landscape of whole-brain modelling as it stands at the start of the 2020s. At the same time, the field has over the past decade expanded in a dozen or more fascinating new methodological, theoretical, and clinical directions. In this chapter we offer a potted Past, Present, and Future of whole-brain modelling, noting what we take to be some of its greatest successes, hardest challenges, and most exciting opportunities.

Piloting an approach to scab control on Welsh sheep farms.

Introduction: Sheep scab caused by Psoroptes ovis, is a disease of concern to many stakeholders in Wales due to its welfare implications. There are good diagnostic tests and treatments available to deal with the disease. Even so, it remains a problem in Welsh flocks. As such a coordinated approach is required to deal with this issue in a more sustainable manner. Pilot design: Sheep scab positive 'index' farms were initially diagnosed using a skin scrape to identify P. ovis mites. Contiguous farms were identified and antibody responses used to confirm onward infestation. All infested farms were treated by either dipping with an organophosphate (OP) dip or injecting with a licensed macrocyclic lactone (ML) product depending on farmer choice. Results: Three positive 'index' farms were identified along with 12 contiguous properties. Positive serological responses were observed in seven of the 12 contiguous farms; four of which were treated by OP dip and three by an injectable ML product. Discussion: To avoid reinfestation of treated farms, dealing with disease on contiguous properties is crucial. Through the project coordinating team, three local outbreaks of scab were dealt with in a short space of time with appropriate diagnosis and treatment being carried out. Some farmers were uncooperative and strategies such as providing additional external support and veterinary involvement might alleviate these issues in the future. This coordinated approach is recommended to veterinary surgeons in the field when dealing with scab on farm.