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.

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.

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.

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.

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.

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.

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.

Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits.

Cortical processing depends on finely tuned excitatory and inhibitory connections in neuronal microcircuits. Reduced inhibition by somatostatin-expressing interneurons is a key component of altered inhibition associated with treatment-resistant major depressive disorder (depression), which is implicated in cognitive deficits and rumination, but the link remains to be better established mechanistically in humans. Here we test the effect of reduced somatostatin interneuron-mediated inhibition on cortical processing in human neuronal microcircuits using a data-driven computational approach. We integrate human cellular, circuit, and gene expression data to generate detailed models of human cortical microcircuits in health and depression. We simulate microcircuit baseline and response activity and find a reduced signal-to-noise ratio and increased false/failed detection of stimuli due to a higher baseline activity in depression. We thus apply models of human cortical microcircuits to demonstrate mechanistically how reduced inhibition impairs cortical processing in depression, providing quantitative links between altered inhibition and cognitive deficits.

HIF-1-Independent Mechanisms Regulating Metabolic Adaptation in Hypoxic Cancer Cells.

In solid tumours, cancer cells exist within hypoxic microenvironments, and their metabolic adaptation to this hypoxia is driven by HIF-1 transcription factor, which is overexpressed in a broad range of human cancers. HIF inhibitors are under pre-clinical investigation and clinical trials, but there is evidence that hypoxic cancer cells can adapt metabolically to HIF-1 inhibition, which would provide a potential route for drug resistance. Here, we review accumulating evidence of such adaptions in carbohydrate and creatine metabolism and other HIF-1-independent mechanisms that might allow cancers to survive hypoxia despite anti-HIF-1 therapy. These include pathways in glucose, glutamine, and lipid metabolism; epigenetic mechanisms; post-translational protein modifications; spatial reorganization of enzymes; signalling pathways such as Myc, PI3K-Akt, 2-hyxdroxyglutarate and AMP-activated protein kinase (AMPK); and activation of the HIF-2 pathway. All of these should be investigated in future work on hypoxia bypass mechanisms in anti-HIF-1 cancer therapy. In principle, agents targeted toward HIF-1ß rather than HIF-1α might be advantageous, as both HIF-1 and HIF-2 require HIF-1ß for activation. However, HIF-1ß is also the aryl hydrocarbon nuclear transporter (ARNT), which has functions in many tissues, so off-target effects should be expected. In general, cancer therapy by HIF inhibition will need careful attention to potential resistance mechanisms.

Tumor Cell IDO Enhances Immune Suppression and Decreases Survival Independent of Tryptophan Metabolism in Glioblastoma.

PURPOSE: Glioblastoma (GBM) is an incurable primary brain tumor that has not benefited from immunotherapy to date. More than 90% of GBM expresses the tryptophan (Trp) metabolic enzyme, indoleamine 2,3-dioxygenase 1 (IDO). This observation supported the historical hypothesis that IDO suppresses the antitumor immune response solely through a mechanism that requires intratumoral Trp depletion. However, recent findings led us to investigate the alternative hypothesis that IDO suppresses the anti-GBM immune response independent of its association with Trp metabolism. EXPERIMENTAL DESIGN: IDO-deficient GBM cell lines reconstituted with IDO wild-type or IDO enzyme-null cDNA were created and validated in vitro and in vivo. Microarray analysis was conducted to search for genes that IDO regulates, followed by the analysis of human GBM cell lines, patient GBM and plasma, and The Cancer Genome Atlas (TCGA) database. Ex vivo cell coculture assays, syngeneic and humanized mouse GBM models, were used to test the alternative hypothesis. RESULTS: Nonenzymic tumor cell IDO activity decreased the survival of experimental animals and increased the expression of complement factor H (CFH) and its isoform, factor H like protein 1 (FHL-1) in human GBM. Tumor cell IDO increased CFH and FHL-1 expression independent of Trp metabolism. Increased intratumoral CFH and FHL-1 levels were associated with poorer survival among patients with glioma. Similar to IDO effects, GBM cell FHL-1 expression increased intratumoral regulatory T cells (Treg) and myeloid-derived suppressor cells while it decreased overall survival in mice with GBM. CONCLUSIONS: Our study reveals a nonmetabolic IDO-mediated enhancement of CFH expression and provides a new therapeutic target for patients with GBM.

Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.