Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences. Sleep, along with diet and exercise, is essential for optimal health and wellbeing. However, globally, nearly 2 billion people are estimated to have one or both of the two most common clinical sleep disorders–sleep apnea  and insoCurrent sleep apnea diagnostic approaches rely on traditional labor-intensive overnight sleep tests and subjective manual scoring approaches developed around the constraints of paper-based methods from the 1960’s. This approach, in combination with the advent of continuous positive airway pressure (CPAP) to reverse airway collapse during sleep , led to Current gold-standard methodology to quantify sleep relies on overnight polysomnographic (PSG) recordings. This includes collection of a wealth of neurophysiological data from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiography (ECG), body position and movement, and respiratory-related signals including airflow, chest and abdominal motion, and oximetry. These signals are then manually reviewed and analyzed to classify wake, light through to deep non-rapid eye movement (NREM) , and rapid eye movement (REM) sleep in 30-s epochs. Transient cortical arousals (3–15 s) and longer awakening (>15 s) events are also manually scored on the basis of internationally standardized American Academy of Sleep Medicine (AASM) EEG criteria .Traditional polysomnography scoring evolved from observations of behavioral responsiveness changes coincident with changes in EEG patterns of activity at a time when chart recorders necessitated manual scoring, quite literally page-by-30-s-page . This paAutomated sleep scoring methods using advanced signal processing and machine learning approaches to analyze polysomnography signals have been widely developed and can achieve good agreement against consensus-based traditional human scoring . HoweverFor example, EEG delta waves are tightly coupled in time and precede pulsatile changes in cerebral blood volume and cerebrospinal fluid flow during deep sleep . FurtherDefining evidence-based electrophysiological sleep markers is important in the age of precision medicine, particularly following rapid growth in minimally intrusive recording and consumer wearable devices that allow for sleep-related monitoring over prolonged periods in the home environment . These aTogether, a range of new and emerging devices could routinely generate large volumes of sleep measurements over extended periods. This approach will require evidence to support clinical use and value, and software tools to assist clinicians to assess, analyze, and interpret sleep-omics . To incrThis review focuses on novel sleep metrics derived from EEG collected clinically using routine polysomnography. Other reviews regarding potential neurobiological insights of sleep physiology and circadian rhythms through high density EEG and intra-cranial/depth EEG are available elsewhere .Slow waves (0.5–4.5 Hz) are the main feature of deep sleep and one of the fundamental electrophysiological features of synchronous neuronal “down states” of relative neuronal inactivity and “up states” as activity resumes . These wK-complexes are a form of isolated slow waves that provide unique insight into sleep stability and sleep disruption. They can occur spontaneously during sleep. However, K-complexes can also provide a sensitive marker of sensory disturbance to noise, respiratory and vibratory stimuli during sleep . Abnorman < 100). In a clinical population of 47 patients with obstructive sleep apnea (OSA), greater sleep spindle activity was associated with better implicit learning  have specific frequency characteristics , as showThe odds ratio product (ORP) is a novel EEG-derived metric that provides a continuous index of sleep depth and alertness . ORP is The scale-free component of neural activity  is a further EEG component that may be an important biomarker of arousal level in human sleep . ConsistA limitation of all current biomarkers is the reliance on traditional manual scoring to express and evaluate summary values against conventional metrics with uncertain relationships with clinical endpoints. For example, absolute delta power, or ORP values, are usually averaged in NREM sleep. Spindles may be only detected in N2 sleep, and K-complex densities calculated in N2 and N3 sleep do not consider fluctuations in neurophysiological features across sleep cycles. EEG dynamics across sleep cycles are highly likely to be regulated by physiological processes such as circadian rhythms, brain metabolism, motor control learning, and memory consolidation processes e.g., . While a2) and are an almost ubiquitous device in the hospital environment , typically calculated as the number of 3 or 4% desaturations below baseline levels per hour of sleep. However, these metrics have their limitations and agreed standards for their calculation remain lacking. For example, the ODI is partly dependent on the criteria used to define SpO2 dip onsets, offsets, and duration. The ODI also only reflects the frequency of hypoxemic events and fails to reflect the degree and duration of hypoxemia and further oxidative stress through rapid reoxygenation  (2 < 90%) divided by total sleep time  , and theeep time  have beeeep time . Quantifeep time . Accordieep time  that mayeep time .Assessment of autonomic nervous system activity during sleep is facilitated using photoplethysmography and ECG. The use of these signals in sleep medicine including new analytical methods and the potential insights they can provide has been covered in recent in-depth reviews .High increases in heart rate following apneic events are associated with 30–60% increases in mortality risk and non-fatal/fatal cardiovascular disease compared to normal heart rate responses . New eviWhile an exhaustive list of sleep metrics is not the objective of this review, and recent detailed reviews are available elsewhere , a few kMotor system disorders such as periodic limb movement (PLM) and REM sleep behavior disorders (RBD) are associated with adverse outcomes. For example, PLMs are associated with stroke and cardiovascular risk factors in certain patient populations . RBD mayThe cyclic alternating pattern (CAP) is an additional sleep scoring system beyond traditional AASM sleep scoring which aims to quantify NREM discontinuity by characterizing phases of activation (A phases) and periods of inactivity (B phases) . AutomatSeveral research groups have investigated the coupling between multiple physiological signals, such as heart rate with respiratory signals (named cardio-pulmonary coupling) . CouplinThe underlying causes of the most common sleep-related breathing disorder, OSA, vary considerably between patients. Current evidence indicates that there are at least four key pathophysiological “phenotypes,” more recently termed “endotypes,” that contribute to OSA pathophysiology . While iHowever, current detailed physiological quantification of OSA endotypes is intrusive and far more complex and time-consuming to perform and analyze than standard polysomnography . Thus, tAside from advances in PSG sleep and breathing metrics, new approaches are emerging in the assessment of circadian rhythms; another key determinant of sleep and its disorders . These eGiven the major role of circadian rhythms in mediating sleep patterns and behavior, methods to assess circadian rhythms across the different manifestations of sleep disruption are likely to be insightful. In chronic insomnia, circadian rhythm factors may importantly contribute to the underlying etiology and pathophysiology . ChronobThe current “gold standard” measure of circadian rhythms is salivary or blood dim-light melatonin onset . This meEmerging methods include advanced monitoring devices and biomathematical modeling to infer circadian rhythm metrics . Newer tThe sleeping environment affects sleep ability, but is minimally assessed in routine clinical practice. Consequently, sleep disruption may be misattributed to endogenous factors alone, ignoring the potential impact of exogenous factors. These include noise, light, temperature, and other factors that impact comfort within the sleep context. In a laboratory environment, these factors are typically well-controlled and designed to be conducive for sleep. However, as the assessment of sleep disruption shifts from the laboratory to the less well-controlled home environment, the assessment and consideration of environmental factors becomes increasingly important to understand mechanisms of sleep disruption.Potentially the strongest exogenous influencer of sleep is noise, which can adversely affect sleep attainment and maintenance and fragment sleep to reduce total sleep time and quality . The mosThere are two seemingly opposing challenges regarding sleep monitoring and diagnostics. There is a need for greater in-depth insight into the underlying neurobiology of sleep, yet there is also a need for less intrusive and user-friendly technology. Detailed, in-depth assessments and monitoring approaches as well as smarter use of existing signals and information derived from traditional polysomnography approaches are required to better understand sleep pathology. Yet, given the burden of disease and the scale of sleep disruption in the community, there is also a pressing need for less intrusive sleep tracking technology that can be readily and easily adopted in a home-based setting.A plethora of technologies have emerged to track sleep in the home setting . These iNew and emerging approaches to better define sleep and circadian disruption and its consequences offers considerable promise to move beyond the limitations of current sleep metrics and management. To improve outcomes, these approaches need to be underpinned by consideration for underlying neurobiology and will likely require a multisystem approach to capture the diverse impacts that sleep and circadian disruption can have on health and wellbeing. Development of practical, inexpensive methods to assess sleep and circadian disruption, its key contributors, and consequences at scale, including comprehensive, long-term remote monitoring has the potential to transform sleep medicine and management. This includes implementation of precision sleep medicine and targeted therapy approaches.All authors contributed to drafting and/or revising one or more of the sections of this manuscript, provided feedback on the final version, and agreed to be accountable for the content of the work.DE has a Collaborative Research Centre (CRC-P) Grant, a consortium grant between the Australian Government, Academia and Industry  and has research grants and serves as a consultant for Bayer and Apnimed. AV has received research grant funding and equipment from ResMed and Philips Respironics. PC has received research funding from Defence Science and Technology and received research grant funding and equipment from Philips Respironics. The funders Philips Respironics, ResMed, Oventus Medical, Bayer, Apnimed were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Lighthouse for Older Adults, an innovative public-private partnership, was developed in response to COVID-19 as a means of advancing telehealth for low-income older adults living in affordable housing communities. Residents of these communities often don’t have reliable access to devices, sufficient bandwidth for telehealth, or adequate social services, further complicated by the need for multi-lingual and culturally sensitive programs. This presentation will share program implementation strategies and outcomes, including the essential role telehealth services play in the care and wellbeing of older adults during and beyond COVID-19. This session will review evidence-based components of a telehealth intervention, including digital literacy training and technology support. Key drivers for successful implementation  as well as barriers to implementation  will be reviewed. Lessons learned through program replication and scaling of Lighthouse telehealth services will be discussed.


Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments. Deep brain stimulation is an established treatment option for patients with movement disorders , as demonstrated in randomized controlled trials . CurrentClosed-loop strategies offer the possibility to optimize DBS by automatically adjusting the timing and parameters of stimulation in real time based on biomarkers . The adaA putative limitation of current closed-loop strategies is their restricted scope, in which biomarkers, controller design and parameter choices are optimized to a unique symptom or neural manifestation in isolation. However, clinical states are dynamic, multi-faceted, and inter-connected. Some operate at the millisecond range while others evolve over many hours. They can occur independently or influence each other. Consequently, even though aforementioned closed-loop approaches showed improved efficacy over standard continuous DBS in well-controlled research conditions, the question of whether a satisfactory 24 h therapeutic coverage of multiple symptoms may be achieved with such strategies is far from clear.A global framework is critically missing to guide the integration of all these developments into a clinically relevant therapeutic portfolio that exploits recent advances in implantable neurotechnologies . This inWe suggest that the structure of clinical and neurophysiological manifestations, segregated in time and space over multiple layers (see section below), may be mirrored by control strategies to steer the design of modular therapies embedding parallel control loops. This principled framework may guide future developments and become an integral part of next generation closed loop DBS systems.Motor and non-motor symptoms exhibit highly diverse temporal properties. They emerge at different timepoints, progress at various speeds over the course of the disease, and diurnally fluctuate in intensity with according to their own variable time-constants. These distinct temporal behaviors are further intertwined since clinical manifestations can occur simultaneously or influence each other, adding a layer of complexity in the management of symptoms. For instance, tremor oscillations  stand in contrast to slow-changing states such as a dopaminergic wearing off episodes, which affect the condition of patients in the range of hours. Yet both states can be temporally related, as the likelihood of tremor episodes in PD may increase during wearing OFF dopaminergic states. Moreover, throughout the day, patients are engaged in different physiological states such as walking, talking, or sleeping, which may also continuously or intermittently be affect by disease-specific symptoms.via different temporal dynamics to address the same or various clinical goals.Signals to control DBS may be derived from neural recordings in the brain, peripheral sensors, or a combination of sources. Neural signals may encode various slow- or fast-changing states. Even depending on the way they are analytically processed, a same biomarker may be used to regulate control loops operating at different time scales. For instance, the better explored closed-loop DBS approaches for PD have employed beta oscillations in subthalamic nucleus (STN), which correlate with bradykinesia and rigidity . Closed-A comprehensive understanding of the temporal properties governing different clinical manifestations and neurophysiological signatures, along with their interdependencies, is thus critical for the design of therapies that can optimally address multiple states in parallel. We outline a selection of different clinical and neurophysiological layers relevant to closed-loop therapies.Across disorders, tremor tends to appear episodically (lasting from less than minutes up to hours), favored for instance by insufficient pharmacological control or agitation . Tremor Biomarkers and closed-loop strategies: Approaches for closed-loop DBS explored multiple control sources and control paradigms. Some used peripheral sensors to measure the amplitude of movements in the tremor frequency range  or delivGait and balance deficits are common in PD, and induce a broad range of impairments including reduced arm swing and step length, shuffling steps, festination, freezing of gait or lack of postural control . This phBiomarkers and closed-loop strategies: During gait execution, alternating right and left gait cycles (1–2 Hz) are accompanied by periodic, time-locked modulations in the beta and gamma band power in STN LFP . Recent In addition, how freezing of gait (FoG) episodes could be delineated and targeted remains unanswered. In contrast to the alternating neuronal activity patterns during locomotion, FoG has been linked to the occurrence of prolonged bursts of beta activity  and firsConsidering these multi-faceted manifestations, therapies may need to flexibly combine (i) continuous adaptations in DBS during gait execution, as well as (ii) actively switching settings to improve and stabilize locomotion and prevent FoG .Progressive speech impairments are common in various neurological disorders. Both in PD and ET, DBS often leads to further deterioration of speech performance which plays a limiting factor in the optimization of DBS .Biomarkers and closed-loop strategies: Closed-loop DBS may prevent speech deterioration, which is often encountered as a side-product during continuous DBS . It may The later stages of PD are characterized by fluctuations of motor and non-motor symptoms that are difficult to control with standard therapies . ON/OFF Biomarkers and closed-loop strategies: Currently, beta activity recorded from the basal ganglia (particularly the STN), represents the best-characterized biomarker to inform about drug-induced fluctuations, bradykinesia and rigidity . DifferePhysiologically beta activity appears as short bursts (100 and 200 ms) . HoweverThe temporal dynamics of beta activity can also be processed at longer temporal scales, with time-constants in the range of minutes. This processing does not capture beta burst dynamics, but instead accounts for clinical OFF/ON fluctuations related to medication intake. Adaptive DBS trials using this temporal resolution have been successfully piloted with a smoothing time constant of 50 s and a slow proportional controller that adapted stimulation accordingly . A direc60–90 Hz frequency activity detected in the electrocorticogram, has been linked to the presence of dyskinesia in the ON medication state, and represents a promising electrophysiological biomarker to regulate DBS for such manifestations . This apCurrent knowledge already delineates how control algorithms may need to follow and integrate different temporal dynamics of distinct biomarkers.Standard DBS therapies assume that the patient is in the same clinical state throughout the whole 24-h cycle. However, PD can be associated with different sleep problems such as REM-sleep behavior disorders (RBD), which can range from seconds to minutes, and alterations of sleep architecture. Several studies reported that STN DBS has a deepening and consolidating impact on nocturnal sleep .Biomarkers and closed-loop strategies: It is not yet clear how DBS should optimally act during sleep. Treatment goals and stimulation parametrization are likely to be different than those during daytime. Sleep therapies could potentially be optimized by considering sleep architecture and pathological sleep phenomena. An important prerequisite is the recognition of sleep stages, so that therapies may adapt to their specific requirements. NREM 1–3 and REM stages alternate cyclically, as defined by standard 30-s epochs classification systems . RecordiDespite the heterogeneity of clinical manifestations and neurophysiological signatures, the time-constants that govern their individual behaviors may be categorized into discrete temporal layers . This laCross-layer interferences inevitably arise in multi-objective control. They happen when one control loop  induces  a change in manifestation 2, which in turn triggers a response in a second control loop, and so on. If unaccounted for, interferences may lead controllers to diverge. Importantly, interferences are less likely to occur when controlled variables have different time constants. This “temporal decoupling” allows to pause one therapy, for instance a slow controller operating in the hour range, and to temporarily deliver another one (a fast controller reacting to an episodic event in the range of seconds), without much impact on the earlier.Overall, the complexity of developing therapies that can address multiple manifestations may be distributed over three hierarchical levels of operation : the lowWe propose that this hierarchical organization may simplify the design of multi-objective control therapies, while allowing to easily integrate existing algorithmic strategies for closed-loop DBS. In this integrative framework, control loops targeting different temporal layers are combined in a modular manner and operate in parallel . For a gA variety of control strategies have been proposed for addressing individual manifestations through closed-loop DBS. They relied predominantly on fast control approaches, either bang-bang controllers triggered by one  of two  thresholExamples from other neural engineering applications highlight the benefits of data-driven predictive models in closed-loop therapies. These used either movement sensor data or neural signals  to control prostheses or robotic systems , spinal The variety of clinical and neurophysiological manifestations also shapes the choice of middle-level control strategies to manage the joint outcome of multiple objectives, and critically affects the robustness and stability when addressing them concurrently. We outline various approaches that may be used:1.State machine controllers allow to switch between independent states and make it possible to deliver various therapies in an interleaved manner. This approach is simple and easy to tune, as it only requires a few parameters (transitions). However, state machines do not directly manage interferences. They are thus most useful for processes that operate at clearly distinct layers . Example2.Unidirectional adaptations allow to tune the parameters of one controller using feedback of another temporal layer. This cross-layer interaction makes it possible to link two (or more) temporal layers, regardless of how far apart layers are. Examples include tuning the threshold for detecting beta-bursts based on feedback of ON-OFF fluctuations .3.Optimization controllers employ a function of joint dynamics and are thus able to intrinsically account for interactions between manifestations. They provide the best way to deal with dependencies and are appropriate for layers that have similar time-constants . However, these strategies are complex and require building models of the underlying processes and their responses to stimulation. Models may use biophysical, population-based or data-driven black box (machine learning) approaches, and allow to include feedforward and feedback control loops to more accurately control multiple objectives .Overall for a specific patient, the implementation of a suitable therapy capable of addressing multiple symptomatic traits would involve (i) establishing the patient profile, his/her specific therapeutic requirements and their time constants (similar to a “system identification” step), (ii) establishing what low-level controllers (modules) need to be activated to address each one of these manifestations, (iii) defining the neuro-physiological signatures or feedback signals that will drive each low-level controller, along with the objective to be achieved by each one of them, (iv) calibrating the individual parameters of each controller, e.g., threshold values, control coefficients, adaptation rates (optimized for each module in isolation), (v) defining possible inter-dependencies and interferences, and the best way to address them (choice of middle-level control type) based on the number of low-level controllers, their expected crosstalk and the relative importance of the manifestations that they monitor (defining priorities), (vi) evaluate the stability of the combined control strategy and establishing boundaries and safety measures to prevent divergence. This process may be repeated separately for each discrete program (high level control layer) e.g., one for the day and one for the night.Steps (i–vi) may need to be done iteratively, over multiple sessions. Modularity would allow to incrementally refine therapies, adding one low-level controller at a time and tuning middle-layer control strategies accordingly to cope with added modules.To increase stability and robustness over time, each low-level controller may include a self-adaptation term that tracks changes occurring over time and slowly adapts . Its impBeside the conceptual framework of multi layered control, the technological requirements (hardware and software) to implement such comprehensive closed-loop strategies should not be left unmentioned. There are crucial technical capability demands for neurostimulators in the future. For instance, devices need to be able to monitor and differentially process multiple electrophysiological brain biomarkers and integrate them in the decision-making process (as outlined above). Such co-processing capabilities to flexible handle multiple inputs have been piloted , and neeFor over 30 years, DBS therapies have been restricted to continuous paradigms. Advances in implantable technology now offer the possibility to monitor and control neural signatures in chronically implanted patients, providing the technical substrate to deploy truly personalized therapies. More than ever, it is important to draw awareness on the multi-faceted and dynamic nature of clinical and neurophysiological manifestations. A conceptual framework is critical to steer the development of therapies that can manage multiple dynamical objectives in parallel and integrate existing closed-loop strategies into a clinically relevant therapeutic portfolio. Modularity will play a key role in rendering these approaches manageable, allowing to easily select and tune therapies that operate on multiple temporal layers, and linking them to patient-specific electro-clinical profiles. While several technological and neurophysiological advances are still needed to enable nested multilayer control capabilities, hardware, software and therapeutic developments will need to go hand in hand. The proposed conceptual framework may thus represent an integral part next generation precision medicine instruments.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.GT and EMM contributed to the conceptual design, writing, editing, and generation of figures for the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The study of long-range GABAergic projections has traditionally been focused on those with subcortical origin. In the last few years, cortical GABAergic neurons have been shown to not only mediate local inhibition, but also extend long-range axons to remote cortical and subcortical areas. In this review, we delineate the different types of long-range GABAergic neurons (LRGNs) that have been reported to arise from the hippocampus and neocortex, paying attention to the anatomical and functional circuits they form to understand their role in behavior. Although cortical LRGNs are similar to their interneuron and subcortical counterparts, they comprise distinct populations that show specific patterns of cortico-cortical and cortico-fugal connectivity. Functionally, cortical LRGNs likely induce timed disinhibition in target regions to synchronize network activity. Thus, LRGNs are emerging as a new element of cortical output, acting in concert with long-range excitatory projections to shape brain function in health and disease. Cortical inhibitory GABAergic neurons can act locally or remotely through short-range or long-range axons, respectively . They haThus far, most of the work has focused on characterizing LRGNs, with a few papers having studied their impact on neural circuit function. In this review, we will describe the diverse anatomical circuits formed by LRGNs across different cortical regions. Whenever possible we will highlight the pre and postsynaptic cell types that compose the circuit, incorporating their morphological and functional characteristics. As a comparison, we will briefly introduce essential features of local inhibitory circuits in the cortex as well as classical non-cortical GABAergic projections. Cortical LRGNs are defined as GABA-producing neurons that extend their axons outside of their region of origin, e.g., the hippocampus (HPC) or neocortex, enabling communication between functionally distinct brain regions . AccordiTo understand how cortical LRGNs uniquely contribute to brain function and behavior, we will start by introducing key features of their better-known counterparts, namely cortical GABAergic interneurons (INs) and subcortical GABAergic projection neurons. Cortical LRGNs will be discussed depending on whether they originate from the HPC or the neocortex, two regions of intense research. Finally, we will summarize works that reveal LRGN modulation of behaviors and postulate general principles underlying LRGN function.morphological, electrophysiological and transcriptomic features into “met-types”  receive glutamatergic inputs from different cortical and thalamic areas, and in the case of the ventral portion, also from the ventral HPC and amygdala . FurtherThe amygdala is made up of a diverse collection of interconnected nuclei to regulate emotionally relevant behaviors . For exaIn addition to mediating local inhibition, GABA neurons in the VTA also provide long-distance inhibition to several distinct brain regions, including the basal ganglia, amygdala, dorsal raphe nucleus, HPC and prefrontal cortex as revealed by retrograde tracing . Interesvia disinhibition in downstream brain regions.Besides its better known endocrine function, the hypothalamus, at least the lateral portion (LH), is another important brain structure that sends GABAergic projections to distant regions like the basal forebrain , VTA Ni, 2016, aAn essential characteristic of cortical areas is its organization in layers, which is thought to be crucial for amplifying and computing incoming signals. Here, we will consider as cortical structures the neocortex and the HPC . Early evidence suggesting the existence of cortical LRGNs came from the observation of non-pyramidal retrogradely labeled somata in the cortex . The advvia its various subfields  from layer II/III of EC, a region with medial (MEC) and lateral (LEC) anatomical subdivisions . These ghemistry  to potenhemistry . More rehemistry , and thuC and MS , HILs maC and MS . These dstratum lacunosum moleculare (SLM). CA3 pyramidal neurons project their excitatory axons through Schaffer collaterals in the stratum radiatum (SR) to the CA1 area. CA1 pyramidal cells are the main output of the HPC, but also receive direct excitatory inputs from the EC  and direct glutamatergic inputs from layer II of the EC in m the EC . Additiom the EC .Although distributed LRGNs in CA3 have been found, not much information about them is available. A common feature of CA3-originating LRGNs is that they express SOM as a molecular marker but are heterogeneous, showing a diversity of co-expression with other markers . The somstratum oriens (SO) and SR, extending hundreds of microns horizontally throughout MEC layer I  have been discovered to also project to extrahippocampal regions such as the band of broca and the tenia tectum in the frontal cortex  and RSC that project to the caudal striatum , showingLong-range GABAergic neurons in the motor cortex (MC) located in deep layers connect distal subcortical and cortical regions that influence movement behaviors . In primMore recently, through viral genetic means, VIP-LRGNs in M1 has been shown to innervate the striatum . Their dIn the auditory cortex (AC), LRGNs corresponding to diverse neurochemical classes of cortical GABAergic neurons  have been found, and they project to different subcortical regions. For example, as revealed by viral tracing, SOM + LRGNs projecting to the lateral amygdala (LA) were primarily located in layers V/VI of AC , potentiLRGNs in the auditory cortex can also express VIP, innervating remote areas including the ipsilateral striatum, amygdala and medial geniculate body as well as the superior and inferior colliculus . The funin vivo activation of these inhibitory projections to NAcc elicits avoidance, mPFC-NAcc LRGNs may either directly inhibit D1R-MSNs or inhibit local GABAergic INs to disinhibit D2R-MSNs. However, the specific circuits that drive this avoidance behavior remain to be described. A caveat is the finding that LRGNs in the mPFC also project to other subcortical targets, including dorsal striatum, claustrum and amygdala  and GABA from optogenetically activated septal fibers occurs  neurons . Interes neurons .Neuropeptides do not have to be co-transmitted with GABA to modulate its function. In the hypothalamus, sustained postsynaptic depolarization induces somatodendritic secretion of CCK that subsequently triggers NO production and potentiates presynaptic GABA release . FurtherThere is a general agreement that behavior not only depends on the activity of one structure but also needs to be coordinated among several brain areas . In thisin vivo calcium imaging, Basu and colleagues demonstrate that the GABAergic axon terminals from the LEC onto CA1 interneurons are active during unimodal sensory stimulation . Interestingly, during multimodal stimulation, calcium activity of these terminals further increased, suggesting that LEC-CA1 LRGNs integrate multisensory signals to provide contextual information. Indeed, inactivation of these LRGNs impairs learning by changing the specificity and context of formed memories but not memory storage per se. At the circuit level, this disinhibitory pathway can shape a form of synaptic plasticity that depends on the timing of inputs; whether this mechanism directly participates in contextual learning remains to be determined. However, it is noteworthy that this study revealed a role of LRGNs in synaptic plasticity that is considered to be a neural correlate of learning and memory (Several recent works support a role of LRGNs in brain function. For example, LEC-CA1 LRGNs have been shown to contribute to sensory processing . Using id memory .In the olfactory system, LRGNs from the cortex and basal forebrain have been suggested to exert inhibitory feedback onto subcortical sensory areas to regulate odor discrimination . In thisLong-range GABAergic neurons in the PFC are anatomically connected to several subcortical areas, but their impact on network function and behavior is only beginning to be revealed. For example, activation of PFC-NAcc LRGNs induces avoidance in a real-time place preference task, suggesting that these inhibitory connections transmit aversive signals and modulate motivational valence . InteresPFC-HPC LRGN activation also increases gamma and theta power within the HPC . MoreoveThe function of cortical LRGNs is also related to their activation patterns. In the HPC, LRGNs originating from CA1 SO increase their firing during immobility , but theCortical LRGNs in general may function to mediate timed cross-regional dialogue. For example, using viral tracing and optogenetic stimulation, PV + LRGNs have been found to directly connect bilateral cortical regions including the AC, VC and MC . In AC, Although the presence of LRGNs in the cortex challenges the classical view of local inhibition, evidence of their ubiquitous existence and function continues to strengthen. Cortical LRGNs are not simply conventional non-cortical GABAergic projection neurons replicated in the cortex. Canonical subcortical long-range GABAergic projections are more numerous, and even in some cases they emanate from principal cells that express calcium-calmodulin kinase IIα rather than the classical GABA-producing cell markers observed in the cortex . NeverthCortical LRGNs are not simply cortical INs with long axons, but instead may exhibit distinctive synaptic targeting patterns and kinetic properties. Therefore, although INs and LRGNs share some electrophysiological and molecular similarities, it is presumptuous to think that LRGNs act like their short-range counterparts. As discussed in this review, SOM + LRGNs can deviate from the dendrite-targeting phenotype of SOM-INs in the HPC , corticain vivo electrophysiological recordings have made great strides. It will be important to determine the circumstances by which these LRGNs are recruited by the network and to assess the causal relationship of their activity to performance in specific tasks. The growing availability of activity-dependent reporters, viral tracers and transgenic mice will facilitate in vivo imaging and manipulation of labeled LRGNs throughout the cortex in live animals. In addition, many of the cellular targets of the different LRGNs remain unknown, as well as their pattern of innervation, the properties of the synapses they form and how these synapses are modulated by the great variety of neurochemicals present in the brain. The combination of tracing techniques and high throughput single cell-resolution imaging technology such as light-sheet microscopy will enable the discovery of novel circuits formed by LRGNs as well as propel electrophysiological interrogation of synaptic plasticity and neuromodulation in long-range GABAergic communication. If these long-range inhibitory synapses show activity-dependent plasticity, what is the implication for learning and memory? In addition to discovering new cortical LRGNs, the next steps in research need to focus on advancing our knowledge of the known cortical LRGNs with more detailed study of their function under normal and pathological conditions.The functional impact of cortical LRGNs on behavior is beginning to be revealed. In this regard, JU-P, CM-M, and CQC conceived of the general ideas presented in this review. JU-P and CM-M drove this work to completion, contributing equally to all sections. NS-G and APE contributed to the specific subsections of the written work, provided feedback, and helped with figure construction. CQC supervised the work. All authors made significant contributions to the information content and writing of this final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Neuronal hyperexcitability in the central auditory pathway linked to reduced inhibitory activity is associated with numerous forms of hearing loss, including noise damage, age-dependent hearing loss, and deafness, as well as tinnitus or auditory processing deficits in autism spectrum disorder (ASD). In most cases, the reduced central inhibitory activity and the accompanying hyperexcitability are interpreted as an active compensatory response to the absence of synaptic activity, linked to increased central neural gain control (increased output activity relative to reduced input). We here suggest that hyperexcitability also could be related to an immaturity or impairment of tonic inhibitory strength that typically develops in an activity-dependent process in the ascending auditory pathway with auditory experience. In these cases, high-SR auditory nerve fibers, which are critical for the shortest latencies and lowest sound thresholds, may have either not matured  or are dysfunctional . Fast auditory processing deficits can occur despite maintained basal hearing. In that case, tonic inhibitory strength is reduced in ascending auditory nuclei, and fast inhibitory parvalbumin positive interneuron (PV-IN) dendrites are diminished in auditory and frontal brain regions. This leads to deficits in central neural gain control linked to hippocampal LTP/LTD deficiencies, cognitive deficits, and unbalanced extra-hypothalamic stress control. Under these conditions, a diminished inhibitory strength may weaken local neuronal coupling to homeostatic vascular responses required for the metabolic support of auditory adjustment processes. We emphasize the need to distinguish these two states of excitatory/inhibitory imbalance in hearing disorders: (i) Under conditions of preserved fast auditory processing and sustained tonic inhibitory strength, an excitatory/inhibitory imbalance following auditory deprivation can maintain precise hearing through a memory linked, transient disinhibition that leads to enhanced spiking fidelity  (ii) Under conditions of critically diminished fast auditory processing and reduced tonic inhibitory strength, hyperexcitability can be part of an increased synchronization over a broader frequency range, linked to reduced spiking reliability . This latter stage mutually reinforces diminished metabolic support for auditory adjustment processes, increasing the risks for canonical dementia syndromes. Hearing loss is a very common problem in the aging population of industrial societies. Globally, an estimated 1.57 billion people had hearing loss in 2019, accounting for one in five people (20.3%) . The proHere, we review how impaired auditory input can affect the excitatory/inhibitory balance within the central auditory system and suggest that hearing loss and cognitive decline may be linked through changes in the excitatory/inhibitory balance associated with the functional attenuation of distinct auditory fiber types. We further suggest that these changes in the excitatory/inhibitory balance may, in turn, influence neurovascular coupling, possibly further affecting cognitive function in aging.In the following, we lay out this idea in more detail. First, we provide an overview of the development of inhibitory GABAergic circuits (see Section “Maturation of GABA-Responsive Neurons Prior to Hearing Onset”). Second, we describe the role that different auditory nerve-fiber types might play during development and in regulating the excitatory/inhibitory balance in the auditory system . We then discuss the role of fast auditory processing see  may playWe define fast auditory processing as the increase in auditory acuity that is linked to lowering of hearing thresholds, increased suprathreshold ABR waves I and IV, the shortening of first spike latencies, and widening of response dynamic range with auditory experience, all shown in DCN neurons , IC neurWe define increased central neural gain as the identifiable network homeostasis that increases stimulus-evoked synchronous neural activity at the level of the inferior colliculus (IC) (ABR wave IV) relative to its input at the level of the auditory nerve (ABR wave I). Increased central neural gain can occur following, e.g., auditory deprivation  or sound enrichment. As a multi-level framework, central neural gain includes a positive auditory feedforward and positive fronto-striatal feedback cycle that require co-activation. Mechanistically increased central neural gain likely requires a reinforcement process, as it is also known from auditory perception or improved task performance i) is maintained in most neurons, ensured by the sodium-potassium-chloride co-transporter type 1 (NKCC1)  and at lower auditory brain levels , are spliced to a common protein-encoding exon (IX)    . Both exxon (IX) , cyan anxon (IX) , yellow xon (IX) .Bdnf promoters in SGN to drive the depolarizing-to-hyperpolarizing switch in a bottom-up direction within the ascending auditory circuits both prior to and following hearing onset  . In respng onset , BDNF⇑. ng onset  or CRF lng onset  may be r–]I concentrations.In summary, prior to the first auditory experience and during hearing onset, an initial period of hyperexcitability exists, with excitatory activity dominating over inhibitory activity. Within this transient time period, GABA-responsive neurons have reached their target regions but still react with depolarizing responses, due to the low level of neuronal KCC2 and high [Clvia ribbon synapses  IHC-driven synaptic events that mature during hearing onset, (ii) differences in cochlear IHC output activity through differential maturation of efferent feedback to auditory fibers, as well as (iii) differences in the genetic fate of SGNs. After hearing onset, fast auditory processing  matures The sharpening of cortical receptive fields, i.e., narrower bandwidth responses, occurs for all sensory cortices, including the auditory cortex  as a resImportant to mention here is that the overall process of maturation of fast auditory processing appears to be dispensable for basal hearing function. Thus, when BDNF was deleted in GABAergic precursor neurons in the brainstem of mice under the Pax2 promoter, and despite normal hearing thresholds based on measuring outer hair cell function, supra-threshold auditory nerve (ABR wave I) amplitudes remained low and the late ABR wave IV was delayed, indicating that fast auditory processing may have not matured properly . As a reTo obtain an idea when this critical time period of maturation of inhibitory strength occurs in auditory and associated circuits in humans, we have to consider that the fast inhibitory PV-IN activity regulates not only higher cortical microcircuit functions , but alsWe thus conclude that auditory experience-dependent maturation processes of high-SR auditory nerve fibers in the auditory system are critical for the maturation of fast auditory processing, including the formation of activity-driven, fast inhibitory PV-IN microcircuits. Only then is the neuronal network implemented for a fine-grained resolution of sound discrimination, temporally precise hearing, and fast discrimination of novel auditory information .Numerous studies have indicated that acoustic trauma and age-dependent hearing loss are linked to reduced inhibition and enhanced excitation in ascending auditory circuits . Since lIn addition to low-SR auditory fiber processing sounds , sustainde Kloet et al. (2014)Bdnf transcripts can be seen in nerve endings, glia cells, and capillaries  of default mode network activity, including the prefrontal cortex regions (PFC) . In the 66Met polymorphism  mutant mouse   , elevate(VGlut3)  and from(VGlut3) . Since aexcluded . In geneexcluded  as a resexcluded , or as aexcluded  due to mexcluded . We propexcluded  ; i.e., if not restored early enough, do the relevant auditory brain circuits remain in a stage of insufficient inhibitory strengths that hampers precise sharpening of receptive fields and proper inhibitory strength in the fine-grained microcircuits required for speech discrimination and temporal coding ? The imm1, which is used as an objective measure of developmental hearing experience  showed an autism-like phenotype , linked with enhanced LTP and maintained temporal processing  Figure  Figure , suggestg group, , experimg group, . In thesIn questioning whether reduced tonic inhibitory strength following fast auditory processing may be particularly critical for cognition, as predicted from autism animal models (see Section “Failed Fast Auditory Processing in Autism Spectrum Disorders”), the critical time period of maturation of fast auditory processing and inhibitory strength in auditory and associated limbic circuits - between the 2nd and 3rd postnatal week in rodents  needs toWe questioned whether the maturation of fast (high-SR) auditory processing, of inhibitory PV-IN microcircuits, of hemodynamic BOLD fMRI responses, and of LTP may not only be correlated in time, but also functionally. More precisely, we asked whether the imbalances in excitation and inhibition in hearing disorders that correlated with reduced tonic inhibitory strength, as predicted in the case of impaired fast auditory nerve fiber responses , might aThis hypothesis is based on new insights into the mechanism of hemodynamic responses: Previously, glutamatergic neuronal activity was assumed to mainly trigger hemodynamic responses and vasodilation during a bilateral homeostatic response: Glutamatergic neuronal activity, such as neural feedforward signaling, includes neuronal-derived nitric oxide (NO) release from the glutamatergic synapses that causes a metabolic feedback signal in smooth muscle cells of parenchymal arterioles, finally leading to vasodilation [for a review see The contradicting assumptions, that on the one hand NO-release from GABA-IN may influence endothelia cells of blood vessels, and thereby change their diameter , while oIt is thus challenging to consider that the increase in PV-IN mediated inhibitory strength between the 2nd and 3rd postnatal week  and the During aging or following acoustic trauma, acute or congenital deafness, a critical diminution of fast (high-SR) auditory driving force diminishes activity-dependent BDNF activities and tonic-PV-IN strength, hippocampal LTP, extra-hypothalamic stress control and possibly proper coupling of inhibitory neuronal activity to hemodynamic responses, accelerating a negative feedback cycle (In view of the increasing evidence of a link between hearing loss and dementia, a better understanding of this possible relationship is an important challenge . We suggMK designed, wrote, and revised the manuscript. WS wrote the manuscript and made the figures. KS, GEH, LR, CB, and RL wrote and revised the manuscript. YLH wrote the figure legends and helped writing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Multiparametric magnetic resonance imaging (mpMRI) has improved the triage of men with suspected prostate cancer, through precision prebiopsy identification of clinically significant disease. While multiple important characteristics, including tumour grade and size have been shown to affect conspicuity on mpMRI, tumour location and association with mpMRI visibility is an underexplored facet of this field. Therefore, the objective of this systematic review and meta-analysis is to collate the extant evidence comparing MRI performance between different locations within the prostate in men with existing or suspected prostate cancer. This review will help clarify mechanisms that underpin whether a tumour is visible, and the prognostic implications of our findings.The databases MEDLINE, PubMed, Embase and Cochrane will be systematically searched for relevant studies. Eligible studies will be full-text English-language articles that examine the effect of zonal location on mpMRI conspicuity. Two reviewers will perform study selection, data extraction and quality assessment. A third reviewer will be involved if consensus is not achieved. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines will inform the methodology and reporting of the review. Study bias will be assessed using a modified Newcastle-Ottawa scale. A thematic approach will be used to synthesise key location-based factors associated with mpMRI conspicuity. A meta-analysis will be conducted to form a pooled value of the sensitivity and specificity of mpMRI at different tumour locations.Ethical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations.CRD42021228087. This systematic review protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols guidelines.This review addresses a gap in the literature with a detailed assessment of the crucial questions surrounding invisible multiparametric magnetic resonance imaging lesions.Due to the limited evidence on the topic, the included evidence will be retrospective cohort studies, limiting the strength of conclusions drawn for the thematic synthesis and meta-analysis.The degree of heterogeneity may limit the generalisability of the results.Multiparametric magnetic resonance imaging (mpMRI) has improved the triage of men with suspected prostate cancer, through precision prebiopsy identification of significant disease. Nevertheless, anywhere from 7% to 55% of prostate cancer lesions may be overlooked by mpMRI.Although the link between tumour location and conspicuity on mpMRI has not yet been fully elucidated, location does appear to be associated with clinical risk. Transitional zone (TZ) tumours have been associated with lower pathological grade diseaseThis systematic review protocol has been written in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocol (PRISMA-P) 2015 checklist.The screening process will be conducted by two independent reviewers, removing irrelevant articles based on titles and abstracts. Pertinent studies will be downloaded and their full text assessed to see if they meet the inclusion criteria. Any dispute between reviewers over the relevance of a study will be discussed until concordance is achieved, or a third reviewer will be consulted. The reasons for exclusion will be documented and detailed in the PRISMA flow diagram. Before starting screening, to minimise inter-reviewer bias, we will conduct calibration exercises to maintain consistency between the two reviewers.Studies should compare MRI performance between different locations of the prostate in men with existing or suspected prostate cancer. Zonal location may be defined as traditional McNeal zones,Conference abstracts, expert opinions, correspondence articles and case reports will be excluded. Studies that do not correlate zonal location and mpMRI conspicuity will be excluded, as will studies not in the English-language.Like the study selection process, reviewers will then independently extract the relevant study information using the Cochrane data extraction form. Discrepancies between the data extraction will be resolved by consensus. The following study characteristics will be collated: year of publication, authors, study design, patient population, number and age of study participants, mpMRI scoring scheme used (PI-RADS or Likert), definition of clinically significant disease, definition of tumour visibility, sample processing approach, zone(s) assessed and the differential quantification of conspicuity. The primary endpoint will be the differential tumour visibility on mpMRI, as stratified by location. Secondary endpoints will include explanatory links between location and mpMRI conspicuity, as well as the potential clinical implications. As study methodology may impact the determination of the zone, we will include these variables in a moderator analysis .The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool to assess bias in diagnostic accuracy studies will be calculated across the included studies.As previously described,mi), the total number of patients without prostate cancer (in), the true positives , false positives , true negatives  and false negative  will be recorded. In the case of studies which do not give sufficient detail to derive these values but do list sensitivity or specificity and total patient numbers, these values will be reverse engineered, for example in the case of a study which reports mpMRI sensitivity for a TZ lesion at 0.788 in a group of 60, the true positives can be calculated as i=mipiy rounded to the nearest integer or TP=60 × 0.788=47.28 (47.3 true positives). The respective sensitivities and specificities of mpMRI for tumours within differing zonal locations will then be compared. The distribution of untransformed, logit and double arcsine transformed sensitivities and specificities will be compared. Whichever distributions resemble a normal distribution (assessed using density plots and Shapiro-Wilk tests) will be used for further analysis.For each topological location reported across studies, the total number of lesions with prostate cancer (2); if significant, a random-effect model will be fitted, or a fixed-effect model if not. After fitting a model to all relevant studies, leave-one-out (LOO) analyses and accompanying diagnostic plots will be used to identify influential studies, including externally studentised residuals, difference in ﬁts values, Cook’s distances, covariance ratios, LOO estimates of the amount of heterogeneity, LOO values of the test statistics for heterogeneity, hat values and weights. Studies with a statistically significant influence on the fitted model will be removed as outliers and the model refitted. These outliers will be examined for potential confounding variables such as study methodology or poor interobserver agreement for mpMRI scans. Predicted sources of heterogeneity in mpMRI performance include interstudy variation in MRI reader experience, the MRI scoring system used and the MRI magnet strength used, so these will be accounted for in the interpretation of the results. For each tumour location, individual models will first be fit and the pooled sensitivities, specificities, negative predictive value and positive predictive value will be reported. Finally, using tumour location as a subgroup, a bivariate model will be fit to the data and any differences in sensitivity and specificity will be compared using SROC curves and tested using the χ2 likelihood ratio test.The model fitted will be determined based on interstudy variation  or visibility definition. Analytics will be performed as outlined by Wang.Following Level 1 evidence support of mpMRI-directed prostate cancer diagnosisUnusual radiological manifestations of prostate cancer are not uncommon due to nuanced histopathological landscape in each zone, rendering MRI signal generation highly varied. In the peripheral zone, several radiological ‘hiding places’ exist, such as very distal apical tumours, subcapsular crescentic tumours, tumours mimicking the posterior midline and infiltrative peripheral zone tumours.In summary, this systematic review and meta-analysis will combine the extant evidence in this emerging field, for the first time. Collation and analysis of these data will enrich our understanding of the effect of zonal location on the conspicuity of prostate cancer on mpMRI. Additionally, this process will help reveal the potential clinical role that these effects play in both diagnosis and treatment, thereby aiding identification of important avenues for future research.Preliminary searches: started.Piloting of the study selection process: started.Formal screening: startedData extraction: not started.Risk of bias assessment: not started.Data analysis: not started.(Prostate cancer OR prostate tumo*r OR prostate lesion)AND AND (mpMRI OR multiparametric MRI OR multiparametric MR imaging OR mp-mri)AND Due to the nature of the study, there are no relevant ethical concerns and informed consent will not be required. The protocol and systematic review will be disseminated via a peer-reviewed journal.


Increasing misinformation spread, including news about COVID-19, poses a threat to older adults but there is little empirical research on this population within the fake news literature. Embedded in the Changes in Integration for Social Decisions in Aging (CISDA) model, this study examined the role of (i) analytical reasoning; (ii) affect; and (iii) news consumption frequency, and their interplay with (iv) news content, in determining fake news detection in aging during the COVID-19 pandemic. Young  and older  adults were randomly assigned to view COVID or non-COVID news articles, followed by measures of analytical reasoning, affect, and news consumption frequency. Comparable across young and older adults, fake news detection accuracy was higher for news unrelated to COVID, and non-COVID fake news detection was predicted by individual differences in analytic reasoning. Examination of chronological age effects further revealed that detection of fake news among older adults aged over 70 years depended on interactions between individual CISDA components and news content. Collectively, these findings suggest that age-related susceptibility to fake news may only be apparent in later stages of older adulthood, but vulnerabilities are context dependent. Our findings advance understanding of psychological mechanisms in fake news evaluation and empirically support CISDA in its application to fake news detection in aging.


Experimental models of the central nervous system (CNS) are imperative for developmental and pathophysiological studies of neurological diseases. Among these models, three-dimensional (3D) induced pluripotent stem cell (iPSC)-derived brain organoid models have been successful in mitigating some of the drawbacks of 2D models; however, they are plagued by high organoid-to-organoid variability, making it difficult to compare specific gene regulatory pathways across 3D organoids with those of the native brain. Single-cell RNA sequencing (scRNA-seq) transcriptome datasets have recently emerged as powerful tools to perform integrative analyses and compare variability across organoids. However, transcriptome studies focusing on late-stage neural functionality development have been underexplored. Here, we combine and analyze 8 brain organoid transcriptome databases to study the correlation between differentiation protocols and their resulting cellular functionality across various 3D organoid and exogenous brain models. We utilize dimensionality reduction methods including principal component analysis (PCA) and uniform manifold approximation projection (UMAP) to identify and visualize cellular diversity among 3D models and subsequently use gene set enrichment analysis (GSEA) and developmental trajectory inference to quantify neuronal behaviors such as axon guidance, synapse transmission and action potential. We showed high similarity in cellular composition, cellular differentiation pathways and expression of functional genes in human brain organoids during induction and differentiation phases, i.e., up to 3 months in culture. However, during the maturation phase, i.e., 6-month timepoint, we observed significant developmental deficits and depletion of neuronal and astrocytes functional genes as indicated by our GSEA results. Our results caution against use of organoids to model pathophysiology and drug response at this advanced time point and provide insights to tune in vitro iPSC differentiation protocols to achieve desired neuronal functionality and improve current protocols. Early studies into human brain embryonic development used human embryonic stem cells (hESCs) to produce embryoid bodies and generate neural precursors, which, in turn, could be directed to various fates such as neurons , oligodeTo address these shortcomings, recent studies have focused on developing 3D organoid in vitro models that better recapitulate the complex structure of the brain microenvironment. However, to develop 3D brain organoids, researchers rely on the endogenous developmental program with little control over the composition of the final tissue . These oTo support the mechanical integrity of the tissue and promote 3D cellular growth, some protocols use extracellular-matrix derived hydrogels such as Matrigel . During Single-cell RNA sequencing (scRNA-seq) is a promising method to study the entire transcriptome of individual cells, enabling an unprecedented understanding of cellular compositions and cell-type-specific expression profiles in the brain. With single cell resolutions and high throughput processing, which allows the analysis of hundreds of thousands of cells, this method has provided novel insights into cellular function and diversity . DiffereAnother study by Kinugawa et al. compared cellular diversity in human brain organoids of brainstem (hBSOs) and of midbrain (hMBOs) and showed region-specific differences in cell types, specifically, they identified hindbrain progenitors expressing Zic Family Member 1 (ZIC1) and Zic Family Member 4 (ZIC4) and microglia expressing Allograft Inflammatory Factor 1 (AIF1) only in hBSOs, whereas hMBOs uniquely contained radial glia and mesenchymal cells. Their analysis, however, does not cover developmental pathways giving rise to these region-specific differences. Moreover, characterization of cell-type-specific functional pathways across different developmental phases and time points is lacking.Here, we implement a comparative analysis on aggregate single-cell RNA-seq data amassed from eight recent organoid differentiation protocols as well as from studies conducted on fetal human brain samples . These dDroplet-based scRNA-seq data from brain organoids were collected from public resources and combined with human fetal brain scRNA-seq datasets as in vivo benchmarks. scRNA-seq datasets used in this study are part of the public domain and accessible as gene-cell count matrices on NCBI Gene Expression Omnibus (GEO) with study, year and accession number listed in key resource table.scRNA-seq datasets were pre-processed to ensure that reads corresponded to viable cells. In short, the data sets were evaluated to exclude possible doublets by filtering out cells with a very high number of genes expressed . Next, gAs count depths are generated following reverse transcription, PCR amplification and subsequent library preparation steps necessary to perform next generation sequencing, final count depths can differ significantly due to inherent variation at any of these intermediate steps. Thus, raw counts were normalized to obtain correct relative gene expressions. We used a commonly applied normalization protocol depth scaling or count per million (CPM) normalization and subsequently log1p transformation. This protocol normalizes count data by a factor proportional to the count depth per cell . While nCorrecting batch effect artifacts and improving data integration from multiple batches are of special emphasis as data sets used are generated in multiple laboratories under different sets of experimental and culture conditions that can significantly affect the transcriptome. To address this challenge, we utilized a recently published algorithm  that prAs scRNA-seq data contains expression values for all human genes for each cell, the resulting normalized count matrix has the dimensions: number of cells times the number of genes. While the number of cells can be from a few hundred to hundreds of thousands, the number of genes in the dataset can be up to 25,000. Analysis and visualization in this 25,000-dimensional space is not very informative and computationally prohibitive. Thus, to ease the downstream analysis and reduce noise, a common method is to select specific genes and perform a dimensionality reduction on the dataset. It is most informative to limit the analysis to the genes that have the highest level of expression variability among the cells, as these highly variable genes (HVGs) best capture the differences between cellular specification and function. We used about 1000 over-dispersed genes as feature genes for downstream analysis. The number of folds in the cross-validation of random forest analysis was varied between 5 and 10. Biologically, the genes expressed at similar levels across many different cell types can be referred to as “housekeeping genes” with vital functions for metabolism, cellular growth, immune recognition, and many other functions. However, since expression level of these genes is similar across many cell types, they do not provide the best means of clustering and differentiating between various cell types present in the dataset and are thus filtered out for the purpose of dimensionality reduction. Dimensionality reduction allows visualization of the entire dataset in two or three dimensions. Common dimensionality reduction methods can be linear such as principal component analysis (PCA) , or non-The developmental path of cells was traced by calculating the cellular representation in diffusion map space to denoise the graph, followed by computing a neighborhood graph and coarse-grained PAGA graph using the corresponding functions in Scanpy. A force directed graph was computed with PAGA initialization, which preserves the global topology of the manifold, and then visualized using pl.paga_compare function with edge threshold value set at 0.3.p-values < 0.05 were considered as statistically significant. Differential expression analysis for identification of cluster-specific expression at gene level was performed using Scanpy rank genes groups function with Wilcoxon, t-test and t-test overestimated variance specified for the method parameter.The enrichment of the gene signature was evaluated by GSEA  (v4.1.0)To analyze the transcriptomic profiles and features of cells during human brain development, we obtained and integrated the transcriptomics datasets from 3D brain organoids and fetal brain samples. Batch-specific variations are present in single-cell transcriptomic datasets due to differences in single cell and library generation technologies, variations in protocols and operations among different laboratories, which complicates integration, and effective batch correction with limited computational resources. Many methods have been recently developed to mitigate batch effects without confounding biological variations, in depth review and benchmarks are reported elsewhere ,28. HereWe validated the cellular composition and characterization as described by Tanaka et al. , but witBy using differential expression analysis among the clusters, we identified two predominant specific markers for each cell type e. ClusteEukaryotic translation initiation factor 1 (EIF1), a RNA transport and translation factor of which the reduced expression has been implicated in Parkinson’s disease  and metaDifferential expression and gene ranking analyses were carried out using three methods, namely, the t-test, t-test with overestimated variance, and the Wilcoxon method. The Wilcoxon results are reported in e. As depThe complex biological processes that govern brain development rely on the spatiotemporally precise control of signaling pathways including the wingless/integrated (Wnt), bone morphogenetic protein (BMP), Notch, and Sonic hedgehog (SHH), among others.Organoid models provide the opportunity to model, using an in vitro system, morphogen signaling, which momentously impacts cellular development during embryogenesis but can be difficult to elucidate without the appropriate models. For instance, Wnt signaling is important in cell fate determination and regional patterning, but the absolute effects are stage-specific and depend on regional and temporal gene expression, along with other factors. Wnt antagonists are highly expressed in the anterior region of embryos and most organoid protocols utilize Wnt inhibition to induce neural cell proliferation and specification. At later stages of specification after anteroposterior axis formation, Wnt signaling remains an important pathway by repressing ventral cell fates in forebrain progenitors.While Wnt signaling is important in patterning of the anteroposterior axis, BMP signaling is the key pathway in the formation of medio-lateral patterns. During neural crest formation, antagonists expressed in dorsal mesoderm regulate BMP signaling within the ectoderm to generate the intermediate level of BMP signaling that is required to specify neural, epidermal and neural crest lineages . MoreoveNotch signaling is initiated when a Notch receptor on one cell interacts with a Notch ligand, such as Delta or Jagged-1 and Jagged-2, on another cell, which triggers the release of intracellular Notch domain that translocates to the nucleus and initiates transcriptional activation . ThroughTo quantitatively measure and relate expression profiles to phenotypic developmental behaviors such as axon development, neural differentiation, and Notch signaling, we identified specific gene subsets from literature and published databases including the Molecular Signatures Database (MSigDB), Gene Ontology Biological Process (GOBP), and the Gene Ontology Resource . To studp value)  ad ad40] adAmong the various functional development pathways in neural populations, axon guidance, synaptic transmission and action potential are of special importance. To further characterize cell type-specific neural function in organoids, we analyzed the enrichment of gene sets related to these functions d,f, withAmong genes related to synaptic formation and function in organoids, we investigated tropomodulin 2 (TMOD2), a member of the tropomodulin actin-regulatory proteins that caps the actin filament ends to prevent elongation and depolymerization during synapse formation, and kinesin family member 1B (KIF1B), a transport protein that guides synaptic vesicle precursors. TMOD2 was most predominantly expressed in late-stage excitatory neurons which suggests well defined network structure and mature synapse formation in organoids after 12 weeks. Highest levels of KIF1B expression were found among early neural progenitors that differentiate into excitatory and inhibitory neurons, with higher remnant expression in excitatory neurons and glutamatergic neuron subpopulations. However, KIF1B was also expressed in significant amounts in other cell populations including radial glia, astrocytes, and OPCs, suggesting auxiliary function in neural cells that warrants further investigation.+-gated ion channels and other critical cellular membrane complexes to the spectrin-actin cytoskeleton at the axonal initial segments and nodes of Ranvier, thereby enabling action potential signals. Sodium voltage-gated channel alpha subunit 2 (SCN2A) encodes one of the alpha subunits of voltage-gated sodium channels that function in initialization and propagation of action potentials. The expression of these action potential-associated genes was almost exclusively restricted to excitatory neurons and was more pronounced in neurons at weeks 12 and 24, consistent with electrophysiological activity in terminally differentiated neurons.Ankyrin 3 (ANK3) is a member of the ankyrins family of proteins, and the ankyrin-G isoform encoded by this gene links NaMorphologically, astrocyte protrusions have been shown to make contact with post-synaptic elements . These protrusions express proteins such as glutamate transporters (GLT1), potassium channels, cell adhesion molecules, i.e., ephrin, and lactate transporters with specific roles in glutamate and glutamine uptake/cycling and providing energy substrates required in synaptic reorganization and transmission . SynaptiOrganoid models with self-organizing, 3D structure, provide a unique opportunity to model complex processes involved in human brain development. They can carry out the endogenous differentiation programs that give rise to diverse cell types and tissue and organ level function such as network formation, myelination, and electrophysiological activity, as well as region-specific identities. Guided protocols use signaling molecules to further advance differentiation efficiencies and regional specificity of the organoids. Compared to traditional neural culture models, organoids provide significantly more elaborate and representative microenvironments, while maintaining relative ease and accessibility for experimentation, observation, and genomic manipulation.Moreover, they can be derived from disease-specific cell lines which further enhances their value as disease modeling platforms. Despite these advantages, current organoid models are still limited due to issues with reproducibility and variability across cell lines and protocols and cellular repertoires, while diverse, remain incomplete. In this study, we provided a systematic analysis of cellular diversity and reproducibility of gene-expression patterns among organoids derived from different protocols, outlined specific differentiation trajectories and developmental paths that give rise to these cell types, and compared cellular maturity across timepoints. We further analyzed enrichment of gene networks that govern important cellular signaling pathways influencing neural development, including Notch, Wnt, and BMP signaling, as well as specific cellular functionality such as neuron synapse formation, action potential, axonal development, and guidance. We observed similar expression patterns between organoids and in vivo fetal samples in most pathways at the 3-month time point; however, by six months, the organoids showed relatively delayed neural maturation that might be attributable to lack of vascularization and neuroendocrine interactions via the cerebrospinal fluid . Our anaUltimately, the approach presented here would complement current drug discovery and disease modeling methods and can expedite the assessment procedure by providing more accurate predictions of the native tissue responses and enabling the visualization of various cell type transitions and systemic expression changes. This integrative approach provides a framework for the evaluation of organoids as models of human brain development and improving existing protocols.