Digital Twin Brain: Generating Multitask Behavior from Connectomes for Personalized Therapy

In the human brain, default mode network BOLD deactivations can be accompanied by both increases and decreases in glucose metabolism, depending on the respective metabolic demands of task-positive cognitive control and attention networks.

Mapping Cortical Activity Elicited with Electrical Microstimulation Using fMRI in the Macaque

Age-related decline in cognitive control and general slowing are prominent phenomena in aging research. These declines in cognitive functions have been shown to also involve age-related decline in brain structure. However, most evidence in support of these associations is based on cross-sectional data. Therefore, the aim of this study is to contrast cross-sectional and longitudinal analyses to re-examine if the relationship between age-related brain structure and cognitive function are similar between the two approaches. One hundred and two participants completed two sessions with an average interval of 2 years. All participants were assessed by questionnaires, a series of cognitive tasks, and they all underwent neuroimaging acquisition. The main results of this study show that the majority of the conclusions regarding age effect in cognitive control function and processing speed in the literature can be replicated based on the cross-sectional data. Conversely, when we followed up individuals over an average interval of 2 years, then we found much fewer significant relationships between age-related change in gray matter structure of the cognitive control network and age-related change in cognitive control function. Furthermore, there was no “initial age” effect in the relationships between age-related changes in brain structure and cognitive function. This finding suggests that the “aging” relationship between brain structure and cognitive function over a short period of time are independent of “initial age” difference at time point 1. The result of this study warrants the importance of longitudinal research for aging studies to elucidate actual aging processes on cognitive control function.

Behavior needs neural variability

'Digital twins', also called 'digital patient twins' or 'virtual human twins' - digital patient-specific models derived from multimodal health data - are a strong focus in health care and are emerging as a promising tool for improving personalized care in uro-oncology. These models can integrate clinical, genomic, imaging and histopathological information to simulate organ behaviour and disease progress as well as predict responses to treatments. The concept of digital twins has shown potential in various fields, but its application in uro-oncology is still evolving, with few assessments of their feasibility and clinical utility. The advent of artificial intelligence adds a new dimension to their development, potentially enabling the synthesis of diverse, high-quality datasets to improve modelling accuracy and support real-time decision-making. However, substantial challenges exist, including data integration, patient privacy, computational demands and ethical frameworks. In addition, the interpretability of predictions remains essential for gaining clinical trust and guiding patient-centred decisions. The use of digital twins in uro-oncology has the potential to improve patient stratification and treatment planning; however, barriers must be overcome for their successful implementation in clinical routine. By integrating new technologies, fostering interdisciplinary collaboration and prioritizing transparency, digital twins could shape the future of precision uro-oncology.

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Resting-state functional magnetic resonance imaging (rs-fMRI) and brain functional connectome (we use 'brain connectome' hereafter for simplicity) have advanced our understanding of the ageing brain and age-related changes in cognitive function. Previous studies have investigated the association among brain connectome and age, global cognition, and memory function separately. However, very few have predicted age, overall cognitive functioning and memory performance in a single study to better understand their complex relationship. In this cross-sectional study, we applied an exploratory, data-driven method to investigate the brain connectome markers that could predict ageing, overall cognitive functioning assessed as intelligence quotient (IQ, measured by Wechsler Memory Scale) and memory performance assessed as memory quotient (MQ, measured by Wechsler Memory Scale) in a carefully designed, multicentre, normal ageing cohort (n = 313). Our results showed that brain connectome could predict ageing and IQ, but the association with MQ was weak. We found that the connectivity with orbital frontal cortex was associated with both ageing and IQ. Mediation analysis further showed that the brain connectome mediated the relationship between age and overall cognitive functioning, suggesting a protective brain connectomic mechanism for maintaining normal cognitive functions during healthy ageing. This work may shed light on the potential neural correlates of healthy ageing, overall cognitive functioning and memory performance.

BackgroundThe concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient.ObjectiveThe objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature.MethodsWe searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed.ResultsWe identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining “digital twin,” 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization.ConclusionsWe propose defining a patient digital twin as “a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions” and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care.

Dopamine (DA) modulates corticostriatal connections. Studies in which imaging of the DA system is integrated with functional imaging during cognitive performance have yielded mixed findings. Some work has shown a link between striatal DA (measured by PET) and fMRI activations, whereas others have failed to observe such a relationship. One possible reason for these discrepant findings is differences in task demands, such that a more demanding task with greater prefrontal activations may yield a stronger association with DA. Moreover, a potential DA–BOLD association may be modulated by task performance. We studied 155 (104 normal-performing and 51 low-performing) healthy older adults (43% females) who underwent fMRI scanning while performing a working memory (WM) n-back task along with DA D2/3 PET assessment using [11C]raclopride. Using multivariate partial-least-squares analysis, we observed a significant pattern revealing positive associations of striatal as well as extrastriatal DA D2/3 receptors to BOLD response in the thalamo–striatal–cortical circuit, which supports WM functioning. Critically, the DA–BOLD association in normal-performing, but not low-performing, individuals was expressed in a load-dependent fashion, with stronger associations during 3-back than 1-/2-back conditions. Moreover, normal-performing adults expressing upregulated BOLD in response to increasing task demands showed a stronger DA–BOLD association during 3-back, whereas low-performing individuals expressed a stronger association during 2-back conditions. This pattern suggests a nonlinear DA–BOLD performance association, with the strongest link at the maximum capacity level. Together, our results suggest that DA may have a stronger impact on functional brain responses during more demanding cognitive tasks.SIGNIFICANCE STATEMENT Dopamine (DA) is a major neuromodulator in the CNS and plays a key role in several cognitive processes via modulating the blood oxygenation level-dependent (BOLD) signal. Some studies have shown a link between DA and BOLD, whereas others have failed to observe such a relationship. A possible reason for the discrepancy is differences in task demands, such that a more demanding task with greater prefrontal activations may yield a stronger association with DA. We examined the relationship of DA to BOLD response during working memory under three load conditions and found that the DA–BOLD association is expressed in a load-dependent fashion. These findings may help explain the disproportionate impairment evident in more effortful cognitive tasks in normal aging and in those suffering dopamine-dependent neurodegenerative diseases (e.g., Parkinson's disease).

Local action for global vision

Aging is associated with declines in autonomic nervous system (ANS) function, impaired neurovascular coupling, and diminished cerebrovascular responsiveness—factors that may contribute to cognitive decline and neurodegenerative diseases. Understanding how aging alters the integration of physiological signals in the brain is crucial for identifying potential interventions to promote brain health. This study examines age-related differences in coupling between low-frequency cardiac rate and respiratory volume fluctuations and the blood oxygenation level-dependent (BOLD) signal, using two independent resting-state fMRI datasets with concurrent physiological recordings from younger and older adults. Our findings reveal significant age-related reductions in the percent variance of the BOLD signal explained by heart rate (HR), respiratory variation (RV), and end-tidal CO2, particularly in regions involved in autonomic regulation, including the orbitofrontal cortex, anterior cingulate cortex, insula, basal ganglia, and white matter. Cross-correlation analysis also revealed that younger adults exhibited stronger HR–BOLD coupling in white matter, as well as a more rapid BOLD response to RV and CO2 in gray matter. Additionally, we investigated the effects of heart rate variability biofeedback (HRV-BF) training, a non-invasive intervention designed to modulate heart rate oscillations. The intervention modulated physiological–BOLD coupling in a manner dependent on both age and training condition: older adults who underwent HRV-BF to enhance HR oscillations exhibited a shift toward younger-like HR–BOLD coupling patterns. These findings suggest that HRV-BF may help mitigate age-related declines in autonomic or cerebrovascular function. Overall, this study underscores the role of physiological dynamics in brain aging and highlights the importance of considering autonomic function when interpreting BOLD signals. By demonstrating that HRV-BF can modulate physiological–BOLD interactions, our findings suggest a potential pathway for enhancing cerebrovascular function and preserving brain health across the lifespan.

Esmolol is often applied perioperatively to maintain stable hemodynamic conditions in neurosurgical patients. Little is known, however, about its effects on cerebral circulation. The authors employed functional magnetic resonance imaging based on blood oxygenation level-dependent contrast to explore the effect of esmolol on the human brain. The purpose of the study was to investigate the effect of esmolol on cerebral blood flow, cerebral vasoreactivity, and cognitive performance. Ten healthy volunteers were investigated in two separate experimental sessions using functional magnetic resonance imaging. During the first experimental session, a hyperventilation task and a cognitive task, subjects had to perform both tasks twice, once after administration of an esmolol bolus of 1 mg/kg followed by a continuous infusion of 150 microg.kg.min and once without beta-blockade, in a random order. During the second experimental session subjects were scanned at resting state after administration of esmolol. Furthermore, the effect of the esmolol dose on hemodynamic changes caused by beta-adrenergic stimulation with orciprenaline was investigated. Esmolol decreased heart rate and blood pressure during the various experimental conditions and blunted the increase in heart rate and blood pressure caused by orciprenaline. Infusion of esmolol affects neither the blood oxygenation level-dependent contrast during the functional challenges nor the reaction times during the cognitive task. However, the esmolol bolus caused a brief blood oxygenation level-dependent contrast increase. The results indicate that effective beta-blockade with esmolol does not affect cerebral blood flow, cerebrovascular reactivity, or cognitive performance.

The literature on cognitive markers in preclinical AD is reviewed. The findings demonstrate that impairment in multiple cognitive domains is typically observed several years before clinical diagnosis. Measures of executive functioning, episodic memory and perceptual speed appear to be most effective at identifying at-risk individuals. The fact that these cognitive domains are most implicated in normal cognitive aging suggests that the cognitive deficit observed preclinically is not qualitatively different from that observed in normal aging. The degree of cognitive impairment prior to the diagnosis of Alzheimer's disease (AD) appears to generalize relatively well across major study characteristics, including sample ascertainment procedures, age and cognitive status of participants, as well as time to diagnosis of dementia. In episodic memory, there is evidence that the size of the preclinical deficit increases with increasing cognitive demands. The global cognitive impairment observed is highly consistent with observations that multiple brain structures and functions are affected long before the diagnosis of AD. However, there is substantial overlap in the distribution of cognitive scores between those who will and those who will not be diagnosed with AD, hence limiting the clinical utility of cognitive markers for early identification of cases. Future research should consider combining cognitive indicators with other types of markers (i.e. social, somatic, genetic, brain-based) in order to increase prediction accuracy.

Although neuroendocrine changes after induction of hypoglycemia, in patients with diabetes and healthy persons, are thoroughly investigated, cognitive adaptation processes are still insufficiently understood. Changes in cognitive functions are mainly investigated by psychometric tests, which represent a summation of different cognitive processes. We aimed at dissecting cognitive adaptation into single components, i.e. stimulus selection, response choice, and reaction speed during a hyperinsulinemic hypoglycemic clamp in patients with type-1 diabetes and matched healthy controls. Using novel neurophysiological analyses, the event-related potentials of early stimulus selection (selection negativity) and response selection (lateralized readiness potential) were studied, in addition to reaction time (RT). A total of 12 diabetic patients and 12 normal volunteers were studied while receiving a hyperinsulinemic hypoglycemic clamp. RTs and the event-related potentials related to stimulus selection and response selection were significantly delayed during hypoglycemia in both groups, whereas early evoked potentials (P100) were unaltered. This suggests that hypoglycemia delays stimulus selection, with the consequence that also central and motor processing are delayed. In addition, patients with diabetes showed an earlier negative shift over the frontal cortex, which, when compared with the controls, reveals better adaptation to hypoglycemia in frontal cortical brain regions. After restoration of euglycemia stimulus selection, response selection and RT returned to baseline level in the type-1 group. In the control group, however, response selection and RTs were still delayed. This suggests that type-1 patients, possibly because of the past occurrence of hypoglycemic events, might be able to better cope with the hypoglycemic state than healthy volunteers who lack such a history. In summary, our data demonstrate, for the first time, that cognitive adaptation processes to an experimental hypoglycemic episode can clearly be dissected into their single components.

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35351, “Digital Twin for Model-Based Operational Support: Innovative Applications,” by Chinenye E. Ogugbue, SPE, Zachary M. Greenawalt, and Yevgeniy Kondratenko, BP, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ Network simulation and optimization are a well-established methodology for the operator in increasing and sustaining production capacity. To improve existing workflows, the operator has enhanced its previous petroleum- and process-engineering-focused toolkit and has deployed globally a production system that uses digital twins from end to end for model-based surveillance and optimization. The cloud-based production-system digital twin connects sensor data from each asset’s data historian to an equipment-data model and first-principle steady-state simulation tools to create a reliable status of the well network and processing facilities. Integrated Production-System Digital Twin By applying cloud computing technology in the proposed system, all complex calculations are executed on the virtual machines and users can access the digital twin with a Web-based application. Comprised of two subsystems—well network and processing facilities—the end-to-end production-system digital twin began with a focus only on the subsurface well-production network. By leveraging first-principle hydraulic models to balance pressure, flow rates, and temperature dynamically from all components in the system, the well-network digital twin has proven to be robust and user-friendly enough to simulate complex steady-state operation scenarios with high fidelity and is widely used for surveillance and production optimization across production assets. Upon the full deployment of the well-network digital twin, development focus shifted toward simulating production facilities. The digital twin for the processing facility uses a first-principle compositional-chemical-process simulator to model the processes involved in oil, gas, and water processing mathematically. In its standalone capacity, this production-facility digital twin adds value by offering virtual sensors and enhancing existing sensors. Moreover, it enables the rigorous evaluation of facility limits. An extensive deployment of the processing-facility digital twin has been completed for operated assets with the objective of enhancing existing workflows and achieving optimal operations. Design and Implementation Each subsystem was designed to be independently operated. However, the primary strength of this digital twin lies in connecting the well network with processing-facility simulations and real-time sensor data, establishing a linked simulation workflow. Specifically, the pertinent outputs from well-network simulations are captured and incorporated as inputs into the processing-facility simulation. The dynamic communication between these two subsystems is facilitated through a customized interface, resulting in an integrated simulation from the well’s sandface to the export stage. Consequently, comprehensive understanding and holistic study of the effect of current or proposed future operational conditions become feasible.