Synergistic Information Research Articles

Decomposing causality into its synergistic, unique, and redundant components

Causality lies at the heart of scientific inquiry, serving as the fundamental basis for understanding interactions among variables in physical systems. Despite its central role, current methods for causal inference face significant challenges due to nonlinear dependencies, stochastic interactions, self-causation, collider effects, and influences from exogenous factors, among others. While existing methods can effectively address some of these challenges, no single approach has successfully integrated all these aspects. Here, we address these challenges with SURD: Synergistic-Unique-Redundant Decomposition of causality. SURD quantifies causality as the increments of redundant, unique, and synergistic information gained about future events from past observations. The formulation is non-intrusive and applicable to both computational and experimental investigations, even when samples are scarce. We benchmark SURD in scenarios that pose significant challenges for causal inference and demonstrate that it offers a more reliable quantification of causality compared to previous methods.

Competitive interactions shape brain dynamics and computation across species.

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

Direct GPCR-EGFR interaction enables synergistic membrane-to-nucleus information transfer

We addressed the heteromerization of the epidermal growth factor receptor (EGFR) with G-protein coupled receptors (GPCR) on the basis of angiotensin-II-receptor-subtype-1(AT1R)-EGFR interaction as proof-of-concept and show its functional relevance during synergistic nuclear information transfer, beyond ligand-dependent EGFR transactivation. Following in silico modelling, we generated EGFR-interaction deficient AT1R-mutants and compared them to AT1R-wildtype. Receptor interaction was assessed by co-immunoprecipitation (CoIP), Förster resonance energy transfer (FRET) and fluorescence-lifetime imaging microscopy (FLIM). Changes in cell morphology, ERK1/2-phosphorylation (ppERK1/2), serum response factor (SRF)-activation and cFOS protein expression were determined by digital high content microscopy at the single cell level. FRET, FLIM and CoIP confirmed the physical interaction of AT1R-wildtype with EGFR that was strongly reduced for the AT1R-mutants. Responsiveness of cells transfected with AT1R-WT or –mutants to angiotensin II or EGF was similar regarding changes in cell circularity, ppERK1/2 (direct and by ligand-dependent EGFR-transactivation), cFOS-expression and SRF-activity. By contrast, the EGFR-AT1R-synergism regarding these parameters was completely absent for in the interaction-deficient AT1R mutants. The results show that AT1R-EGFR heteromerisation enables AT1R-EGFR-synergism on downstream gene expression regulation, modulating the intensity and the temporal pattern of nuclear AT1R/EGFR-information transfer. Furthermore, remote EGFR transactivation, via ligand release or cytosolic tyrosine kinases, is not sufficient for the complete synergistic control of gene expression.

Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information.

Shannon Information theory has long been a tool of choice to measure empirically how populations of neurons in the brain encode information about cognitive variables. Recently, Partial Information Decomposition (PID) has emerged as principled way to break down this information into components identifying not only the unique information carried by each neuron, but also whether relationships between neurons generate synergistic or redundant information. While it has been long recognized that Shannon information measures on neural activity suffer from a (mostly upward) limited sampling estimation bias, this issue has largely been ignored in the burgeoning field of PID analysis of neural activity. We used simulations to investigate the limited sampling bias of PID computed from discrete probabilities (suited to describe neural spiking activity). We found that PID suffers from a large bias that is uneven across components, with synergy by far the most biased. Using approximate analytical expansions, we found that the bias of synergy increases quadratically with the number of discrete responses of each neuron, whereas the bias of unique and redundant information increase only linearly or sub-linearly. Based on the understanding of the PID bias properties, we developed simple yet effective procedures that correct for the bias effectively, and that improve greatly the PID estimation with respect to current state-of-the-art procedures. We apply these PID bias correction procedures to datasets of 53117 pairs neurons in auditory cortex, posterior parietal cortex and hippocampus of mice performing cognitive tasks, deriving precise estimates and bounds of how synergy and redundancy vary across these brain regions.

Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.

Striking progress has been made in understanding cognition by analyzing how the brain is engaged in different modes of information processing. For instance, so-called synergistic information (information encoded by a set of neurons but not by any subset) plays a key role in areas of the human brain linked with complex cognition. However, two questions remain unanswered: (a) how and why a cognitive system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an information-decomposition framework to investigate neural networks performing cognitive tasks. Our results show that synergy increases as networks learn multiple diverse tasks, and that in tasks requiring integration of multiple sources, performance critically relies on synergistic neurons. Overall, our results suggest that synergy is used to combine information from multiple modalities-and more generally for flexible and efficient learning. These findings reveal new ways of investigating how and why learning systems employ specific information-processing strategies, and support the principle that the capacity for general-purpose learning critically relies on the system's information dynamics.

Asymmetric Dimethylarginine and NT-proBNP Levels Provide SynergisticInformation in PulmonaryArterial Hypertension.

Plasma asymmetric dimethylarginine (ADMA) is elevated in pulmonary arterial hypertension (PAH) and is associated with unfavorable outcomes. The aim of this study was to assess changes in ADMA plasma levels for monitoring disease progression and outcomes during PAH-specific therapy. ADMA was measured at baseline and after at least 6months of follow-up using enzyme-linked immunosorbent assay and high-performance liquid chromatography. Changes in ADMA were analyzed in relation to changes in established PAH markers, including hemodynamic status, N-terminal pro-brain natriuretic peptide (NT-proBNP) and risk assessment scores. Impact on survival was assessed using Kaplan-Meier curves and Cox proportional hazards models. Between 2008 and 2019, ADMA samples were collected prospectively from 215 patients with PAH. Change in ADMA plasma level was a predictor of disease progression and survival. ΔADMA (median-0.03μmol/L; 95%CI:-0.145 to 0.0135) was correlated with change in mean pulmonary arterial pressure (P< 0.005; rS=0.287) but was not significantly correlated with ΔNT-proBNP (P=0.056; rS=0.135). Patients with decreased ADMA plasma levels at follow-up had better 3-year and 5-year survival rates (88% and 80%, respectively, vs 72% and 53% in those without decreases in ADMA) (P< 0.005; pulmonary hypertension-related mortality or lung transplantation). Patients with decreases in both ADMA and NT-proBNP had better survival rates compared with patients in whom only 1 parameter improved (P< 0.005). ΔADMA was a significant predictor of survival in Cox regression analysis and also when corrected for ΔNT-proBNP (HRs: 1.27 and 1.35, respectively; P< 0.005). ADMA and NT-proBNP provide synergistic prognostic information for patients with PAH. ADMA could be used as an objective and distinct biomarker for monitoring treatment response in PAH.

Evolving higher-order synergies reveals a trade-off between stability and information-integration capacity in complex systems.

There has recently been an explosion of interest in how "higher-order" structures emerge in complex systems comprised of many interacting elements (often called "synergistic" information). This "emergent" organization has been found in a variety of natural and artificial systems, although at present, the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems under study. Typical research treats the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyze these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, the average transient length, and the Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a system's dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity) and that certain kinds of complexity naturally balance this trade-off.

Synergistic Information Security Design Implementation based on Role-Based Access Control, Information Classification, and AES Cryptographic Encryption

Security technology has undergone significant development and research in response to increasing cyber threats. The Intranet Document Management System (IDMS) was created to centralize documents within organizations, ensuring efficiency and streamlining processes. Given the critical nature of document management in organizational workflows, secure and safe management is paramount. This study aims to develop a secure IDMS using Advanced Encryption Standard (AES) encryption, Role-Based Access Control (RBAC), and an Information Classification Model. We also provide a comprehensive overview of the key characteristics and performance metrics of each access control model and cryptographic algorithm, facilitating decision-making for system design and implementation. The system offers high granularity, ease of administration through role assignments with document classification, high flexibility with customized permissions, and scalability with roles and classification. AES is chosen for its high security and fast performance, making it a widely used encryption standard. Keywords: Advanced Encryption Standard (AES), Access Control, Cryptographic Algorithm, Document Management System, Information Classification Received Date: April 2, 2024Revised Date: May 10, 2024Accepted Date: May 14, 2024

Distributed representations of prediction error signals across the cortical hierarchy are synergistic

A relevant question concerning inter-areal communication in the cortex is whether these interactions are synergistic. Synergy refers to the complementary effect of multiple brain signals conveying more information than the sum of each isolated signal. Redundancy, on the other hand, refers to the common information shared between brain signals. Here, we dissociated cortical interactions encoding complementary information (synergy) from those sharing common information (redundancy) during prediction error (PE) processing. We analyzed auditory and frontal electrocorticography (ECoG) signals in five common awake marmosets performing two distinct auditory oddball tasks and investigated to what extent event-related potentials (ERP) and broadband (BB) dynamics encoded synergistic and redundant information about PE processing. The information conveyed by ERPs and BB signals was synergistic even at lower stages of the hierarchy in the auditory cortex and between auditory and frontal regions. Using a brain-constrained neural network, we simulated the synergy and redundancy observed in the experimental results and demonstrated that the emergence of synergy between auditory and frontal regions requires the presence of strong, long-distance, feedback, and feedforward connections. These results indicate that distributed representations of PE signals across the cortical hierarchy can be highly synergistic.

Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network.

Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity. We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification. Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling. The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

Changes in high-order interaction measures of synergy and redundancy during non-ordinary states of consciousness induced by meditation, hypnosis, and auto-induced cognitive trance

High-order interactions are required across brain regions to accomplish specific cognitive functions. These functional interdependencies are reflected by synergistic information that can be obtained by combining the information from all the sources considered and redundant information (i.e., common information provided by all the sources). However, electroencephalogram (EEG) functional connectivity is limited to pairwise interactions thereby precluding the estimation of high-order interactions. In this multicentric study, we used measures of synergistic and redundant information to study in parallel the high-order interactions between five EEG electrodes during three non-ordinary states of consciousness (NSCs): Rajyoga meditation (RM), hypnosis, and auto-induced cognitive trance (AICT). We analyzed EEG data from 22 long-term Rajyoga meditators, nine volunteers undergoing hypnosis, and 21 practitioners of AICT. We here report the within-group changes in synergy and redundancy for each NSC in comparison with their respective baseline. During RM, synergy increased at the whole brain level in the delta and theta bands. Redundancy decreased in frontal, right central, and posterior electrodes in delta, and frontal, central, and posterior electrodes in beta1 and beta2 bands. During hypnosis, synergy decreased in mid-frontal, temporal, and mid-centro-parietal electrodes in the delta band. The decrease was also observed in the beta2 band in the left frontal and right parietal electrodes. During AICT, synergy decreased in delta and theta bands in left-frontal, right-frontocentral, and posterior electrodes. The decrease was also observed at the whole brain level in the alpha band. However, redundancy changes during hypnosis and AICT were not significant. The subjective reports of absorption and dissociation during hypnosis and AICT, as well as the mystical experience questionnaires during AICT, showed no correlation with the high-order measures. The proposed study is the first exploratory attempt to utilize the concepts of synergy and redundancy in NSCs. The differences in synergy and redundancy during different NSCs warrant further studies to relate the extracted measures with the phenomenology of the NSCs.

Association of the immediate perioperative dynamics of circulating DNA levels and neutrophil extracellular traps formation in cancer patients.

Elevated circulating DNA (cirDNA) concentrations were found to be associated with trauma or tissue damage which suggests involvement of inflammation or cell death in post-operative cirDNA release. We carried out the first prospective, multicenter study of the dynamics of cirDNA and neutrophil extracellular trap (NETs) markers during the perioperative period from 24 h before surgery up to 72 h after curative surgery in cancer patients. We examined the plasma levels of two NETs protein markers [myeloperoxidase (MPO) and neutrophil elastase (NE)], as well as levels of cirDNA of nuclear (cir-nDNA) and mitochondrial (cir-mtDNA) origin in 29 colon, prostate, and breast cancer patients and in 114 healthy individuals (HI). The synergistic analytical information provided by these markers revealed that: (i) NETs formation contributes to post-surgery conditions; (ii) post-surgery cir-nDNA levels were highly associated with NE and MPO in colon cancer [r=0.60 (P<0.001) and r=0.53 (P<0.01), respectively], but not in prostate and breast cancer; (iii) each tumor type shows a specific pattern of cir-nDNA and NETs marker dynamics, but overall the pre- and post-surgery median values of cir-nDNA, NE, and MPO were significantly higher in cancer patients than in HI. Taken as a whole, our work reveals the association of NETs formation with the elevated cir-nDNA release during a cancer patient's perioperative period, depending on surgical procedure or cancer type. By contrast, cir-mtDNA is poorly associated with NETs formation in the studied perioperative period, which would appear to indicate a different mechanism of release or suggest mitochondrial dysfunction.

Abstract 1022: Integration of cerebrospinal fluid methylome and proteome can obviate the need for biopsy in central nervous system lymphoma

Abstract Background: The diagnosis of intra-axial brain tumors requires histopathological examination of tissue obtained by neurosurgery in current clinical practice, which comes with inherent risks. Some patients, particularly those with primary CNS lymphoma (PCNSL), only undergo surgery to obtain a diagnosis and do not derive any therapeutic benefit from surgical resection or debulking. Less-invasive techniques for diagnosis, such as liquid biopsy, therefore provides an opportunity to mitigate surgical risk in these patients. Methods: Patients with histopathology confirmed glioblastoma (GBM, IDH wild type), brain metastases (BM), and PCNSL with accompanying cerebrospinal fluid (CSF) samples were included in our study. Cell-free DNA methylation profiling and shotgun proteomics were obtained for all patients and used to train classifiers to distinguish each tumour entity from others. Specifically, binomial elastic net regression models were built by combining both data modalities using established early and late integration paradigms and performance was compared to classifiers built from individual data types alone. Each model was repeated 100 fold and performance assessed on an untouched testing subset. Results: Our cohort includes 20 patients with GBM, 17 with BM, and 14 with PCNSL, each with matching CSF cell-free DNA methylation and shotgun proteomic profiling. We show that these data can be integrated to fully discriminate PCNSL from its major diagnostic counterparts with a perfect median AUC of 1.00 (95% CI 1-1) and 100% specificity. Integrated "lymphoma vs other" models significantly outperform models trained on methylation or protein data alone, though the same dramatic improvement was not demonstrated in GBM or BM, suggesting synergistic biological information is specific to lymphoma. Conclusions: There is a critical need to diagnose patients with intra-axial tumours, particularly PCNSL, without relying on invasive and costly surgery. We present the most specific and accurate CNS lymphoma classifier to date by integrating the methylome and proteome of CSF. This has the potential for immediate clinical utility, eliminating the need for biopsy in an important subset of these patients. Citation Format: Alex P. Landry, Jeffrey A. Zuccato, Vikas Patil, Mathew Voisin, Justin Z. Wang, Yosef Ellenbogen, Chloe Gui, Andrew Ajisebutu, Farshad Nassiri, Gelareh Zadeh. Integration of cerebrospinal fluid methylome and proteome can obviate the need for biopsy in central nervous system lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1022.

Generalized decomposition of multivariate information.

Since its introduction, the partial information decomposition (PID) has emerged as a powerful, information-theoretic technique useful for studying the structure of (potentially higher-order) interactions in complex systems. Despite its utility, the applicability of the PID is restricted by the need to assign elements as either "sources" or "targets", as well as the specific structure of the mutual information itself. Here, I introduce a generalized information decomposition that relaxes the source/target distinction while still satisfying the basic intuitions about information. This approach is based on the decomposition of the Kullback-Leibler divergence, and consequently allows for the analysis of any information gained when updating from an arbitrary prior to an arbitrary posterior. As a result, any information-theoretic measure that can be written as a linear combination of Kullback-Leibler divergences admits a decomposition in the style of Williams and Beer, including the total correlation, the negentropy, and the mutual information as special cases. This paper explores how the generalized information decomposition can reveal novel insights into existing measures, as well as the nature of higher-order synergies. We show that synergistic information is intimately related to the well-known Tononi-Sporns-Edelman (TSE) complexity, and that synergistic information requires a similar integration/segregation balance as a high TSE complexity. Finally, I end with a discussion of how this approach fits into other attempts to generalize the PID and the possibilities for empirical applications.

Characterization of Demographical Histologic Diversity in Small Renal Masses With the Clear Cell Likelihood Score.

This study aimed to develop a diagnostic model to estimate the distribution of small renal mass (SRM; ≤4 cm) histologic subtypes for patients with different demographic backgrounds and clear cell likelihood score (ccLS) designations. A bi-institution retrospective cohort study was conducted where 347 patients (366 SRMs) underwent magnetic resonance imaging and received a ccLS before pathologic confirmation between June 2016 and November 2021. Age, sex, race, ethnicity, socioeconomic status, body mass index (BMI), and the ccLS were tabulated. The socioeconomic status for each patient was determined using the Area Deprivation Index associated with their residential address. The magnetic resonance imaging-derived ccLS assists in the characterization of SRMs by providing a likelihood of clear cell renal cell carcinoma (ccRCC). Pathological subtypes were grouped into four categories (ccRCC, papillary renal cell carcinoma, other renal cell carcinomas, or benign). Generalized estimating equations were used to estimate probabilities of the pathological subtypes across different patient subgroups. Race and ethnicity, BMI, and ccLS were significant predictors of histology (all P < 0.001). Obese (BMI, ≥30 kg/m 2 ) Hispanic patients with ccLS of ≥4 had the highest estimated rate of ccRCC (97.1%), and normal-weight (BMI, <25 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 had the lowest (0.2%). The highest estimated rates of papillary renal cell carcinoma were found in overweight (BMI, 25-30 kg/m 2 ) non-Hispanic Black patients with ccLS ≤2 (92.3%), and the lowest, in obese Hispanic patients with ccLS ≥4 (<0.1%). Patient race, ethnicity, BMI, and ccLS offer synergistic information to estimate the probabilities of SRM histologic subtypes.

Multiplexing Autoradiography.

Autoradiography, the direct imaging of radioactive distribution in tissue sections, is a powerful technique that has several key advantages for the validation of PET radiotracers. Using autoradiography, we can localize radiotracer uptake to neighbours of cells, and when multiplexed with additional radiotracers, fluorescent probes, or in situ tissue analysis, autoradiography can help to characterize the mechanism of radiotracer uptake and assess functional heterogeneity in tissue. In this chapter, the author outlines the basic ex vivo autoradiography protocol and shows how it can be multiplexed using dual radionuclides 18F and 14C. They also highlight where autoradiography can be combined with other technologies to provide synergistic information for interrogating spatial biology.

Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex

Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.

Automatic Head and Neck Tumor segmentation and outcome prediction relying on FDG-PET/CT images: Findings from the second edition of the HECKTOR challenge.

By focusing on metabolic and morphological tissue properties respectively, FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) modalities include complementary and synergistic information for cancerous lesion delineation and characterization (e.g. for outcome prediction), in addition to usual clinical variables. This is especially true in Head and Neck Cancer (HNC). The goal of the HEad and neCK TumOR segmentation and outcome prediction (HECKTOR) challenge was to develop and compare modern image analysis methods to best extract and leverage this information automatically. We present here the post-analysis of HECKTOR 2nd edition, at the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021. The scope of the challenge was substantially expanded compared to the first edition, by providing a larger population (adding patients from a new clinical center) and proposing an additional task to the challengers, namely the prediction of Progression-Free Survival (PFS). To this end, the participants were given access to a training set of 224 cases from 5 different centers, each with a pre-treatment FDG-PET/CT scan and clinical variables. Their methods were subsequently evaluated on a held-out test set of 101 cases from two centers. For the segmentation task (Task 1), the ranking was based on a Borda counting of their ranks according to two metrics: mean Dice Similarity Coefficient (DSC) and median Hausdorff Distance at 95th percentile (HD95). For the PFS prediction task, challengers could use the tumor contours provided by experts (Task 3) or rely on their own (Task 2). The ranking was obtained according to the Concordance index (C-index) calculated on the predicted risk scores. A total of 103 teams registered for the challenge, for a total of 448 submissions and 29 papers. The best method in the segmentation task obtained an average DSC of 0.759, and the best predictions of PFS obtained a C-index of 0.717 (without relying on the provided contours) and 0.698 (using the expert contours). An interesting finding was that best PFS predictions were reached by relying on DL approaches (with or without explicit tumor segmentation, 4 out of the 5 best ranked) compared to standard radiomics methods using handcrafted features extracted from delineated tumors, and by exploiting alternative tumor contours (automated and/or larger volumes encompassing surrounding tissues) rather than relying on the expert contours. This second edition of the challenge confirmed the promising performance of fully automated primary tumor delineation in PET/CT images of HNC patients, although there is still a margin for improvement in some difficult cases. For the first time, the prediction of outcome was also addressed and the best methods reached relatively good performance (C-index above 0.7). Both results constitute another step forward toward large-scale outcome prediction studies in HNC.

Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton

In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.