Epigenomic dissection of Alzheimer's disease pinpoints causal variants and reveals epigenome erosion.

Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.

Caresses, whispers and affective faces: A theoretical framework for a multimodal interoceptive mechanism underlying ASMR and affective touch: An evolutionary and developmental perspective for understanding ASMR and affective touch as complementary processes within affiliative interactions.

Autonomous sensory meridian response (ASMR) and affective touch (AT) are two phenomena that have been independently investigated from separate lines of research. In this article, I provide a unified theoretical framework for understanding and studying them as complementary processes. I highlight their shared biological basis and positive effects on emotional and psychophysiological regulation. Drawing from evolutionary and developmental theories, I propose that ASMR results from the development of biological mechanisms associated with early affiliative behaviour and self-regulation, similar to AT. I also propose a multimodal interoceptive mechanism underlying both phenomena, suggesting that different sensory systems could specifically respond to affective stimulation (caresses, whispers and affective faces), where the integration of those inputs occurs in the brain's interoceptive hubs, allowing physiological regulation. The implications of this proposal are discussed with a view to future research that jointly examines ASMR and AT, and their potential impact on improving emotional well-being and mental health.

Bioinspired In-Sensor Multimodal Fusion for Enhanced Spatial and Spatiotemporal Association.

Multimodal perception can capture more precise and comprehensive information compared with unimodal approaches. However, current sensory systems typically merge multimodal signals at computing terminals following parallel processing and transmission, which results in the potential loss of spatial association information and requires time stamps to maintain temporal coherence for time-series data. Here we demonstrate bioinspired in-sensor multimodal fusion, which effectively enhances comprehensive perception and reduces the level of data transfer between sensory terminal and computation units. By adopting floating gate phototransistors with reconfigurable photoresponse plasticity, we realize the agile spatial and spatiotemporal fusion under nonvolatile and volatile photoresponse modes. To realize an optimal spatial estimation, we integrate spatial information from visual-tactile signals. For dynamic events, we capture and fuse in real time spatiotemporal information from visual-audio signals, realizing a dance-music synchronization recognition task without a time-stamping process. This in-sensor multimodal fusion approach provides the potential to simplify the multimodal integration system, extending the in-sensor computing paradigm.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology.

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

The processing of proprioceptive signals in distributed networks: insights from insect motor control.

The integration of sensory information is required to maintain body posture and to generate robust yet flexible locomotion through unpredictable environments. To anticipate required adaptations in limb posture and enable compensation of sudden perturbations, an animal's nervous system assembles external (exteroception) and internal (proprioception) cues. Coherent neuronal representations of the proprioceptive context of the body and the appendages arise from the concerted action of multiple sense organs monitoring body kinetics and kinematics. This multimodal proprioceptive information, together with exteroceptive signals and brain-derived descending motor commands, converges onto premotor networks - i.e. the local neuronal circuitry controlling motor output and movements - within the ventral nerve cord (VNC), the insect equivalent of the vertebrate spinal cord. This Review summarizes existing knowledge and recent advances in understanding how local premotor networks in the VNC use convergent information to generate contextually appropriate activity, focusing on the example of posture control. We compare the role and advantages of distributed sensory processing over dedicated neuronal pathways, and the challenges of multimodal integration in distributed networks. We discuss how the gain of distributed networks may be tuned to enable the behavioral repertoire of these systems, and argue that insect premotor networks might compensate for their limited neuronal population size by, in comparison to vertebrate networks, relying more heavily on the specificity of their connections. At a time in which connectomics and physiological recording techniques enable anatomical and functional circuit dissection at an unprecedented resolution, insect motor systems offer unique opportunities to identify the mechanisms underlying multimodal integration for flexible motor control.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma.

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

More Than the Sum of Its Parts: Visual-Tactile Integration in the Behaving Rat.

We experience the world by constantly integrating cues from multiple modalities to form unified sensory percepts. Once familiar with multimodal properties of an object, we can recognize it regardless of the modality involved. In this chapter we will examine the case of a visual-tactile orientation categorization experiment in rats. We will explore the involvement of the cerebral cortex in recognizing objects through multiple sensory modalities. In the orientation categorization task, rats learned to examine and judge the orientation of a raised, black and white grating using touch, vision, or both. Their multisensory performance was better than the predictions of linear models for cue combination, indicating synergy between the two sensory channels. Neural recordings made from a candidate associative cortical area, the posterior parietal cortex (PPC), reflected the principal neuronal correlates of the behavioral results: PPC neurons encoded both graded information about the object and categorical information about the animal's decision. Intriguingly single neurons showed identical responses under each of the three modality conditions providing a substrate for a neural circuit in the cortex that is involved in modality-invariant processing of objects.

Assessment of brain cancer atlas maps with multimodal imaging features.

BACKGROUND: Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. MAIN TEXT: Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. CONCLUSIONS: The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy.

Brown goat yogurt: Metabolomics, peptidomics, and sensory changes during production.

Brown goat milk products have gained popularity for their unique taste and flavor. The emergence of chain-reversal phenomenon makes the design and development of goat milk products gradually tend to a consumer-oriented model. However, the precise mechanism of how browning and fermentation process causes characteristics is not clear. In an effort to understand how the treatments potentially lead to certain metabolite profile changes in goat milk, comprehensive, quantitative metabolomics and peptidomics analysis of goat milk samples after browning and fermentation were undertaken. An intelligent hybrid z-score standardization-principal components algorithm-multimodal denoizing autoencoder was used for feature fusion and hidden layer fusion in high-dimensional variable space. The fermentation process significantly improved the flavor of brown goat yogurt through the tricarboxylic acid-urea-glycolysis composite pathway. Bitter peptides HPFLEWAR, PPGLPDKY, and PPPPPKK have strong interactions with both putative dipeptidyl peptidase IV and angiotensin-converting enzyme, proving that brown goat yogurt can be considered as effective provider of potential putative dipeptidyl peptidase IV and angiotensin-converting enzyme inhibitors. The level of health-promoting bioactive components and sensory contributed to consumer selection. The proposed multimodal data integrative analysis platform was applicable to explain the effect of the dynamic changes of metabolites and peptides on consumer preferences.

Late combination shows that MEG adds to MRI in classifying MCI versus controls.

Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.

Honey bees respond to multimodal stimuli following the principle of inverse effectiveness.

Multisensory integration is assumed to entail benefits for receivers across multiple ecological contexts. However, signal integration effectiveness is constrained by features of the spatiotemporal and intensity domains. How sensory modalities are integrated during tasks facilitated by learning and memory, such as pollination, remains unsolved. Honey bees use olfactory and visual cues during foraging, making them a good model to study the use of multimodal signals. Here, we examined the effect of stimulus intensity on both learning and memory performance of bees trained using unimodal or bimodal stimuli. We measured the performance and the latency response across planned discrete levels of stimulus intensity. We employed the conditioning of the proboscis extension response protocol in honey bees using an electromechanical setup allowing us to control simultaneously and precisely olfactory and visual stimuli at different intensities. Our results show that the bimodal enhancement during learning and memory was higher as the intensity decreased when the separate individual components were least effective. Still, this effect was not detectable for the latency of response. Remarkably, these results support the principle of inverse effectiveness, traditionally studied in vertebrates, predicting that multisensory stimuli are more effectively integrated when the best unisensory response is relatively weak. Thus, we argue that the performance of the bees while using a bimodal stimulus depends on the interaction and intensity of its individual components. We further hold that the inclusion of findings across all levels of analysis enriches the traditional understanding of the mechanics and reliance of complex signals in honey bees.

Small-amplitude head oscillations result from a multimodal head stabilization reflex in hawkmoths.

In flying insects, head stabilization is an essential reflex that helps to reduce motion blur during fast aerial manoeuvres. This reflex is multimodal and requires the integration of visual and antennal mechanosensory feedback in hawkmoths, each operating as a negative-feedback-control loop. As in any negative-feedback system, the head stabilization system possesses inherent oscillatory dynamics that depend on the rate at which the sensorimotor components of the reflex operate. Consistent with this expectation, we observed small-amplitude oscillations in the head motion (or head wobble) of the oleander hawkmoth, Daphnis nerii, which are accentuated when sensory feedback is aberrant. Here, we show that these oscillations emerge from the inherent dynamics of the multimodal reflex underlying gaze stabilization, and that the amplitude of head wobble is a function of both the visual feedback and antennal mechanosensory feedback from the Johnston's organs. Our data support the hypothesis that head wobble results from a multimodal, dynamically stabilized reflex loop that mediates head positioning.

Tingles down the spinal cord: A spinal functional magnetic resonance imaging investigation of the autonomous sensory meridian response.

Autonomous sensory meridian response (ASMR) is a perceptual and emotional phenomenon in which specific sensory stimuli elicit a feeling of calm as well as tingling sensations on the scalp, neck, and shoulders. In the current study, we use fMRI to examine whether the motoric and sensory regions of the spinal cord segments associated with these body parts show increased activity during ASMR experiences. Nine individuals with ASMR completed six spinal functional magnetic resonance imaging runs while passively viewing videos. Three of the videos were shown (through pre-testing) to elicit ASMR tingles and three videos did not (i.e., control videos). The results demonstrated that ASMR-related stimuli elicited activity in dorsal (sensory) regions of spinal cord segments C1, C5, and C6; activity was observed in ventral (motoric) regions of segments C2-C8. Similar activity was not detected in response to control videos.

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.

Temporal differences between load and movement signal integration in the sensorimotor network of an insect leg.

Nervous systems face a torrent of sensory inputs, including proprioceptive feedback. Signal integration depends on spatially and temporally coinciding signals. It is unclear how relative time delays affect multimodal signal integration from spatially distant sense organs. We measured transmission times and latencies along all processing stages of sensorimotor pathways in the stick insect leg muscle control system, using intra- and extracellular recordings. Transmission times of signals from load-sensing tibial and trochanterofemoral campaniform sensilla (tiCS, tr/fCS) to the premotor network were longer than from the movement-sensing femoral chordotonal organ (fCO). We characterized connectivity patterns from tiCS, tr/fCS, and fCO afferents to identified premotor nonspiking interneurons (NSIs) and motor neurons (MNs) by distinguishing short- and long-latency responses to sensory stimuli. Functional NSI connectivity depended on sensory context. The timeline of multisensory integration in the NSI network showed an early phase of movement signal processing and a delayed phase of load signal integration. The temporal delay of load signals relative to movement feedback persisted into MN activity and muscle force development. We demonstrate differential delays in the processing of two distinct sensory modalities generated by the sensorimotor network and affecting motor output. The reported temporal differences in sensory processing and signal integration improve our understanding of sensory network computation and function in motor control.NEW & NOTEWORTHY Networks integrating multisensory input face the challenge of not only spatial but also temporal integration. In the local network controlling insect leg movements, proprioceptive signal delays differ between sensory modalities. Specifically, signal transmission times to and neuronal connectivity within the sensorimotor network lead to delayed information about leg loading relative to movement signals. Temporal delays persist up to the level of the motor output, demonstrating its relevance for motor control.

Distributed processing of load and movement feedback in the premotor network controlling an insect leg joint.

In legged animals, integration of information from various proprioceptors in and on the appendages by local premotor networks in the central nervous system is crucial for controlling motor output. To ensure posture maintenance and precise active movements, information about limb loading and movement is required. In insects, various groups of campaniform sensilla (CS) measure forces and loads acting in different directions on the leg, and the femoral chordotonal organ (fCO) provides information about movement of the femur-tibia (FTi) joint. In this study, we used extra- and intracellular recordings of extensor tibiae (ExtTi) and retractor coxae (RetCx) motor neurons (MNs) and identified local premotor nonspiking interneurons (NSIs) and mechanical stimulation of the fCO and tibial or trochanterofemoral CS (tiCS, tr/fCS), to investigate the premotor network architecture underlying multimodal proprioceptive integration. We found that load feedback from tiCS altered the strength of movement-elicited resistance reflexes and determined the specificity of ExtTi and RetCx MN responses to various load and movement stimuli. These responses were mediated by a common population of identified NSIs into which synaptic inputs from the fCO, tiCS, and tr/fCS are distributed, and whose effects onto ExtTi MNs can be antagonistic for both stimulus modalities. Multimodal sensory signal interaction was found at the level of single NSIs and MNs. The results provide evidence that load and movement feedback are integrated in a multimodal, distributed local premotor network consisting of antagonistic elements controlling movements of the FTi joint, thus substantially extending current knowledge on how legged motor systems achieve fine-tuned motor control.NEW & NOTEWORTHY Proprioception is crucial for motor control in legged animals. We show the extent to which processing of movement (fCO) and load (CS) signals overlaps in the local premotor network of an insect leg. Multimodal signals converge onto the same set of interneurons, and our knowledge about distributed, antagonistic processing is extended to incorporate multiple modalities within one perceptual neuronal framework.

Rapid invisible frequency tagging reveals nonlinear integration of auditory and visual information.

During communication in real-life settings, the brain integrates information from auditory and visual modalities to form a unified percept of our environment. In the current magnetoencephalography (MEG) study, we used rapid invisible frequency tagging (RIFT) to generate steady-state evoked fields and investigated the integration of audiovisual information in a semantic context. We presented participants with videos of an actress uttering action verbs (auditory; tagged at 61 Hz) accompanied by a gesture (visual; tagged at 68 Hz, using a projector with a 1,440 Hz refresh rate). Integration difficulty was manipulated by lower-order auditory factors (clear/degraded speech) and higher-order visual factors (congruent/incongruent gesture). We identified MEG spectral peaks at the individual (61/68 Hz) tagging frequencies. We furthermore observed a peak at the intermodulation frequency of the auditory and visually tagged signals (fvisual - fauditory = 7 Hz), specifically when lower-order integration was easiest because signal quality was optimal. This intermodulation peak is a signature of nonlinear audiovisual integration, and was strongest in left inferior frontal gyrus and left temporal regions; areas known to be involved in speech-gesture integration. The enhanced power at the intermodulation frequency thus reflects the ease of lower-order audiovisual integration and demonstrates that speech-gesture information interacts in higher-order language areas. Furthermore, we provide a proof-of-principle of the use of RIFT to study the integration of audiovisual stimuli, in relation to, for instance, semantic context.

Representation of Haltere Oscillations and Integration with Visual Inputs in the Fly Central Complex.

The reduced hindwings of flies, known as halteres, are specialized mechanosensory organs that detect body rotations during flight. Primary afferents of the haltere encode its oscillation frequency linearly over a wide bandwidth and with precise phase-dependent spiking. However, it is not currently known whether information from haltere primary afferent neurons is sent to higher brain centers where sensory information about body position could be used in decision making, or whether precise spike timing is useful beyond the peripheral circuits that drive wing movements. We show that in cells in the central brain, the timing and rates of neural spiking can be modulated by sensory input from experimental haltere movements (driven by a servomotor). Using multichannel extracellular recording in restrained flesh flies (Sarcophaga bullata of both sexes), we examined responses of central complex cells to a range of haltere oscillation frequencies alone, and in combination with visual motion speeds and directions. Haltere-responsive units fell into multiple response classes, including those responding to any haltere motion and others with firing rates linearly related to the haltere frequency. Cells with multisensory responses showed higher firing rates than the sum of the unisensory responses at higher haltere frequencies. They also maintained visual properties, such as directional selectivity, while increasing response gain nonlinearly with haltere frequency. Although haltere inputs have been described extensively in the context of rapid locomotion control, we find haltere sensory information in a brain region known to be involved in slower, higher-order behaviors, such as navigation.SIGNIFICANCE STATEMENT Many animals use vision for navigation; however, these cues must be interpreted in the context of the body's position. In mammalian brains, hippocampal cells combine visual and vestibular information to encode head direction. A region of the arthropod brain, known as the central complex (CX), similarly encodes heading information, but it is unknown whether proprioceptive information is integrated here as well. We show that CX neurons respond to input from halteres, specialized proprioceptors in flies that detect body rotations. These neurons also respond to visual input, providing one of the few examples of multiple sensory modalities represented in individual CX cells. Haltere stimulation modifies neural responses to visual signals, providing a mechanism for integrating vision with proprioception.

Multimodal Integration and Vividness in the Angular Gyrus During Episodic Encoding and Retrieval.

Much evidence suggests that the angular gyrus (AnG) is involved in episodic memory, but its precise role has yet to be determined. We examined two possible accounts within the same experimental paradigm: the "cortical binding of relational activity" (CoBRA) account (Shimamura, 2011), which suggests that the AnG acts as a convergence zone that binds multimodal episodic features, and the subjectivity account (Yazar et al., 2012), which implicates AnG involvement in subjective mnemonic experience (such as vividness or confidence). fMRI was used during both encoding and retrieval of paired associates. During study, female and male human participants memorized picture-pairs of common objects (in the unimodal task) or of an object-picture and an environmental sound (in the crossmodal task). At test, they performed a cued-recall task and further indicated the vividness of their memory. During retrieval, BOLD activation in the AnG was greatest for vividly remembered associates, consistent with the subjectivity account. During encoding, the same effect of vividness was found, but this was further modulated by task: greater activations were associated with subsequent recall in the crossmodal than the unimodal task. Therefore, encoding data suggest an additional role to the AnG in crossmodal integration, consistent with its role at retrieval proposed by CoBRA. These results resolve some of the puzzles in the literature and indicate that the AnG can play different roles during encoding and retrieval as determined by the cognitive demands posed by different mnemonic tasks.SIGNIFICANCE STATEMENT We offer new insights into the multiplicity of processes that are associated with angular gyrus (AnG) activation during encoding and retrieval of newly formed memories. We used fMRI while human participants learned and subsequently recalled pairs of objects presented to the same sensory modality or to different modalities. We were able to show that the AnG is involved when vivid memories are created and retrieved, as well as when encoded information is integrated across different sensory modalities. These findings provide novel evidence for the contribution of the AnG to our subjective experience of remembering alongside its role in integrative processes that promote subsequent memory.

High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility.

Primate cortical evolution has been characterized by massive and disproportionate expansion of a set of specific regions in the neocortex. The associated increase in neocortical neurons comes with a high metabolic cost, thus the functions served by these regions must have conferred significant evolutionary advantage. In the present series of analyses, we show that evolutionary high-expanding cortex - as estimated from patterns of surface growth from several primate species - shares functional connections with different brain networks in a context-dependent manner. Specifically, we demonstrate that high-expanding cortex is characterized by high internetwork functional connectivity; is recruited flexibly over many different cognitive tasks; and changes its functional coupling pattern between rest and a multimodal task-state. The capacity of high-expanding cortex to connect flexibly with various specialized brain networks depending on particular cognitive requirements suggests that its selective growth and sustainment in evolution may have been linked to an involvement in supramodal cognition. In accordance with an evolutionary-developmental view, we find that this observed ability of high-expanding regions - to flexibly modulate functional connections as a function of cognitive state - emerges gradually through childhood, with a prolonged developmental trajectory plateauing in young adulthood.