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Hippocampal ripples index the reconstruction of spatiotemporal neuronal firing patterns essential for the consolidation of memories in the cortex during non-rapid eye movement sleep (NREM). Recently, cortical ripples in humans have been shown to enfold the replay of neuron firing patterns during cued recall. Here, using intracranial recordings from 18 patients (12 female), we show that cortical ripples also occur during NREM in humans, with similar density, oscillation frequency (∼90 Hz), duration, and amplitude to waking. Ripples occurred in all cortical regions with similar characteristics, unrelated to putative hippocampal connectivity, and were less dense and robust in higher association areas. Putative pyramidal and interneuron spiking phase-locked to cortical ripples during NREM, with phase delays consistent with ripple generation through pyramidal-interneuron feedback. Cortical ripples were smaller in amplitude than hippocampal ripples, but were similar in density, frequency, and duration. Cortical ripples during NREM typically occurred just prior to the upstate peak, often during spindles. Upstates and spindles have previously been associated with memory consolidation, and we found that cortical ripples grouped co-firing between units within the window of spike-timing-dependent plasticity. Thus, human NREM cortical ripples are: ubiquitous and stereotyped with a tightly focused oscillation frequency; similar to hippocampal ripples; associated with upstates and spindles; and associated with unit co-firing. These properties are consistent with cortical ripples possibly contributing to memory consolidation and other functions during NREM in humans.Significance StatementIn rodents, hippocampal ripples organize replay during sleep to promote memory consolidation in the cortex, where ripples also occur. However, evidence for cortical ripples in human sleep is limited, and their anatomical distribution and physiological properties are unexplored. Here, using human intracranial recordings, we demonstrate that ripples occur throughout the cortex during waking and sleep with highly stereotyped characteristics. During sleep, cortical ripples tend to occur during spindles on the down-to-upstate transition, and thus participate in a sequence of sleep waves that is important for consolidation. Furthermore, cortical ripples organize single unit spiking with timing optimal to facilitate plasticity. Therefore, cortical ripples in humans possess essential physiological properties to support memory and other cognitive functions.

Synchronous bursts of high frequency oscillations (‘ripples’) are hypothesized to contribute to binding by facilitating integration of neuronal firing across cortical locations. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each-other’s firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during NREM sleep and waking, in temporal and Rolandic cortices, and at distances up to 16mm. Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, and were strongly modulated by ripple phase. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and enhanced when multiple sites co-ripple. Together, these results support the hypothesis that transcortical co-ripples increase the integration of neuronal firing of neurons in different cortical locations, and do so in part through phase-modulation.

The co-occurrence of brief ~90 Hz oscillations (co-ripples) may be important in integrating information across the cortex and hippocampus, essential for sleep consolidation, and cognition in general. However, how such co-ripples are synchronized is unknown. We tested if cortico-cortical and hippocampal-cortical ripple co-occurrences are due to the simultaneous direct propagation of thalamic ripples, or if they are instead facilitated by lower frequency waves. Using human intracranial recordings, we found that ripples are generated in the thalamus during nonrapid eye movement sleep with similar characteristics as cortical and hippocampal ripples. However, thalamic ripples only infrequently and weakly co-occur, and never phase-lock, with cortical and hippocampal ripples. In contrast, thalamo-cortical spindles and upstates strongly facilitated cortico-cortical and hippocampo-cortical co-rippling. Thus, while thalamic ripples may not directly drive multiple cortical or hippocampal sites at ripple frequency, these sites may ripple synchronously in response to widespread activation from thalamo-cortical spindles and upstates.

Declarative memory encoding, consolidation, and retrieval require the integration of elements encoded in widespread cortical locations. The mechanism whereby such ‘binding’ of different components of mental events into unified representations occurs is unknown. The ‘binding-bysynchrony’ theory proposes that distributed encoding areas are bound by synchronous oscillations enabling enhanced communication. However, evidence for such oscillations is sparse. Brief high-frequency oscillations (‘ripples’) occur in the hippocampus and cortex, and help organize memory recall and consolidation. Here, using intracranial recordings in humans, we report that these ~70ms duration 90Hz ripples often couple (within ±500ms), co-occur (≥25ms overlap), and crucially, phase-lock (have consistent phase-lags) between widely distributed focal cortical locations during both sleep and waking, even between hemispheres. Cortical ripple co-occurrence is facilitated through activation across multiple sites, and phaselocking increases with more cortical sites co-rippling. Ripples in all cortical areas co-occur with hippocampal ripples but do not phase-lock with them, further suggesting that cortico-cortical synchrony is mediated by cortico-cortical connections. Ripple phase-lags vary across sleep nights, consistent with participation in different networks. During waking, we show that hippocampo-cortical and cortico-cortical co-ripples increase preceding successful delayed memory recall, when binding between the cue and response is essential. Ripples increase and phase-modulate unit firing, and co-ripples increase high-frequency correlations between areas, suggesting synchronized unit-spiking facilitating information exchange. Co-occurrence, phasesynchrony, and high-frequency correlation are maintained with little decrement over very long distances (25cm). Hippocampo-cortico-cortical co-ripples appear to possess the essential properties necessary to support binding-by-synchrony during memory retrieval, and perhaps generally in cognition.Significance StatementDifferent elements of a memory, or any mental event, are encoded in locations distributed across the cortex. A prominent hypothesis proposes that widespread networks are integrated with bursts of synchronized high-frequency oscillations called ‘ripples,’ but evidence is limited. Here, using recordings inside the human brain, we show that ripples occur simultaneously in multiple lobes in both cortical hemispheres, and the hippocampus, generally during sleep and waking, and especially during memory recall. Ripples phase-lock local cell firing, and phase-synchronize with little decay between locations separated by up to 25cm, enabling long-distance integration. Indeed, co-rippling sites have increased correlation of very high-frequency activity which reflects cell firing. Thus, ripples may help bind information across the cortex in memory and other mental events.

Purpose: Myofiber strain, Eff , is a mechanistically relevant metric of cardiac cell shortening and is expected to be spatially uniform in healthy populations, making it a prime candidate for the evaluation of local cardiomyocyte contractility. In this study, a new, efficient pipeline was proposed to combine microstructural cDTI and functional DENSE data in order to estimate Eff in vivo.Methods: Thirty healthy volunteers were scanned with three long-axis (LA) and three short-axis (SA) DENSE slices using 2D displacement encoding and one SA slice of cDTI. The total acquisition time was 11 minutes ± 3 minutes across volunteers. The pipeline first generates 3D SA displacements from all DENSE slices which are then combined with cDTI data to generate a cine of myofiber orientations and compute Eff . The precision of the post-processing pipeline was assessed using a computational phantom study. Transmural myofiber strain was compared to circumferential strain, Ecc , in healthy volunteers using a Wilcoxon sign rank test.Results: In vivo, computed Eff was found uniform transmurally compared to Ecc (-0.14[-0.15, -0.12] vs -0.18 [-0.20, -0.16], P < .001, -0.14 [-0.16, -0.12] vs -0.16 [-0.17, -0.13], P < .001 and -0.14 [-0.16, -0.12] vs Ecc_C = -0.14 [-0.15, -0.11], P = .002, Eff_C vs Ecc_C in the endo, mid, and epi layers, respectively).Conclusion: We demonstrate that it is possible to measure in vivo myofiber strain in a healthy human population in 10 minutes per subject. Myofiber strain was observed to be spatially uniform in healthy volunteers making it a potential biomarker for the evaluation of local cardiomyocyte contractility in assessing cardiovascular dysfunction.

Purpose Cardiomyocyte organization and performance underlie cardiac function, but the in vivo mobility of these cells during contraction and filling remains difficult to probe. Herein, a novel trigger delay (TD) scout sequence was used to acquire high in-plane resolution (1.6 mm) Spin-Echo (SE) cardiac diffusion tensor imaging (cDTI) at three distinct cardiac phases. The objective was to characterize cardiomyocyte organization and mobility throughout the cardiac cycle in healthy volunteers. Materials and methods Nine healthy volunteers were imaged with cDTI at three distinct cardiac phases (early systole, late systole, and diastasis). The sequence used a free-breathing Spin-Echo (SE) cDTI protocol (b-values = 350s/mm2, twelve diffusion encoding directions, eight repetitions) to acquire high-resolution images (1.6x1.6x8mm3) at 3T in ~7 minutes/cardiac phase. Helix Angle (HA), Helix Angle Range (HAR), E2 angle (E2A), Transverse Angle (TA), Mean Diffusivity (MD), diffusion tensor eigenvalues (λ1-2-3), and Fractional Anisotropy (FA) in the left ventricle (LV) were characterized. Results Images from the patient-specific TD scout sequence demonstrated that SE cDTI acquisition was possible at early systole, late systole, and diastasis in 78%, 100% and 67% of the cases, respectively. At the mid-ventricular level, mobility (reported as median [IQR]) was observed in HAR between early systole and late systole (76.9 [72.6, 80.5]° vs 96.6 [85.9, 100.3]°, p Conclusion We demonstrate that it is possible to probe cardiomyocyte mobility using multi-phase and high resolution cDTI. In healthy volunteers, aggregate cardiomyocytes re-orient themselves more longitudinally during contraction, while cardiomyocyte sheetlets tilt radially during wall thickening. These observations provide new insights into the three-dimensional mobility of myocardial microstructure during systolic contraction.

Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (0.93 ± 0.01), right ventricle (RV) cavity (0.80 ± 0.05), and the myocardium (0.72 ± 0.04). The R(2) coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.

Changes in left ventricular (LV) aggregate cardiomyocyte orientation and deformation underlie cardiac function and dysfunction. As such, in vivo aggregate cardiomyocyte “myofiber” strain ( ${E}_{\text {ff}}$ ) has mechanistic significance, but currently there exists no established technique to measure in vivo ${E}_{\text {ff}}$ . The objective of this work is to describe and validate a pipeline to compute in vivo ${E}_{\text {ff}}$ from magnetic resonance imaging (MRI) data. Our pipeline integrates LV motion from multi-slice Displacement ENcoding with Stimulated Echoes (DENSE) MRI with in vivo LV microstructure from cardiac Diffusion Tensor Imaging (cDTI) data. The proposed pipeline is validated using an analytical deforming heart-like phantom. The phantom is used to evaluate 3D cardiac strains computed from a widely available, open-source DENSE Image Analysis Tool. Phantom evaluation showed that a DENSE MRI signal-to-noise ratio (SNR) ≥20 is required to compute ${E}_{\text {ff}}$ with near-zero median strain bias and within a strain tolerance of 0.06. Circumferential and longitudinal strains are also accurately measured under the same SNR requirements, however, radial strain exhibits a median epicardial bias of −0.10 even in noise-free DENSE data. The validated framework is applied to experimental DENSE MRI and cDTI data acquired in eight ( ${N}={8}$ ) healthy swine. The experimental study demonstrated that ${E}_{\text {ff}}$ has decreased transmural variability compared to radial and circumferential strains. The spatial uniformity and mechanistic significance of in vivo ${E}_{\text {ff}}$ make it a compelling candidate for characterization and early detection of cardiac dysfunction.

Since heart contraction results from the electrically activated contraction of millions of cardiomyocytes, a measure of cardiomyocyte shortening mechanistically underlies cardiac contraction. In this work we aim to measure preferential aggregate cardiomyocyte (“myofiber”) strains based on Magnetic Resonance Imaging (MRI) data acquired to measure both voxel-wise displacements through systole and myofiber orientation. In order to reduce the effect of experimental noise on the computed myofiber strains, we recast the strains calculation as the solution of a boundary value problem (BVP). This approach does not require a calibrated material model, and consequently is independent of specific myocardial material properties. The solution to this auxiliary BVP is the displacement field corresponding to assigned values of myofiber strains. The actual myofiber strains are then determined by minimizing the difference between computed and measured displacements. The approach is validated using an analytical phantom, for which the ground-truth solution is known. The method is applied to compute myofiber strains using in vivo displacement and myofiber MRI data acquired in a mid-ventricular left ventricle section in N=8 swine subjects. The proposed method shows a more physiological distribution of myofiber strains compared to standard approaches that directly differentiate the displacement field.

Computational models of cardiac contraction can provide critical insight into cardiac function and dysfunction. A necessary step before employing these computational models is their validation. Here we propose a series of validation criteria based on left ventricular (LV) global (ejection fraction and twist) and local (strains in a cylindrical coordinate system, aggregate cardiomyocyte shortening, and low myocardial compressibility) MRI measures to characterize LV motion and deformation during contraction. These validation criteria are used to evaluate an LV finite element model built from subject-specific anatomy and aggregate cardiomyocyte orientations reconstructed from diffusion tensor MRI. We emphasize the key role of the simulation boundary conditions in approaching the physiologically correct motion and strains during contraction. We conclude by comparing the global and local validation criteria measures obtained using two different boundary conditions: the first constraining the LV base and the second taking into account the presence of the pericardium, which leads to greatly improved motion and deformation.

Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (\(0.93\pm 0.01\)), right ventricle (RV) cavity (\(0.80\pm 0.05\)), and the myocardium (\(0.72\pm 0.04\)). The \(R^2\) coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.

In vivo cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient-specific computational models are based. Herein, we propose a method to register subject-specific microstructural data to an arbitrary cardiac phase using measured cardiac displacements. We validate our approach using a subject-specific computational phantom based on data from human subjects. Compared to a geometry-based non-rigid registration method, the displacement-based registration leads to improved accuracy (less than 1° versus 10° average median error in cardiomyocyte angular differences) and tighter confidence interval (3° versus 65° average upper limit of the 95% confidence interval).

In vivo cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient-specific computational models are based. Herein, we propose a method to register subject-specific microstructural data to an arbitrary cardiac phase using measured cardiac displacements. We validate our approach using a subject-specific computational phantom based on data from human subjects. Compared to a geometry-based non-rigid registration method, the displacement-based registration leads to improved accuracy (less than 1° versus 10° average median error in cardiomyocyte angular differences) and tighter confidence interval (3° versus 65° average upper limit of the 95% confidence interval).

Metrics of regional myocardial function can detect the onset of cardiovascular disease, evaluate the response to therapy, and provide mechanistic insight into cardiac dysfunction. Knowledge of local myocardial microstructure is necessary to distinguish between isotropic and anisotropic contributions of local deformation and to quantify myofiber kinematics, a microstructurally anchored measure of cardiac function. Using a computational model we combine in vivo cardiac displacement and diffusion tensor data to evaluate pointwise the deformation gradient tensor and isotropic and anisotropic deformation invariants. In discussing the imaging methods and the model construction, we identify potential improvements to increase measurement accuracy. We conclude by demonstrating the applicability of our method to compute myofiber strain in five healthy volunteers.

Importance Following the adoption of electronic health records into a regulatory environment designed for paper records, there has been little investigation into the accuracy of physician documentation. Objective To quantify the percentage of emergency physician documentation of the review of systems (ROS) and physical examination (PE) that observers can confirm. Design, Setting, and Participants This case series took place at emergency departments in 2 academic medical centers between 2016 and 2018. Participants’ patient encounters were observed to compare real-time performance with clinical documentation. Exposures Resident physicians were shadowed by trained observers for 20 encounters (10 encounters per physician per site) to obtain real-time observational data; associated electronic health record data were subsequently reviewed. Main Outcomes and Measures Number of confirmed ROS systems (range, 0-14) divided by the number of documented ROS systems (range, 0-14), and number of confirmed PE systems (range, 0-14) divided by the number of documented PE systems (range, 0-14). Results The final study cohort included 9 licensed emergency medicine residents who evaluated a total of 180 patients (mean [SD] age, 48.7 [20.0] years; 91 [50.5%] women). For ROS, physicians documented a median (interquartile range [IQR]) of 14 (8-14) systems, while audio recordings confirmed a median (IQR) of 5 (3-6) systems. Overall, 755 of 1961 documented ROS systems (38.5%) were confirmed by audio recording data. For PE, resident physicians documented a median (IQR) of 8 (7-9) verifiable systems, while observers confirmed a median (IQR) of 5.5 (3-6) systems. Overall, 760 of 1429 verifiable documented PE systems (53.2%) were confirmed by concurrent observation. Interrater reliability for rating of ROS and PE was more than 90% for all measures. Conclusions and Relevance In this study of 9 licensed year emergency medicine residents, there were inconsistencies between the documentation of ROS and PE findings in the electronic health record and observational reports. These findings raise the possibility that some documentation may not accurately represent physician actions. Further studies should be undertaken to determine whether this occurrence is widespread. However, because such studies are unlikely to be performed owing to institution-level barriers that exist nationwide, payers should consider removing financial incentives to generate lengthy documentation.

Metrics of regional myocardial function can detect the onset of cardiovascular disease, evaluate the response to therapy, and provide mechanistic insight into cardiac dysfunction. Knowledge of local myocardial microstructure is necessary to distinguish between isotropic and anisotropic contributions of local deformation and to quantify myofiber kinematics, a microstructurally anchored measure of cardiac function. Using a computational model we combine in vivo cardiac displacement and diffusion tensor data to evaluate pointwise the deformation gradient tensor and isotropic and anisotropic deformation invariants. In discussing the imaging methods and the model construction, we identify potential improvements to increase measurement accuracy. We conclude by demonstrating the applicability of our method to compute myofiber strain in five healthy volunteers.