Information Rate Research Articles

Comparison of discretization strategies for the model-free information-theoretic assessment of short-term physiological interactions

This work presents a comparison between different approaches for the model-free estimation of information-theoretic measures of the dynamic coupling between short realizations of random processes. The measures considered are the mutual information rate (MIR) between two random processes X and Y and the terms of its decomposition evidencing either the individual entropy rates of X and Y and their joint entropy rate, or the transfer entropies from X to Y and from Y to X and the instantaneous information shared by X and Y. All measures are estimated through discretization of the random variables forming the processes, performed either via uniform quantization (binning approach) or rank ordering (permutation approach). The binning and permutation approaches are compared on simulations of two coupled non-identical Hènon systems and on three datasets, including short realizations of cardiorespiratory (CR, heart period and respiration flow), cardiovascular (CV, heart period and systolic arterial pressure), and cerebrovascular (CB, mean arterial pressure and cerebral blood flow velocity) measured in different physiological conditions, i.e., spontaneous vs paced breathing or supine vs upright positions. Our results show that, with careful selection of the estimation parameters (i.e., the embedding dimension and the number of quantization levels for the binning approach), meaningful patterns of the MIR and of its components can be achieved in the analyzed systems. On physiological time series, we found that paced breathing at slow breathing rates induces less complex and more coupled CR dynamics, while postural stress leads to unbalancing of CV interactions with prevalent baroreflex coupling and to less complex pressure dynamics with preserved CB interactions. These results are better highlighted by the permutation approach, thanks to its more parsimonious representation of the discretized dynamic patterns, which allows one to explore interactions with longer memory while limiting the curse of dimensionality.

Persistent opioid use after cesarean delivery in the United States of America: a systematic review

BackgroundThis systematic review assessed the incidence of persistent opioid use after cesarean delivery in opioid-naïve individuals in the United States of America (USA). MethodsA literature search identified articles that reported persistent opioid use after cesarean delivery between January 2000 and February 2022. Studies were manually reviewed, and data pertaining to rates of persistent postpartum opioid use and methodologic information were qualitatively analyzed. Sixty studies were identified, and four met inclusion criteria. All four studies were retrospective reviews of insurance claims data among individuals naïve to opioids. Data from 486 263 individuals delivering between 2001 and 2016 were included. The criteria to define persistent opioid use in opioid-naïve individuals generally involved two or more opioid prescriptions filled within the first year after cesarean delivery, with each definition including additional varying criteria. ResultsRates of persistent opioid use after cesarean delivery ranged from 0.12% to 2.2%, with the highest rate reported in private insurance claims between 2008 and 2016. Findings suggest a substantial number of individuals are at risk (from 1:1000 to 1:50) for persistent opioid use up to 12 months postpartum. With 1.2 million individuals undergoing cesarean delivery annually in the USA, as few as 1440 and as many as 26 400 may continue using opioids past the fourth trimester. ConclusionsFindings emphasize the importance of developing a standardized definition of persistent opioid use to accurately assess the risk, rate, and trends for persistent opioid use among opioid-naïve individuals undergoing cesarean delivery.

Noise effects on purity and quantum entanglement in terms of physical implementability

Quantum decoherence due to imperfect manipulation of quantum devices is a key issue in the noisy intermediate-scale quantum (NISQ) era. Standard analyses in quantum information and quantum computation use error rates to parameterize quantum noise channels. However, there is no explicit relation between the decoherence effect induced by a noise channel and its error rate. In this work, we propose to characterize the decoherence effect of a noise channel by the physical implementability of its inverse, which is a universal parameter quantifying the difficulty to simulate the noise inverse with accessible quantum channels. We establish two concise inequalities connecting the decrease of the state purity and logarithmic negativity after a noise channel to the physical implementability of the noise inverse, which is required to be decomposed as mutually orthogonal unitaries or product channels respectively. Our results are numerically demonstrated on several commonly adopted two-qubit noise models. We believe that these relations contribute to the theoretical research on the entanglement properties of noise channels and provide guiding principles for quantum circuit design.

Correlates of the Selection Validation Survey with Police Officer Field Performance

In the present study, we examined performance rating correlates of the Selection Validation Survey (SVS), an informant rating form used to describe the characteristics of newly hired public safety personnel following their initial training period. We correlated SVS ratings for n = 174 police officers with aggregate scores derived from daily performance observation ratings provided by their field training officers (i.e., senior law enforcement officers assigned to train, observe, and evaluate police recruits during a formal 16-week training period). Results generally indicated moderate to strong associations between conceptually similar SVS ratings and field training officer scores, providing evidence that the SVS variables validly summarize performance-relevant data accrued during the field training period. For example, a single SVS item asking the rater to characterize the officer’s overall field performance correlated highly in the expected direction (Spearman’s rho = −.69) with a composite of daily ratings describing the officer’s observed field performance and problem-solving skills. Taken together, these findings indicate that the SVS meaningfully and efficiently captures a range of important information regarding the performance and professional skills of new police officers, providing a useful validation criterion for predictors of police officer performance.

Two new low rank tensor completion methods based on sum nuclear norm

The low rank tensor completion (LRTC) problem has attracted great attention in computer vision and signal processing. How to acquire high quality image recovery effect is still an urgent task to be solved at present. This paper firstly proposes a new tensor L2,1 norm minimization model (TLNM) that integrates sum nuclear norm (SNN) method, differing from the classical tensor nuclear norm (TNN)-based tensor completion method, with L2,1 norm and QR decomposition for solving the LRTC problem. To improve the utilization rate of the local prior information of the image, a total variation (TV) regularization term is introduced, resulting in another new class of tensor L2,1 norm minimization with total variation model (TLNMTV). Both proposed models are convex and therefore have global optimal solutions. Moreover, we adopt the Alternating Direction Multiplier Method (ADMM) to obtain the closed-form solution of each variable, which makes the algorithm be able to be efficiently implemented. Numerical experiments show that the two proposed algorithms are convergent and outperform compared methods. In particular, our method significantly outperforms the compared methods when the sampling rate is 2.5% for hyperspectral images.

Efficacy of transversalis fascia plane block as a novel ındication for varicocelectomy surgery: prospective randomized controlled study

BackgroundVaricocele occurs as a result of dilatation of the pampiniform plexus in the spermatic veins. In this study, our primary aim was to evaluate the effect of Transversalis Fascia Plane Block (TFPB) on pain scores in the postoperative period in patients undergoing varicocelectomy surgery, and our secondary aim was to evaluate the effect of TFPB on analgesic consumption.MethodsThe study was initiated following local ethics committee approval, and sixty ASA I-II patients > 18y scheduled to undergo varicocelectomy and who consented to participation were enrolled. Before the procedure, the patients were randomly assigned two groups: Transversalis Fascia Plan block group (Group TFPB) or surgical incision site infiltration group (Group I).All surgeries were carried out under general anesthesia, and microsurgery using the subinguinal approach. After surgical suturing, TFPB and local infiltration blocks were applied prior to termination of anesthesia.For each block, 20 mL of 0.25% bupivacaine was utilized. Patients' demographic information, passive and active VAS ratings after surgery, usage of non steroidal anti-inflammatory medications and rescue analgesia, and the requirement for rescue analgesia, were recorded.ResultsA total of 60 patients were included in the study. In terms of demographic data, there was no difference between the groups. At all hours, there was a statistically significant decrease in favor of Group TFPB in terms of active and passive VAS scores (p < 0.001), non steroidal anti-inflammatory analgesic use (p < 0.05), and tramadol requirement (p < 0.001).ConclusionThis study has shown that TFPB can provide a more effective analgesia when compared to surgical site infiltration.

Multimethod Validation of the Cinematic Self Scale as a Measure of Narrative Identity by Convergent Self and Informant Ratings

According to narrative identity people understand themselves through story. However, evidence suggests people vary dispositionally in how strongly they experience narrative identity. Recently, a self-report scale called the Cinematic Self scale was constructed and validated to measure this trait variation in narrative identity. In that article, evidence across five studies supported the validity of the cinematic self scale as measuring narrative identity. A limitation of that evidence was its exclusive reliance on self-reports; this can be problematic because associations may be inflated by shared method biases. The present research builds on that limitation by using data from multiple methods: self-reports and informant reports. Do self-reported cinematic self scores correspond with informant-reported narrative behaviors? University students ( N = 127) completed the cinematic self scale and informants reported on their narrative behaviors ( N = 395). Positive, medium-sized correlations between self-reports and informant reports were found across multiple analytical approaches, controlling for personality variables. The results extend validation evidence of the cinematic self scale as a measure of narrative identity.

An Adaptive-Iterative Nonlinear Interference Cancellation in Time-Varying Full-Duplex Channels

In this paper, first, the sensitivity of traditional Full-Duplex (FD) communication systems to time variation in the self-interference (SI) channel is demonstrated via performance analysis. It is seen that conventional schemes are not capable of providing efficient operation regarding practical concerns such as spectral efficiency, SI channel aging and learning accuracy, and the contamination of signal-of-interest (SoI). Then, in regard to the aforementioned concerns, a practical FD operation together with a novel iterative SoI-contamination-aware digital SI cancellation (DSIC) algorithm that relieves the sensitivity against time variation considerably is proposed. The proposed scheme, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, is practical in terms of training and computational complexity thanks to its two-stage structure such that complex nonlinear parameter learning (NPL) is conducted very rarely in the first stage, and linear processing is performed adaptively at symbol-rate in the second stage, where SoI is iteratively estimated at each data block and eliminated from the received signal to improve SI channel estimation accuracy. Finally, the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Turbo</i> DSIC, under hardware impairments and time-varying (TV) propagation, is evaluated via both computer simulation environment and actual experimental hardware setup in terms of bit error rate (BER) and achievable information rate (AIR) metrics.

Concatenated Codes for Multiple Reads of a DNA Sequence

Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer nonbinary low-density parity-check code or a polar code and either an inner convolutional code or a time-varying block code. We propose two novel decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences and has a complexity that is linear with the number of sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence. In addition, we succeed in improving the performance of the aforementioned coding scheme by optimizing both the inner and outer codes.

A study on the channel bonding in IoT networks: Requirements, applications, and challenges

SummaryThe most well‐known sort of remote Internet connection is wireless local area networks (WLANs) due to its unsophisticated operation and deployment. Subsequently, the quantity of gadgets getting to the Internet through WLANs, for example, PCs, cell phones, or wearables, is expanding radically at the equivalent time that applications' throughput necessities do. To provide wireless networks with supplementary spectral resources, the researchers are considering the aggregation of frequency spectrums in licensed, unlicensed, and shared access (SA) bands. Channel aggregation/channel bonding (CA/CB) techniques accumulate quite a few channels together as one channel for the purpose of achieving better bandwidth utilization. In this study, we focus on reliable CA/CB techniques in different wireless networks. CA/CB procedures are utilized for empowering higher information rates by transmitting in more extensive channels, accordingly expanding range proficiency with the assured secure channel for communication. We also discuss the spectral scarcity issues in today's wireless IoT network. This paper presents an extensive survey on CA/CB procedures and methods, issues and challenges, and open research areas related to IoT devices. We analyze the performance of channel CA/CB strategies in the different wireless networks too.

Biophysical parameters control signal transfer in spiking network.

Information transmission and representation in both natural and artificial networks is dependent on connectivity between units. Biological neurons, in addition, modulate synaptic dynamics and post-synaptic membrane properties, but how these relate to information transmission in a population of neurons is still poorly understood. A recent study investigated local learning rules and showed how a spiking neural network can learn to represent continuous signals. Our study builds on their model to explore how basic membrane properties and synaptic delays affect information transfer. The system consisted of three input and output units and a hidden layer of 300 excitatory and 75 inhibitory leaky integrate-and-fire (LIF) or adaptive integrate-and-fire (AdEx) units. After optimizing the connectivity to accurately replicate the input patterns in the output units, we transformed the model to more biologically accurate units and included synaptic delay and concurrent action potential generation in distinct neurons. We examined three different parameter regimes which comprised either identical physiological values for both excitatory and inhibitory units (Comrade), more biologically accurate values (Bacon), or the Comrade regime whose output units were optimized for low reconstruction error (HiFi). We evaluated information transmission and classification accuracy of the network with four distinct metrics: coherence, Granger causality, transfer entropy, and reconstruction error. Biophysical parameters showed a major impact on information transfer metrics. The classification was surprisingly robust, surviving very low firing and information rates, whereas information transmission overall and particularly low reconstruction error were more dependent on higher firing rates in LIF units. In AdEx units, the firing rates were lower and less information was transferred, but interestingly the highest information transmission rates were no longer overlapping with the highest firing rates. Our findings can be reflected on the predictive coding theory of the cerebral cortex and may suggest information transfer qualities as a phenomenological quality of biological cells.

Introducing 4D Geometric Shell Shaping for Mitigating Nonlinear Interference Noise

Four dimensional geometric shell shaping (4D-GSS) is introduced as an approach for closing the nonlinearity-caused shaping gap. This format is designed at the spectral efficiency of 8 b/4D-sym and is compared against polarization-multiplexed 16QAM (PM-16QAM) and probabilistically shaped PM-16QAM (PS-PM-16QAM) in a 400ZR-compatible transmission setup with high amount of nonlinearities. Reach increase and nonlinearity tolerance are evaluated in terms of achievable information rates and post-FEC bit-error rate. Numerical simulations for a single-span, single-channel show that 4D-GSS achieves increased nonlinear tolerance and reach increase against PM-16QAM and PS-PM-16QAM when optimized for bit-metric decoding ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{R}_\text{BMD}$</tex-math></inline-formula> ). In terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{R}_\text{BMD}$</tex-math></inline-formula> , gains are small with a reach increase of 1.7% compared to PM-16QAM. When optimizing for mutual information, a larger reach increase of 3% is achieved compared to PM-16QAM. Moreover, the introduced GSS scheme provides a scalable framework for designing well-structured 4D modulation formats with low complexity.

The Chinese version of informant AD8 for mild cognitive impairment and dementia screening in community-dwelling older adults.

Conducting routine mild cognitive impairment (MCI) and dementia screening for older adults in the community is important, which not only can improve our understanding of these diseases but also can increase early detection and treatment. To analyze the reliability and validity of the informant AD8 and explore the cut-off values for screening MCI and dementia in the community-dwelling older adults, this study adopted a multi-stage cluster sampling method to recruit 327 participants aged 60 and over in communities. The informant AD8 and Clinical Dementia Rating (CDR) scales were used to evaluate cognitive function of the subjects, and the receiver operating characteristic curves (ROC) was conducted to test the screening efficacy. Among the 327 participants, 33.0% of them met the criteria of MCI, and 3.4% of them met the criteria of dementia. The area under the receiver operating characteristic curve (AUC) of the informant AD8 for screening dementia was 0.974, with a screening cut-off of three, sensitivity of 90.9% and specificity of 89.0%. But it has a poor discriminability in MCI screening [AUC = 0.645, 95% confidence interval (CI): 0.578-0.711]. This study suggests that the informant AD8 is an ideal and useful tool for dementia screening in community-dwelling older adults. However, it is less capable to distinguish older adults with MCI from those with normal cognitive function.

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors.

Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.

Extru-seq: a method for predicting genome-wide Cas9 off-target sites with advantages of both cell-based and in vitro approaches

We present a novel genome-wide off-target prediction method named Extru-seq and compare it with cell-based (GUIDE-seq), in vitro (Digenome-seq), and in silico methods using promiscuous guide RNAs with large numbers of valid off-target sites. Extru-seq demonstrates a high validation rate and retention of information about the intracellular environment, both beneficial characteristics of cell-based methods. Extru-seq also shows a low miss rate and could easily be performed in clinically relevant cell types with little optimization, which are major positive features of the in vitro methods. In summary, Extru-seq shows beneficial features of cell-based and in vitro methods.

Promoting ICT adoption in rural entrepreneurship: more neighbourhood effect or more institutional incentives?—Empirical evidence from China

AbstractAlthough digital village projects have been vigorously promoted in China in recent years, the application rate of information and communication technology (ICT) is still low, while the ways in which governmental technology promotion turns into endogenous initiatives and the impact of informal institutions remains unclear. This study found that the neighbourhood effect, rather than institutional incentives, is decisive for ICT adoption. Perceived well‐being benefit was found to be an indispensable internal driver for ICT adoption, whereas government incentives varied across industries, producing a variety of impacts. The findings of this study provide practical insights into the effective promotion of ICT adoption, suggesting that government and community forces should be integrated, and that both cognitive and affective psychological drivers should be considered.

Constrained radar waveform optimization for a cooperative radar-communication system

The coexistence of radar-sensing and communication systems research has received a surge of interest in recent times to tackle the issue of spectrum inadequacy. Designing an optimized radar waveform for a coexistence scenario has been a challenging task for accomplishing the convergence of radar-sensing and communication functionalities, without degrading the performance at either end. This paper proposes a novel global optimization-based Spatial Branch and Bound (SBnB) approach to optimize the phase coefficients of a Non-Linear Frequency Modulated (NLFM) waveform in a CRCS framework. In addition, the Modified-Power Ratio Constraint-Cramér–Rao Lower Bound (M-PRC-CRLB), a local optimization-based approach is proposed to optimize the phase coefficients of an NLFM waveform. The spectral energy distribution and auto-correlation characteristics of an NLFM waveform are comprehensively investigated for various values of polynomial order (N) and at different threshold Signal-to-Noise-Ratio (SNR) values. To compare the proposed waveform design approaches (M-PRC-CRLB, SBnB) with the existing waveform design approaches namely, Minimum Estimation Error Variance (MEEV) and PRC- CRLB, a Peak-to-Side-Lobe-Ratio (PSLR), and Integrated-Side-Lobe-Ratio (ISLR) are evaluated at various polynomial orders and threshold SNR values. Furthermore, the performance of a CRCS is assessed using the radar estimation rate and communication data rate. The simulation results reveal that the proposed optimized radar waveform design approaches provide improved performance compared to the existing radar waveform design approaches in terms of radar estimation rate. Further, the proposed global optimization-based SBnB approach achieves a comparable performance of the communication data rate. In addition, the proposed approaches accomplish enhanced spectral utilization, controlled side-lobe energy levels, reduced range-domain ambiguities, and a higher information rate in a CRCS.

Association between early response of alpha-fetoprotein and treatment efficacy of systemic therapy for advanced hepatocellular carcinoma: A multicenter cohort study from China.

Alpha-fetoprotein (AFP) is a well-identified biomarker in hepatocellular carcinoma (HCC). However, only limited AFP-related studies have evaluated its early response to systemic therapy. This study was performed with the aim of assessing the value of early AFP response in predicting overall survival (OS) and progression-free survival (PFS) in advanced HCC patients receiving systemic therapy. This cohort study included HCC patients with baseline AFP ≥ 200 ng/ml and no prior treatment history. A > 20% decline in the serum AFP level from baseline to the first follow-up (i.e., 4~6 weeks after treatment) was defined as an early AFP response. Patient demographic information, clinical characteristics, radiological response, and survival rates were compared between patients with early AFP response and patients without early AFP response. We further utilized multivariate Cox regression to seek characteristics related to OS and PFS. Among 154 patients, 69 patients (44.8%) showed an early AFP response. The disease control rate (76.8 vs. 54.1%; P = 0.003) and objective response rate (38.4 vs. 11.8%; P = 0.001) were significantly higher in patients with an early AFP response. By performing multivariate analysis, early AFP response remained a prognostic factor for longer PFS (HR 0.546; 95% CI 0.371-0.804; P = 0.002) and longer OS (HR 0.529; 95% CI 0.335-0.834; P = 0.006). An early AFP response is correlated with longer overall survival and progression-free survival for advanced HCC patients receiving systemic therapy. Moreover, an early AFP response is an independent prognostic factor for longer OS and PFS.

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.

Multisite ALLFTD study modeling progressive empathy loss from the earliest stages of behavioral variant frontotemporal dementia.

Empathy relies on fronto-cingular and temporal networks that are selectively vulnerable in behavioral variant frontotemporal dementia (bvFTD). This study modeled when in the disease process empathy changes begin, and how they progress. Four hundred thirty-one individuals with asymptomatic genetic FTD (n=114), genetic and sporadic bvFTD (n=317), and 163 asymptomatic non-carrier controls were enrolled. In sub-samples, we investigated empathy measured by the informant-based Interpersonal Reactivity Index (IRI) at each disease stage and over time (n=91), and its correspondence to underlying atrophy (n=51). Empathic concern (estimate=4.38, 95% confidence interval [CI]=2.79, 5.97; p<0.001) and perspective taking (estimate=5.64, 95% CI=3.81, 7.48; p<0.001) scores declined between the asymptomatic and very mild symptomatic stages regardless of pathogenic variant status. More rapid loss of empathy corresponded with subcortical atrophy. Loss of empathy is an early and progressive symptom of bvFTD that is measurable by IRI informant ratings and can be used to monitor behavior in neuropsychiatry practice and treatment trials.