Featured Research

New Vistas for the Relationship between Empathy and Political Ideology 

Niloufar Zebarjadi, Annika Kluge, Eliyahu Adler, and Jonathan Levy 

Based on the increasing number of studies, an emerging topic of research is whether political ideology impacts psychophysiological processes like empathy. This field has largely relied on self-reporting, which can be unreliable.  In a study conducted by members of the Empathy-Building Neuro-lab (Aalto University and Bar-Ilan University) and led by Niloufar Zebarjadi and Jonathan Levy, researchers explored whether political beliefs influence one’s ability to empathize with others in physical pain. To uncover processes and phenomena that are sometimes overlooked by self-reporting, the researchers used neuroimaging to view individuals’ neural responses. In two large cohorts of people from Israel and Finland, with different political beliefs, the researchers observed no differences in neural, behavioral, or self-reported measures reflective of empathy. These findings differ from this lab’s previously published work on whether political ideology impacts empathy for the emotional suffering of others. Thus, empathy subtypes matter and should be considered distinct in laboratory and clinical settings. This work also highlights the need for unbiased measures of empathy that can advance our understanding beyond self-reports.

Encoding of global visual motion in the avian pretectum shifts from a bias for temporal-to-nasal selectivity to omnidirectional excitation across speeds

Suryadeep Dash, Vikram B. Baliga, Anthony B. Lapsansky, Douglas R. Wylie, and Douglas L. Altshuler

As we walk along a forest trail or a crowded city sidewalk, the reflections from the objects in the world move along the back of the eyes, generating a visual signal called optic flow. The retina sends many smaller signals of image movement to a region of the brain called the pretectum, which pools movement signals together to measure optic flow. It has been known for decades that in all vertebrates, from fish to mammals, individual pretectal neurons that respond to optic flow tend to be most sensitive to a specific direction. This response, however, has only been studied over a limited range of optic flow speeds. In a new study, pretectal neurons in zebra finches were studied across a much broader range of image speeds, likely spanning most of the speeds that the brain is capable of detecting. Across this range, it was found that the pretectal neurons shifted from a bias for encoding specific directions at intermediate speeds, to a bias for being responsive to all directions at very fast speeds. This new finding suggests that the same cells may help determine whether either course corrections are needed or a collision is imminent, depending upon the speed of the optic flow signal.

Associations between Thyroid Hormones and Cognitive Impairment in Patients with Parkinson's Disease

Yingying Peng, Lan Zhu, Qingling Bai, Limin Wang, and Qian Li

The correlation of serum thyroid hormone levels to cognitive impairments in Parkinson's disease (PD) patients remains unclear. The current results demonstrate a relationship between changes in serum thyroid hormone levels and cognitive impairments in PD patients. Our findings suggest that thyroid hormone levels, particularly free triiodothyronine, may serve as potential markers for cognitive dysfunction in PD.

Brain Encoding of Naturalistic, Continuous, and Unpredictable Tactile Events

Nicolò Castellani, Alessandra Federici, Marta Fantoni, Emiliano Ricciardi, Francesca Garbarini, and Davide Bottari

This study expands the current research conducted on neural tracking, opening the exploration of idiosyncratic tactile events and overcoming constraints of laboratory tasks that typically rely on discrete events. We validated a protocol for the ecological investigations of continuous, slow, tactile processing of the hands. The employed approach enriches the possible use of the EEG to characterize somatosensory neural representations of tactile events. Findings unravel coherent neural responses to continuous and naturalistic touch, with sensitivity for digit lateralization and representation.

Mechanically Induced Motor Tremors Disrupt the Perception of Time

Keri Gladhill, Rose De Kock, Weiwei Zhou, Wilsaan Joiner, and Martin Wiener

Our perception of time is naturally tied to movements of the body. Yet, how bodily movements bias or enhance estimates of time is still not well understood. We recently proposed that time estimates from body movements are combined with those from other sensory modalities via a Bayesian cue combination mechanism. This suggests that, by adding noise into body movements, time estimates from other sensory modalities should also be noisier. Here, we find evidence for this effect across two experiments where human subjects judged intervals of time while moving a robotic arm at different levels of tremor. These findings support the connection between body movements and time and provide an additional avenue of research using noisy movements to impact sensory estimates.

A Subcortical Model for Auditory Forward Masking with Efferent Control of Cochlear Gain

Braden N. Maxwell, Afagh Farhadi, Marc A. Brennan, Adam Svec, and Laurel H. Carney

The simulations presented here demonstrate that a recent computational model of the auditory subcortex including medial olivocochlear efferents generates forward masking, an increase in detection threshold for a short probe tone following a preceding sound. This model explains results from physiological recordings and suggests potential connections to psychoacoustic experiments. The theory that efferent control of cochlear gain is a contributing mechanism for forward masking explains the strength of masking exhibited by cochlear nucleus neurons (not explained by physiological theories in which the strength of forward masking is increased later in the ascending pathway) and explains results for a psychoacoustic task with random variation in masker level (not explained by the theory that persistent masker energy interferes with detection of the probe).

Machine Learning Elucidates Electrophysiological Properties Predictive of Multi- and Single-Firing Human and Mouse Dorsal Root Ganglia Neurons

Nesia A. Zurek, Sherwin Thiyagarajan, Reza Ehsanian, Aleyah E. Goins, Sachin Goyal, Mark Shilling, Christophe G. Lambert, Karin N. Westlund, and Sascha R. A. Alles

This study will improve the understanding of the electrophysiological mechanisms of DRG neurons. Our machine learning algorithms show few species differences between mouse and human DRG neuron electrophysiology under baseline conditions. These are important findings for the study of neuronal excitability in the context of pain therapeutic development.