Sinan Zhao’s research while affiliated with Auburn University and other places

Simple Summary The expense associated with training detection and service dogs is significant. By employing resting-state functional resonance imaging technique, a non-invasive method capable of probing brain function, we can identify the critical brain regions linked to selecting dogs inclined towards successful training. These biomarkers identified before commencement of training predict successful trainability and hence reduce training costs by obviating the need to invest in dogs that are unlikely to be successful. Furthermore, our research extends to elucidating the identified brain regions in dogs that exhibit homologous functions to those found in the human brain, offering valuable insights into the evolutionary parallels between humans and our closest animal companions. Abstract Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months’ post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.

Objective: It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks. Methods: Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network. Results: MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC. Conclusion: Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Functional magnetic resonance imaging (fMRI) has emerged as a viable method to study the neural processing underlying cognition in awake dogs. Working dogs were presented with pictures of dog and human faces. The human faces varied in familiarity (familiar trainers and unfamiliar individuals) and emotional valence (negative, neutral, and positive). Dog faces were familiar (kennel mates) or unfamiliar. The findings revealed adjacent but separate brain areas in the left temporal cortex for processing human and dog faces in the dog brain. The human face area (HFA) and dog face area (DFA) were both parametrically modulated by valence indicating emotion was not the basis for the separation. The HFA and DFA were not influenced by familiarity. Using resting state fMRI data, functional connectivity networks (connectivity fingerprints) were compared and matched across dogs and humans. These network analyses found that the HFA mapped onto the human fusiform area and the DFA mapped onto the human superior temporal gyrus, both core areas in the human face processing system. The findings provide insight into the evolution of face processing.

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.

Depression is common in patients with Parkinson's disease (PD), which can make all the other symptoms of PD much worse. It is thus urgent to differentiate depressed PD (DPD) patients from non-depressed PD (NDPD). The purpose of the present study was to characterize alterations in directional brain connectivity unique to Parkinson's disease with depression, using resting state functional magnetic resonance imaging (rs-fMRI). Sixteen DPD patients, 20 NDPD patients, 17 patients with major depressive disorder (MDD) and 21 healthy control subjects (normal controls [NC]) underwent structural MRI and rs-fMRI scanning. Voxel-based morphometry and directional brain connectivity during resting-state were analyzed. Analysis of variance (ANOVA) and 2-sample t tests were used to compare each pair of groups, using sex, age, education level, structural atrophy, and/or HAMD, unified PD rating scale (UPDRS) as covariates. In contrast to NC, DPD showed significant gray matter (GM) volume abnormalities in some mid-line limbic regions including dorsomedial prefrontal cortex and precuneus, and sub-cortical regions including caudate and cerebellum. Relative to NC and MDD, both DPD and NDPD showed significantly increased directional connectivity from bilateral anterior insula and posterior orbitofrontal cortices to left inferior temporal cortex. As compared with NC, MDD and NDPD, alterations of directional connectivity in DPD were specifically observed in the pathway from bilateral anterior insula and posterior orbitofrontal cortices to right basal ganglia. Resting state directional connectivity alterations were observed between emotion network and motor network in DPD patients after controlling for age, sex, structural atrophy. Given that these alterations are unique to DPD, it may provide a potential differential biomarker for distinguishing DPD from NC, NDPD, and MDD.