Dog-human social relationship: representation of human face familiarity and emotions in the dog brain.

This study investigated the behavioral and neural indices of detecting facial familiarity and facial emotions in human faces by dogs. Awake canine fMRI was used to evaluate dogs' neural response to pictures and videos of familiar and unfamiliar human faces, which contained positive, neutral, and negative emotional expressions. The dog-human relationship was behaviorally characterized out-of-scanner using an unsolvable task. The caudate, hippocampus, and amygdala, mainly implicated in reward, familiarity and emotion processing, respectively, were activated in dogs when viewing familiar and emotionally salient human faces. Further, the magnitude of activation in these regions correlated with the duration for which dogs showed human-oriented behavior towards a familiar (as opposed to unfamiliar) person in the unsolvable task. These findings provide a bio-behavioral basis for the underlying markers and functions of human-dog interaction as they relate to familiarity and emotion in human faces.

Separate brain areas for processing human and dog faces as revealed by awake fMRI in dogs (Canis familiaris).

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.

MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data.

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled "Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets" [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample.