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.

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Grounded cognition explanations of metaphor comprehension predict activation of sensorimotor cortices relevant to the metaphor's source domain. We tested this prediction for body-part metaphors using functional magnetic resonance imaging while participants heard sentences containing metaphorical or literal references to body parts, and comparable control sentences. Localizer scans identified body-part-specific motor, somatosensory and visual cortical regions. Both subject- and item-wise analyses showed that, relative to control sentences, metaphorical but not literal sentences evoked limb metaphor-specific activity in the left extrastriate body area (EBA), paralleling the EBA's known visual limb-selectivity. The EBA focus exhibited resting-state functional connectivity with ipsilateral semantic processing regions. In some of these regions, the strength of resting-state connectivity correlated with individual preference for verbal processing. Effective connectivity analyses showed that, during metaphor comprehension, activity in some semantic regions drove that in the EBA. These results provide converging evidence for grounding of metaphor processing in domain-specific sensorimotor cortical activity.

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.

ObjectiveIt 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.MethodsTherefore, 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.ResultsMP 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.ConclusionOur 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.