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Evidence from functional neuroimaging of the human brain indicates that information about salient properties of an object-such as what it looks like, how it moves, and how it is used-is stored in sensory and motor systems active when that information was acquired. As a result, object concepts belonging to different categories like animals and tools are represented in partially distinct, sensory- and motor property-based neural networks. This suggests that object concepts are not explicitly represented, but rather emerge from weighted activity within property-based brain regions. However, some property-based regions seem to show a categorical organization, thus providing evidence consistent with category-based, domain-specific formulations as well.

The cognitive and neural mechanisms underlying category-specific knowledge remain controversial. Here we report that, across multiple tasks (viewing, delayed match to sample, naming), pictures of animals and tools were associated with highly consistent, category-related patterns of activation in ventral (fusiform gyrus) and lateral (superior and middle temporal gyri) regions of the posterior temporal lobes. In addition, similar patterns of category-related activity occurred when subjects read the names of, and answered questions about, animals and tools. These findings suggest that semantic object information is represented in distributed networks that include sites for storing information about specific object attributes such as form (ventral temporal cortex) and motion (lateral temporal cortex).

An intriguing and puzzling consequence of damage to the human brain is selective loss of knowledge about a specific category of objects. One patient may be unable to identify or name living things, whereas another may have selective difficulty identifying man-made objects. To investigate the neural correlates of this remarkable dissociation, we used positron emission tomography to map regions of the normal brain that are associated with naming animals and tools. We found that naming pictures of animals and tools was associated with bilateral activation of the ventral temporal lobes and Broca's area. In addition, naming animals selectively activated the left medial occipital lobe--a region involved in the earliest stages of visual processing. In contrast, naming tools selectively activated a left premotor area also activated by imagined hand movements, and an area in the left middle temporal gyrus also activated by the generation of action words. Thus the brain regions active during object identification are dependent, in part, on the intrinsic properties of the object presented.

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Resting-State Functional Magnetic Resonance Imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but not to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences: Global Signal Regression (GSReg) – the practice of projecting out signal averaged over the entire brain – can change resting state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem with GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter inter-regional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true inter-regional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network’s regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.

Behavioral studies of facial emotion recognition (FER) in autism spectrum disorders (ASD) have yielded mixed results. Here we address demographic and experiment-related factors that may account for these inconsistent findings. We also discuss the possibility that compensatory mechanisms might enable some individuals with ASD to perform well on certain types of FER tasks in spite of atypical processing of the stimuli, and difficulties with real-life emotion recognition. Evidence for such mechanisms comes in part from eye-tracking, electrophysiological, and brain imaging studies, which often show abnormal eye gaze patterns, delayed event-related-potential components in response to face stimuli, and anomalous activity in emotion-processing circuitry in ASD, in spite of intact behavioral performance during FER tasks. We suggest that future studies of FER in ASD: 1) incorporate longitudinal (or cross-sectional) designs to examine the developmental trajectory of (or age-related changes in) FER in ASD and 2) employ behavioral and brain imaging paradigms that can identify and characterize compensatory mechanisms or atypical processing styles in these individuals.