Paola Scifo

Bio: Paola Scifo is an academic researcher from Vita-Salute San Raffaele University. The author has contributed to research in topics: Insulin resistance & Medicine. The author has an hindex of 38, co-authored 78 publications receiving 8166 citations. Previous affiliations of Paola Scifo include University of Milano-Bicocca.

Listening to Action-related Sentences Activates Fronto-parietal Motor Circuits

Abstract: Observing actions made by others activates the cortical circuits responsible for the planning and execution of those same actions This observation–execution matching system (mirror-neuron system) is thought to play an important role in the understanding of actions made by others In an fMRI experiment, we tested whether this system also becomes active during the processing of action-related sentences Participants listened to sentences describing actions performed with the mouth, the hand, or the leg Abstract sentences of comparable syntactic structure were used as control stimuli The results showed that listening to action-related sentences activates a left fronto-parieto-temporal network that includes the pars opercularis of the inferior frontal gyrus (Broca's area), those sectors of the premotor cortex where the actions described are motorically coded, as well as the inferior parietal lobule, the intraparietal sulcus, and the posterior middle temporal gyrus These data provide the first direct evidence that listening to sentences that describe actions engages the visuomotor circuits which subserve action execution and observation

Intramyocellular triglyceride content is a determinant of in vivo insulin resistance in humans: a 1H-13C nuclear magnetic resonance spectroscopy assessment in offspring of type 2 diabetic parents.

Abstract: Insulin resistance is the best prediction factor for the clinical onset of type 2 diabetes. It was suggested that intramuscular triglyceride store may be a primary pathogenic factor for its development. To test this hypothesis, 14 young lean offspring of type 2 diabetic parents, a model of in vivo insulin resistance with increased risk to develop diabetes, and 14 healthy subjects matched for anthropomorphic parameters and life habits were studied with 1) euglycemic-hyperinsulinemic clamp to assess whole body insulin sensitivity, 2) localized 1H nuclear magnetic resonance (NMR) spectroscopy of the soleus (higher content of fiber type I, insulin sensitive) and tibialis anterior (higher content of fiber type IIb, less insulin sensitive) muscles to assess intramyocellular triglyceride content, 3) 13C NMR of the calf subcutaneous adipose tissue to assess composition in saturated/unsaturated carbons of triglyceride fatty acid chains, and 4) dual X-ray energy absorption to assess body composition. Offspring of diabetic parents, notwithstanding normal fat content and distribution, were characterized by insulin resistance and increased intramyocellular triglyceride content in the soleus (P < 0.01) but not in the tibialis anterior (P = 0.19), but showed a normal content of saturated/unsaturated carbons in the fatty acid chain of subcutaneous adipocytes. Stepwise regression analysis selected intramyocellular triglyceride soleus content and plasma free fatty acid levels as the main predictors of whole body insulin sensitivity. In conclusion, 1H and 13C NMR spectroscopy revealed intramyocellular abnormalities of lipid metabolism associated with whole body insulin resistance in subjects at high risk of developing diabetes, and might be useful tools for noninvasively monitoring these alterations in diabetes and prediabetic states.

Bilingualism Tunes the Anterior Cingulate Cortex for Conflict Monitoring

Abstract: Monitoring and controlling 2 language systems is fundamental to language use in bilinguals. Here, we reveal in a combined functional (event-related functional magnetic resonance imaging) and structural neuroimaging (voxel-based morphometry) study that dorsal anterior cingulate cortex (ACC), a structure tightly bound to domain-general executive control functions, is a common locus for language control and resolving nonverbal conflict. We also show an experience-dependent effect in the same region: Bilinguals use this structure more efficiently than monolinguals to monitor nonlinguistic cognitive conflicts. They adapted better to conflicting situations showing less ACC activity while outperforming monolinguals. Importantly, for bilinguals, brain activity in the ACC, as well as behavioral measures, also correlated positively with local gray matter volume. These results suggest that early learning and lifelong practice of 2 languages exert a strong impact upon human neocortical development. The bilingual brain adapts better to resolve cognitive conflicts in domain-general cognitive tasks.

Neural language networks at birth

Abstract: The ability to learn language is a human trait. In adults and children, brain imaging studies have shown that auditory language activates a bilateral frontotemporal network with a left hemispheric dominance. It is an open question whether these activations represent the complete neural basis for language present at birth. Here we demonstrate that in 2-d-old infants, the language-related neural substrate is fully active in both hemispheres with a preponderance in the right auditory cortex. Functional and structural connectivities within this neural network, however, are immature, with strong connectivities only between the two hemispheres, contrasting with the adult pattern of prevalent intrahemispheric connectivities. Thus, although the brain responds to spoken language already at birth, thereby providing a strong biological basis to acquire language, progressive maturation of intrahemispheric functional connectivity is yet to be established with language exposure as the brain develops.

Imaging Cognition II: An Empirical Review of 275 PET and fMRI Studies

Abstract: Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) have been extensively used to explore the functional neuroanatomy of cognitive functions. Here we review 275 PET and fMRI studies of attention (sustained, selective, Stroop, orientation, divided), perception (object, face, space/motion, smell), imagery (object, space/ motion), language (written/spoken word recognition, spoken/ no spoken response), working memory (verbal/numeric, object, spatial, problem solving), semantic memory retrieval (categorization, generation), episodic memory encoding (verbal, object, spatial), episodic memory retrieval (verbal, nonverbal, success, effort, mode, context), priming (perceptual, conceptual), and procedural memory (conditioning, motor, and nonmotor skill learning). To identify consistent activation patterns associated with these cognitive operations, data from 412 contrasts were summarized at the level of cortical Brodmann's areas, insula, thalamus, medial-temporal lobe (including hippocampus), basal ganglia, and cerebellum. For perception and imagery, activation patterns included primary and secondary regions in the dorsal and ventral pathways. For attention and working memory, activations were usually found in prefrontal and parietal regions. For language and semantic memory retrieval, typical regions included left prefrontal and temporal regions. For episodic memory encoding, consistently activated regions included left prefrontal and medial-temporal regions. For episodic memory retrieval, activation patterns included prefrontal, medial-temporal, and posterior midline regions. For priming, deactivations in prefrontal (conceptual) or extrastriate (perceptual) regions were consistently seen. For procedural memory, activations were found in motor as well as in non-motor brain areas. Analysis of regional activations across cognitive domains suggested that several brain regions, including the cerebellum, are engaged by a variety of cognitive challenges. These observations are discussed in relation to functional specialization as well as functional integration.

Where Is the Semantic System? A Critical Review and Meta-Analysis of 120 Functional Neuroimaging Studies

Abstract: Semantic memory refers to knowledge about people, objects, actions, relations, self, and culture acquired through experience. The neural systems that store and retrieve this information have been studied for many years, but a consensus regarding their identity has not been reached. Using strict inclusion criteria, we analyzed 120 functional neuroimaging studies focusing on semantic processing. Reliable areas of activation in these studies were identified using the activation likelihood estimate (ALE) technique. These activations formed a distinct, left-lateralized network comprised of 7 regions: posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus. Secondary analyses showed specific subregions of this network associated with knowledge of actions, manipulable artifacts, abstract concepts, and concrete concepts. The cortical regions involved in semantic processing can be grouped into 3 broad categories: posterior multimodal and heteromodal association cortex, heteromodal prefrontal cortex, and medial limbic regions. The expansion of these regions in the human relative to the nonhuman primate brain may explain uniquely human capacities to use language productively, plan, solve problems, and create cultural and technological artifacts, all of which depend on the fluid and efficient retrieval and manipulation of semantic knowledge.

BrainNet Viewer: a network visualization tool for human brain connectomics.

Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

Cellular mechanisms of insulin resistance

Abstract: It is estimated that by the year 2020 there will be approximately 250 million people affected by type 2 diabetes mellitus worldwide (1). Although the primary factors causing this disease are unknown, it is clear that insulin resistance plays a major role in its development. Evidence for this comes from (a) the presence of insulin resistance 10–20 years before the onset of the disease (2, 3); (b) cross-sectional studies demonstrating that insulin resistance is a consistent finding in patients with type 2 diabetes (3–6); and (c) prospective studies demonstrating that insulin resistance is the best predictor of whether or not an individual will later become diabetic (2, 3). Here, I focus on some recent advances in our understanding of human insulin resistance that have been made using nuclear magnetic resonance spectroscopy (NMR). This technique takes advantage of the spin properties of the nuclei of certain isotopes, such as 1H, 13C, and 31P, which endow the isotopes with a magnetic component that can be used to measure the concentration of intracellular metabolites noninvasively and to assess biochemical differences between normal and diabetic subjects. Drawing on NMR studies from my laboratory and others, I first consider the control of glucose phosphorylation and transport in regulating muscle responses to insulin. I then turn to the effects of fatty acids on insulin responses, showing that commonly accepted models that attempt to explain the association of insulin resistance and obesity are incompatible with recent findings. Finally, I propose an alternative model that appears to fit these and other available data.