Persichetti Research

Highlighted Publications

Dilks, D.D., Kamps, F.S., Persichetti, A.S. Three cortical scene systems and their development. Trends Cogn Sci. 2022;26(2):117-127.

This paper proposes a novel theory that challenges a prevalent assumption that all three scene-selective regions in the human brain directly support navigation. Dr. Persichetti and colleagues, propose instead that cortical scene processing regions support three distinct computational goals (and one not for navigation at all): (i) The parahippocampal place area supports scene categorization, which involves recognizing the kind of place we are in; (ii) the occipital place area supports visually guided navigation, which involves finding our way through the immediately visible environment, avoiding boundaries and obstacles; and (iii) the retrosplenial complex supports map-based navigation, which involves finding our way from a specific place to some distant, out-of-sight place. They further hypothesize that these systems develop along different timelines, with both navigation systems developing slower than the scene categorization system

Persichetti, A.S., Avery, J.A., Huber, L., Merriam, E.P., Martin, A. Layer-Specific Contributions to Imagined and Executed Hand Movements in Human Primary Motor Cortex. Curr Biol. 2020;30(9):1721-1725.e3.

The human ability to imagine motor actions without executing them (i.e., motor imagery) is crucial to a number of cognitive functions, including motor planning and learning, and has been shown to improve response times and accuracy of subsequent motor actions. Although these behavioral findings suggest the possibility that imagined movements directly influence primary motor cortex (M1), how this might occur remains unknown. Dr. Persichetti and colleagues used a cutting-edge fMRI method to measure neural activations across cortical laminae in M1 while participants either tapped their thumb and forefinger together or simply imagined doing so. They found, whereas executed movements (i.e., finger tapping) evoked neural responses in both the superficial layers of M1 that receive cortical input and the deep layers of M1 that send output to the spinal cord to support movement, imagined movements evoked responses in superficial cortical layers only. Furthermore, finger tapping preceded by both imagined and executed movements showed a reduced response in the superficial layers (repetition suppression) coupled with a heightened response in the deep layers (repetition enhancement). Taken together, these results provide evidence for a mechanism whereby imagined movements can directly affect motor performance and might explain how neural repetition effects lead to improvements in behavior (e.g., repetition priming).

Shao, J., Gotts, S.J., Li, T.L., Martin, A., Persichetti, A.S. FunMaps: a method for parcellating functional brain networks using resting-state functional MRI data. Front Hum Neurosci. 2024;18:1461590.

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, Dr. Persichetti and colleagues developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps).

Persichetti, A.S., Shao, J., Gotts, S.J., Martin, A. A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization. Mol Psychiatry. 2025;30(4):1518-1528.

This paper used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in individuals with autism spectrum disorder (ASD) and a typically developing (TD) control group. A comparison of the maps from each group demonstrated that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

Persichetti, A.S., Shao, J., Denning, J.M., Gotts, S.J., Martin, A. Taxonomic structure in a set of abstract concepts. Front Psychol. 2024;14:1278744.

A large portion of human knowledge comprises “abstract” concepts that lack readily perceivable properties (e.g., “love” and “justice”). Since abstract concepts lack such properties, they have historically been treated as an undifferentiated category of knowledge in the psychology and neuropsychology literatures. More recently, the categorical structure of abstract concepts is often explored using paradigms that ask participants to make explicit judgments about a set of concepts along dimensions that are predetermined by the experimenter. Such methods require the experimenter to select dimensions that are relevant to the concepts and further that people make explicit judgments that accurately reflect their mental representations. Dr. Persichetti and colleagues bypassed these requirements by collecting two large sets of non-verbal and implicit judgments about which dimensions are relevant to the similarity between pairs of 50 abstract nouns to determine the representational space of the concepts. They then identified categories within the representational space using a clustering procedure that required categories to replicate across two independent data sets. In a separate experiment, they used automatic semantic priming to further validate the categories and to show that they are an improvement over categories that were defined within the same set of abstract concepts using explicit ratings along predetermined dimensions. These results demonstrate that abstract concepts can be characterized beyond their negative relation to concrete concepts and that categories of abstract concepts can be defined without using a priori dimensions for the concepts or explicit judgments from participants.