Biology subjects

Molfese, P. J.

Publications and source records attributed to Molfese, P. J..

6 recordsLinked to original sources

bioRxiv2026

Subjective, not objective, socialness drives activity in the brain's third visual pathway

Recent work, including the third visual stream hypothesis, frames social perception as a largely automatic visual process. Simple animations of shapes engaged in "social" behaviors such as chasing have long been used to study social perception; however, both behavioral and neural responses are typically modeled against discrete, experimenter-assigned stimulus labels. This approach is insufficient, given that perceptions of such stimuli vary not only between people but also across trials. Here, we directly compared the neural responses to objective (stimulus-based) and subjective (response-based) socialness in an fMRI study in which the degree of chase between two agents was varied algorithmically. Participants (n=24) rated their perception of a chase on a continuous scale. Subjective ratings better predicted activity in visuo-social brain regions, predominantly in area MT, even after controlling for optic flow. Our findings underscore the visual nature of social cognition and highlight the importance of analyses sensitive to idiosyncrasies in perception.

neuroscience↗

bioRxiv2025

Repetition-related reductions in neural activity support improved behavior through increases in oscillatory power

Repeatedly processing an object leads to subsequent behavioral improvements in its identification and reductions in associated neural activity, a form of neural processing efficiency that multiple theoretical models have attempted to explain. Using simultaneous fMRI-EEG in humans during object naming, we find that stimulus-driven oscillatory power from high (gamma) to low frequencies (theta) increases and resolves earlier with repetition in task-engaged frontal and occipitotemporal brain regions identified as showing repetition suppression in fMRI. Changes in gamma oscillatory power in these regions were correlated with behavioral priming across subjects and additionally with fMRI repetition suppression in left frontal cortex, providing multimodal support for a novel mixture of the previously proposed Synchrony and Facilitation models.

neuroscience↗

bioRxiv2020

The impact of second language immersion: Evidence from a bi-directional longitudinal cross-linguistic study

Brice et al. (2019) presented data from the first epoch of a longitudinal study of the neurobiological underpinnings of first-language (L1) and second-language (L2) processing. Results showed a similar network of activation for reading across L1 and L2, as well as significant convergence of print and speech processing across a network of left-hemisphere regions in both L1 and L2 with greater activation and convergence for L2 in anterior regions, and greater activation and convergence for L1 in posterior regions of the reading network. Here, we present the first look at longitudinal changes in these effects. L2 showed relatively few changes in activation, with some shifts in the weighting between ventral and dorsal processing. L1, however, showed more widespread differences in processing, suggesting that the neurobiological footprint of reading is dynamic, with both L1 and L2 impacting each other. Print/speech convergence showed very little longitudinal change, suggesting that it is a stable marker of the differences in L1 and L2 processing.

neuroscience↗

bioRxiv2020

Different activation signatures in the primary sensorimotor and higher-level regions for haptic three-dimensional curved surface exploration

Haptic object perception begins with continuous exploratory contacts, and the human brain needs to accumulate sensory information continuously over time. However, it is still unclear how the primary sensorimotor cortex (PSC) interacts with these higher-level regions during haptic exploration across time. This functional magnetic resonance imaging (fMRI) study investigates time-dependent haptic object processing by examining brain activity during haptic 3D curve and roughness estimation. For this experiment, we designed sixteen haptic stimuli (4 kinds of curve x 4 kinds of roughness) for the haptic curve and roughness estimation tasks. Twenty participants were asked to move their right index and middle fingers along with the surface twice and to estimate one of the two features--roughness or curvature--dependent on the task instruction. We found that the brain activity in several higher-level regions (e.g., bilateral posterior parietal cortex) linearly increased with curvature through the haptic exploration phase. Surprisingly, we found that the contralateral PSC was parametrically modulated by the number of curves only during the late exploration phase, but not during the early exploration phase. In contrast, we found no similar parametric modulation activity patterns for haptic roughness estimation in either the contralateral PSC or in the higher-level regions. Together, our findings suggest that haptic 3D object perception is processed across the cortical hierarchy, while the contralateral PSC interacts with other higher-level regions across time in a manner that is dependent upon object features. HighlightsO_LIWe observed the brain activity of haptic object perception using parametric stimuli. C_LIO_LIHaptic curve estimation showed parametric modulation across the cortical hierarchy. C_LIO_LICurve parametric modulation in the sensorimotor cortex showed time dependency. C_LIO_LIRoughness parametric modulation showed very little dependency in any regions of the brain. C_LIO_LIThese findings reflect the nature of time-dependent haptic object processing in the brain. C_LI

neuroscience↗

bioRxiv2020

Thalamus is a common locus of reading, arithmetic, and IQ: Analysis of local intrinsic functional properties

Neuroimaging studies of basic achievement skills, reading and arithmetic, often control for the effect of IQ to identify unique neural correlates of each skill. This may underestimate possible effects of common factors between achievement and IQ measures on neuroimaging results. Here, we simultaneously examined achievement (reading and arithmetic) and IQ measures in young adults, aiming to identify MRI correlates of their common factors. Resting-state fMRI (rs-fMRI) data were analyzed using two metrics assessing local intrinsic functional properties; regional homogeneity (ReHo) and fractional amplitude low frequency fluctuation (fALFF), measuring local intrinsic functional connectivity and intrinsic functional activity, respectively. ReHo highlighted the thalamus/pulvinar (a subcortical region implied for selective attention) as a common locus for both achievement skills and IQ. More specifically, the higher the ReHo values, the lower the achievement and IQ scores. For fALFF, the left superior parietal lobule, part of the dorsal attention network, was positively associated with reading and IQ. Collectively, our results highlight attention-related regions, particularly the thalamus/pulvinar as a key region related to individual differences in performance on all the three measures. ReHo in the thalamus/pulvinar may serve as a tool to examine brain mechanisms underlying a comorbidity of reading and arithmetic difficulties, which could co-occur with weakness in general intellectual abilities. HighlightsO_LIAchievement (reading; arithmetic) and IQ measures are simultaneously examined; not controlling for IQ. C_LIO_LIReHo and fALFF are used to examine local intrinsic functional connectivity and activity, respectively. C_LIO_LIHigher ReHo in thalamus/pulvinar is associated with lower performance on all the three measures. C_LIO_LIHigher fALFF in the left superior parietal lobule is associated with higher word reading and IQ. C_LI

neuroscience↗

bioRxiv2019

Untangling the Relatedness among Correlations, Part III: Inter-Subject Correlation Analysis through Bayesian Multilevel Modeling for Naturalistic Scanning

While inter-subject correlation (ISC) analysis is a powerful tool for naturalistic scanning data, drawing appropriate statistical inferences is difficult due to the daunting task of accounting for the intricate relatedness in data structure as well as the intricacy of handling the multiple testing issue. In our previous work we proposed nonparametric approaches to performing group ISC analysis through bootstrapping for one group of subjects and permutation testing for two groups (Chen et al., 2016). A more flexible methodology is to parametrically build a linear mixed-effects (LME) model that captures the relatedness embedded in the data (Chen et al., 2017); in addition, the LME approach also has the capability of incorporating explanatory variables such as subject-grouping factors and quantitative covariates. However, the whole-brain LME modeling methodology still faces some challenges. When an LME model becomes sophisticated, it becomes difficult or even impossible to assign accurate degrees of freedom for each testing statistic. In addition, the typical correction methods for multiple testing through spatial extent tend to be over-penalizing, and dichotomous decisions through thresholding under null hypothesis significance testing (NHST) are controversial in general and equally problematic in neuroimaging as well. For instance, the popular practice of only reporting \"statistically significant\" results in neuroimaging not only wastes data information, but also distorts the full results as well as perpetuates the reproducibility crisis because of the fact that the difference between a \"significant\" result and a \"non-significant\" one is not necessarily significant.\n\nHere we propose a Bayesian multilevel (BML) framework for ISC data analysis that integrates all the spatial elements (i.e., regions of interest) into one model. By loosely constraining the regions through a weakly informative prior, BML conservatively pools the effect of each region toward the center, and improves collective fitting and overall model performance. The BML paradigm leverages the commonality or similarity among brain regions and the information across multiple levels embedded in the hierarchical data structure instead of leveraging the spatial extent adopted in the conventional correction method for multiple testing. In addition to potentially achieving a higher inference efficiency than the conventional LME approach, BML improves spatial specificity and easily allows the investigator to adopt a philosophy of full results reporting (instead of dichotomizing into \"significant\" and \"non-significant\" results), thus minimizing loss of information while enhancing reproducibility. A dataset of naturalistic scanning is utilized to illustrate the modeling approach with 268 parcels and to showcase the modeling capability, flexibility and advantages in reports reporting. The associated program will be available as part of the AFNI suite for general use.

neuroscience↗