Biology subjects

Nieto-Castanon, A.

Publications and source records attributed to Nieto-Castanon, A..

7 recordsLinked to original sources

bioRxiv2026

The Incremental Cluster Threshold-Free Cluster Enhancement Algorithm for Functional Connectivity Analysis

Threshold-free cluster enhancement (TFCE) is one of the most used statistical inference methods in neuroimaging, but its computational cost limits some of its applications. The current implementations recompute clusters at each threshold step, creating computation costs that poorly scale with precision increases. Furthermore, as larger samples and reduced noise increase maximum t-statistics, computational burden grows correspondingly. As the field moves towards finer parcellations, the number of FC edges grows quadratically with the number of ROIs, making TFCE computationally infeasible at the scales increasingly demanded by the field. We present Incremental Cluster TFCE (IC-TFCE), an algorithm that produces numerically equivalent results to standard TFCE while decoupling runtime from discretization precision. The IC-TFCE builds clusters incrementally from previous threshold steps rather than recomputing them, stores TFCE results on a region of interest (ROI) based structure instead of a functional connectivity (FC) edge structure for improved speed, and can be applied to voxel data through a novel graph transformation described and validated herein. This algorithm achieves a measured 3-93x speedup for FC TFCE depending on the precision parameter dh, making TFCE analyses with fine parcellations of 1000 or more ROIs computationally tractable for the first time. Finally, we validate correctness through mathematical proof and numerical comparison. The efficiency provided by IC-TFCE allowed a large-scale empirical power analysis across dh values to guide practitioners in parameter selection for their analyses.

neuroscience↗

bioRxiv2025

Semantic reasoning takes place largely outside the language network

The brains language network is often implicated in the representation and manipulation of abstract semantic knowledge. However, this view is inconsistent with a large body of evidence suggesting that language processing is neurally distinct from the rest of cognition. Here, we use precision brain imaging to uncover a set of brain regions, separate from the language network, that are engaged in semantic processing of both linguistic and pictorial stimuli. In three fMRI experiments, participants (total n=41 tested across 49 sessions) viewed sentences and pictures depicting simple events. In separate blocks, they performed either a semantic task or a difficulty-matched perceptual task. Across all three experiments, several areas in left lateral prefrontal cortex, left temporo-parietal cortex, and right cerebellum responded to semantic tasks for both sentences and pictures. These semantic processing areas are spatially and functionally distinct from the nearby language-selective areas, as well as from the multiple demand and default mode networks, exhibiting a unique response profile. Our results provide evidence for a new kind of selectivity in the human brain and pave the way for future explorations of the neural mechanisms that underlie semantic reasoning.

neuroscience↗

bioRxiv2024

Precision fMRI reveals that the language network exhibits adult-like left-hemispheric lateralization by 4 years of age

In adults, left hemisphere (LH) damage often leads to aphasia, but many cases of early damage leave linguistic processing intact, with a functional language system developing in the right hemisphere. To explain this early apparent equipotentiality of the two hemispheres for language, some have proposed that the language system is more bilateral during early development and becomes increasingly left-lateralized with age. We examined language lateralization using fMRI in two large developmental cohorts (total n=273 children aged 4-16 years; n=107 adults). Strong, adult-like LH lateralization (in response magnitude and activation volume) was evident by age 4, although other features of the LH language network showed protracted development, including the magnitude of language response and the strength of functional connectivity. Thus, although the RH can take over language function in some cases of early brain damage, this plasticity occurs in spite of adult-level LH bias present by age 4 years.

neuroscience↗

bioRxiv2022

LanA (Language Atlas): A probabilistic atlas for the language network based on fMRI data from >800 individuals

Two analytic traditions characterize fMRI language research. One relies on averaging activations voxel-wise across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, a location in a common brain space cannot be meaningfully linked to function. An alternative approach relies on identifying language areas in each individual using a functional localizer. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. We provide a solution for bridging these currently disjoint approaches in the form of a probabilistic functional atlas created from fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common brain space belongs to the language network, and thus can help interpret group-level peaks and meta-analyses of such peaks, and lesion locations in patient investigations. More meaningful comparisons of findings across studies should increase robustness and replicability in language research.

neuroscience↗

bioRxiv2021

Resting State Functional Connectivity Predicts Future Changes in Sedentary Behavior

Information about a persons available energy resources is integrated in daily behavioral choices that weigh motor costs against expected rewards. It has been posited that humans have an innate attraction towards effort minimization and that executive control is required to overcome this prepotent disposition. With sedentary behaviors increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths, understanding the predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. In 64 healthy older adults participating in a 6-month aerobic exercise intervention, we use neuroimaging (resting state functional connectivity), baseline measures of executive function and accelerometer measures of time spent sedentary to predict future changes in objectively measured time spent sedentary in daily life. Using cross-validation and bootstrap resampling, our results demonstrate that functional connectivity between 1) the anterior cingulate cortex and the supplementary motor area and 2) the right anterior insula and the left temporoparietal/temporooccipital junction, predict changes in time spent sedentary, whereas baseline cognitive, behavioral and demographic measures do not. Previous research has shown activation in and between the anterior cingulate and supplementary motor area as well as in the right anterior insula during effort avoidance and tasks that integrate motor costs and reward benefits in effort-based decision making. Our results add important knowledge toward understanding mechanistic associations underlying complex sedentary behaviors.

neuroscience↗

bioRxiv2020

The neural circuitry underlying the "rhythm effect" in stuttering

PurposeStuttering is characterized by intermittent speech disfluencies which are dramatically reduced when speakers synchronize their speech with a steady beat. The goal of this study was to characterize the neural underpinnings of this phenomenon using functional magnetic resonance imaging. MethodData were collected from 17 adults who stutter and 17 adults who do not stutter while they read sentences aloud either in a normal, self-paced fashion or paced by the beat of a series of isochronous tones ("rhythmic"). Task activation and task-based functional connectivity analyses were carried out to compare neural responses between speaking conditions and groups. ResultsAdults who stutter produced fewer disfluent trials in the rhythmic condition than in the normal condition. While adults who do not stutter had greater activation in the rhythmic condition compared to the normal condition in regions associated with speech planning, auditory feedback control, and timing perception, adults who stutter did not have any significant changes. However, adults who stutter demonstrated increased functional connectivity between bilateral inferior cerebellum and bilateral orbitofrontal cortex as well as increased connectivity among cerebellar regions during rhythmic speech as compared to normal speech. ConclusionModulation of connectivity in the cerebellum and prefrontal cortex during rhythmic speech suggests that this fluency-inducing technique activates a compensatory timing system in the cerebellum and potentially modulates top-down motor control and attentional systems. These findings corroborate previous work associating the cerebellum with fluency in adults who stutter and indicate that the cerebellum may be targeted to enhance future therapeutic interventions.

neuroscience↗

bioRxiv2019

Reliability of single-subject neural activation patterns across speech production tasks

Speech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.

neuroscience↗