Biology subjects

Fiecas, M.

Publications and source records attributed to Fiecas, M..

8 recordsLinked to original sources

bioRxiv2026

Fast Generalized Longitudinal Functional Principal Components Analysis with Applications to Population-Level Neuronal Dynamics

Functional data are widely collected across many fields. One approach for modeling functional data collected across repeated measures is longitudinal functional PCA (LFPCA), which allows for the decomposition of variability at multiple levels. However, existing estimation approaches for LFPCA can be computationally demanding for large or high-dimensional datasets. Here, we propose fast generalized LFPCA, a novel estimation technique that bins data along the functional domain and fits a marginal mixed model at each bin. Simulation results show that the proposed method is orders of magnitude faster than existing techniques while achieving similar accuracy, making LFPCA scalable to datasets with more densely sampled observations and larger sample sizes. We apply the proposed technique to model neuronal firing rate data both on and off deep brain stimulation (DBS) to investigate how the intervention modulates population-level neuronal activity. Our results suggest that the vast majority of between-neuron variation can be explained by four principal components, which relate to individual differences in neuronal firing as well as the arm manipulation paradigm under study. We also find that DBS significantly increases the variability of firing activity, likely driven by increased firing from a subset of neurons in response to DBS, a novel insight not obtained in previous analyses. Code to implement the proposed method and run simulation studies is included in the supplement.

neuroscience↗

bioRxiv2026

Spontaneous eye behavior is an objective marker of parkinsonian severity

Eye behavior disruption is reported in Parkinson's disease (PD), but its correlation with disease severity has not been well examined. Here, in a nonhuman primate model of PD, we show that spontaneous eye behavior is impaired in parkinsonism and correlates with clinical rating scores and subthalamic beta oscillatory activity. These findings suggest that spontaneous eye behavior could serve as an objective, non-invasive biomarker of parkinsonian severity.

neuroscience↗

bioRxiv2025

Cerebral cortical structures linked to intelligence

Understanding the neural basis of intelligence in humans remains an ongoing scientific pursuit. Early studies with small samples identified potential regions but lacked consistency across findings. Recent large-scale magnetic resonance imaging (MRI) datasets, using intelligence measures focused on verbal-numerical reasoning, now offer more robust opportunities for discovery. In this study (N=11,289), we showed the dorsolateral prefrontal cortex as exhibiting the strongest effect size and a significant causal relationship with intelligence, where larger surface area predicts higher intelligence as revealed by Mendelian randomization analyses. Additional regions, including the orbitofrontal and temporal cortices, also showed causal links to intelligence. These regions are critical for working memory, executive function, and language. Reverse causality analyses further indicated that higher intelligence contributes to increased total surface area and greater cortical thickness in the perisylvian language region. Our findings replicate prior evidence of a bidirectional relationship between total surface area and intelligence and further offer novel insights into regional cortical associations and causal effect. Collectively, these findings support a polyregional cortical configuration of intelligence, highlighting the dorsolateral prefrontal cortex--a key hub for cognitive ability.

neuroscience↗

bioRxiv2023

Excessive cortical beta oscillations are associated with slow-wave sleep dysfunction in mild parkinsonism

Increasing evidence associates slow-wave sleep (SWS) dysfunction with neurodegeneration. Using a within-subject design in the nonhuman primate model of Parkinsons disease (PD), we found that reduced SWS quantity in mild parkinsonism was accompanied by elevated beta and reduced delta power during SWS in the motor cortex. Our findings support excessive beta oscillations as a mechanism for SWS dysfunction and will inform development of neuromodulation therapies for enhancing SWS in PD.

neuroscience↗

bioRxiv2022

Deep Learning-based Feature Extraction with MRI Data in Neuroimaging Genetics for Alzheimer's Disease

The prognosis and treatment of the patients suffering from Alzheimers disease (AD) have been one of the most important and challenging problems over the last few decades. To better understand the mechanism of AD, it is of great interest to identify genetic variants associated with brain atrophy. Commonly in these analyses, neuroimaging features are extracted based on one of many possible brain atlases with FreeSurf and other popular softwares, which however may lose important information due to our incomplete knowledge about brain function embedded in these suboptimal atlases. To address the issue, we propose convolutional neural network (CNN) models applied to three-dimensional MRI data for the whole brain or multiple divided brain regions to perform completely data-driven and automatic feature extraction. These image-derived features are then used as endophenotypes in Genome-Wide Association Studies (GWAS) to identify associated genetic variants. When applied to the ADNI data, we identified several associated SNPs which have been previously shown to be related to several neurodegenerative/mental disorders such as AD, depression and schizophrenia. Code and supplementary materials are available at https://github.com/Dipnil07. The codes have been implemented using Python, R and Plink softwares.

bioinformatics↗

bioRxiv2022

CLEAN: Leveraging spatial autocorrelation in neuroimaging data in clusterwise inference

While clusterwise inference is a popular approach in neuroimaging that improves sensitivity, current methods do not account for explicit spatial autocorrelations because most use univariate test statistics to construct cluster-extent statistics. Failure to account for such dependencies could result in decreased reproducibility. To address methodological and computational challenges, we propose a new powerful and fast statistical method called CLEAN (Clusterwise inference Leveraging spatial Autocorrelations in Neuroimaging). CLEAN computes multivariate test statistics by modelling brain-wise spatial autocorrelations, constructs cluster-extent test statistics, and applies a refitting-free resampling approach to control false positives. We validate CLEAN using simulations and applications to the Human Connectome Project. This novel method provides a new direction in neuroimaging that paces with advances in high-resolution MRI data which contains a substantial amount of spatial autocorrelation.

neuroscience↗

bioRxiv2020

High-frequency oscillations in the internal globus pallidus: a pathophysiological biomarker in Parkinson's disease?

Abnormal oscillatory neural activity in the basal ganglia is thought to play a pathophysiological role in Parkinsons disease. Many patient studies have focused on beta frequency band (13-35 Hz) local field potential activity in the subthalamic nucleus, however increasing evidence points to alterations in neural oscillations in high frequency ranges (>100 Hz) having pathophysiological relevance. Prior studies have found that power in subthalamic high frequency oscillations (HFOs) is positively correlated with dopamine tone and increased during voluntary movements, implicating these brain rhythms in normal basal ganglia function. Contrary to this idea, in the current study we present a combination of clinical and preclinical data that support the hypothesis that HFOs in the internal globus pallidus (GPi) are a pathophysiological feature of Parkinsons disease. Spontaneous and movement-related pallidal field potentials were recorded from deep brain stimulation (DBS) leads targeting the GPi in five externalized Parkinsons disease patients, on and off dopaminergic medication. We identified a prominent oscillatory peak centered at 200-300 Hz in the off-medication rest recordings in all patients. High frequency power increased during movement, and the magnitude of modulation was negatively correlated with bradykinesia. Moreover, high frequency oscillations were significantly attenuated in the on-medication condition, suggesting they are a feature of the parkinsonian condition. To further confirm that GPi high frequency oscillations are characteristic of dopamine depletion, we also collected field potentials from DBS leads chronically implanted in three rhesus monkeys before and after the induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine (MPTP). High frequency oscillations and their modulation during movement were not prominent in the normal condition but emerged in the parkinsonian condition in the monkey model. These data provide the first evidence demonstrating that exaggerated, movement-modulated high frequency oscillations in the internal globus pallidus are a pathophysiological feature of Parkinsons disease, and motivate additional investigations into the functional roles of high frequency neural oscillations across the basal ganglia-thalamocortical motor circuit and their relationship to motor control in normal and diseased states. These findings also provide rationale for further exploration of these signals for electrophysiological biomarker-based device programming and stimulation strategies in patients receiving deep brain stimulation therapy.

neuroscience↗

bioRxiv2017

A Longitudinal Model for Functional Connectivity Using Resting-State fMRI

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current network modeling literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC network model using a variance components approach. First, for all subjects visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the FC network, and 3) the FC networks longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in baseline FC and change in FC over time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimers Disease Neuroimaging Initiative database.

neuroscience↗