bioRxiv Subject Collection: Neuroscience's Journal
[Most Recent Entries]
[Calendar View]
Friday, August 2nd, 2024
| Time |
Event |
| 1:16a |
Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships
The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkits features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining [~]30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies. | | 1:16a |
What the Average Really Means: Dissociating Effect Size and Effect Prevalence using p-curve Mixtures
Most research in the behavioral sciences aims to characterize effects of interest using sample means intended to describe the "typical" person. A difference in means is usually construed as a size difference in an effect common across subjects. However, mean effect size varies with both within-subject effect size and population prevalence (proportion of population showing the effect) in compared groups or across conditions. Few studies consider how prevalence affects mean effect size measurements and existing estimators of prevalence are, conversely, confounded by uncertainty about within-subject power. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size. Our approach outperforms existing prevalence estimation methods when within-subject power is uncertain and is sensitive to differences in prevalence or effect size across groups or experimental conditions. We present examples, extracting novel insights from existing datasets, and provide a user-facing software tool. | | 9:46a |
Spatial 3D genome organization controls the activity of bivalent chromatin during human neurogenesis
The nuclear genome is spatially organized into a three-dimensional (3D) architecture by physical association of large chromosomal domains with subnuclear compartments including the nuclear lamina at the radial periphery and nuclear speckles within the nucleoplasm1-5. However, how spatial genome architecture regulates human brain development has been overlooked owing to technical limitations. Here, we generate high-resolution maps of genomic interactions with the lamina and speckles in cells of the neurogenic lineage isolated from midgestational human cortex, uncovering an intimate association between subnuclear genome compartmentalization, chromatin state and transcription. During cortical neurogenesis, spatial genome organization is extensively remodeled, relocating hundreds of neuronal genes from the lamina to speckles including key neurodevelopmental genes bivalent for H3K27me3 and H3K4me3. At the lamina, bivalent genes have exceptionally low expression, and relocation to speckles enhances resolution of bivalent chromatin to H3K4me3 and increases transcription >7-fold. We further demonstrate that proximity to the nuclear periphery - not the presence of H3K27me3 - is the dominant factor in maintaining the lowly expressed, poised state of bivalent genes embedded in the lamina. In addition to uncovering a critical role of subnuclear genome compartmentalization in neurogenic transcriptional regulation, our results establish a new paradigm in which knowing the spatial location of a gene is necessary to understanding its epigenomic regulation. | | 9:46a |
Direct imaging of neural activity reveals neural circuits via spatiotemporal activation mapping
Two years ago, our group reported direct imaging of neuronal activity (DIANA), a functional magnetic resonance imaging (fMRI) technique that directly detects neuronal activity at high spatiotemporal resolution. In this study, we successfully reproduced the DIANA response in medetomidine-anesthetized mice using forelimb electrical stimulation at 11.7 T. More importantly, we showed that multiple neural circuits can be effectively revealed by DIANA fMRI through spatiotemporal activation mapping. The spatiotemporal activation mapping proposed here utilizes the temporal information of the DIANA response, that is, the time when the DIANA response reaches its peak, which is a unique feature that distinguishes it from the activation mapping method used in existing fMRI. Based on DIANA activation areas, we identified several neural circuits involved in forelimb sensory processing in the somatosensory network, which includes multiple brain regions: ventral posterolateral nucleus of the thalamus (VPL), posteromedial thalamic nucleus (POm), forelimb primary somatosensory cortex (S1FL), secondary somatosensory cortex (S2), primary motor cortex (M1), and secondary motor cortex (M2). Additionally, we also identified a pain-related neural circuit involving brain regions of the anterior cingulate cortex (ACC) and mediodorsal nucleus (MD). Interestingly, the spatiotemporal activation mapping also allowed us to identify subregions with different DIANA response times within the same functional region (e.g., VPL, POm, S1FL, and S2). Our study highlights the potential of DIANA fMRI to advance our understanding of sensory information processing throughout the brain and to provide insight into the spatiotemporal dynamics of brain networks at the level of neural circuits. | | 9:46a |
Assessing the Impact of Selective Attention on the Cortical Tracking of the Speech Envelope in the Delta and Theta Frequency Bands and How Musical Training Does (Not) Affect It
Oral communication regularly takes place amidst background noise, requiring the ability to selectively attend to a target speech stream. Musical training has been shown to be beneficial for this task. Regarding the underlying neural mechanisms, recent studies showed that the speech envelope is tracked by neural activity in the auditory cortex, which plays a role in the neural processing of speech, including speech in noise. The neural tracking occurs predominantly in two frequency bands, the delta and the theta band. However, much regarding the specifics of these neural responses, as well as their modulation through musical training, still remain unclear. Here, we investigated the delta- and theta-band cortical tracking of the speech envelope of attended and ignored speech using magnetoencephalography (MEG) recordings. We thereby assessed both musicians and non-musicians to explore potential differences between these groups. The cortical speech tracking was quantified through source-reconstructing the MEG data and subsequently relating the speech envelope in a certain frequency band to the MEG data using linear models. We thereby found the theta-band tracking to be dominated by early responses with comparable magnitudes for attended and ignored speech, whereas the delta band tracking exhibited both earlier and later responses that were modulated by selective attention. Almost no significant differences emerged in the neural responses between musicians and non-musicians. Our findings show that only the speech tracking in the delta but not in the theta band contributes to selective attention, but that this mechanism is essentially unaffected by musical training. |
|