Biology subjects

Wassermann, D.

Publications and source records attributed to Wassermann, D..

8 recordsLinked to original sources

bioRxiv2026

What Transfers Across Brains Is Not What Stays Within Individuals: Evidence from Bilingual Language States

Cognitive neuroscience often seeks brain activity that is consistent across people. Individual differences in brain organization have received less attention. Bilinguals, in particular, remain understudied at the individual level, as neuroimaging has mostly approached them as a group. We analyzed functional magnetic resonance imaging (fMRI) data from 77 Chinese-English bilinguals. We decoded each person's current language state, Chinese or English, from cortex-wide activity and assessed cross-person transfer across participant pairs. We identified a low-dimensional population representation, derived outside each tested pair, that was sufficient to support transfer, with performance nearly matching that of the full cortical pattern. Variation in this transfer tracked how early participants had learned English. Removing these dimensions barely changed within person accuracy, but transfer progressively declined until it collapsed. Our findings reveal two levels of organization: a common component that supports transfer between brains and an individual component that remains reliably stable within a person across sessions.

neuroscience↗

bioRxiv2026

Whole-brain modeling of dynamic causal circuits in human cognition using amortized variational inference

Understanding dynamic mechanisms underlying cognition remains a major challenge in human neuroscience. Here, we develop, validate, and apply Multivariate Dynamical Systems Identification with Amortized Variational Inference (MDSI-AVI), a novel computational framework designed to address critical challenges in capturing asymmetric, context-dependent, whole-brain directed interactions while accounting for regional hemodynamic response variability in fMRI data. MDSI-AVI leverages simulation-based inference through forward and reverse variational inference to address the limitations of conventional variational methods in high-dimensional settings. By averaging over uncertainty in hemodynamic response parameters using forward simulation, MDSI-AVI provides well-calibrated posteriors of directed connectivity that scale efficiently to networks with hundreds of nodes. Applied to Human Connectome Project data (N=728), MDSI-AVI reveals new insights into working memory mechanisms, identifying the dorsal anterior insula as a critical hub influencing activity at the whole-brain level. We demonstrate task-dependent modulation of causal influences, where the salience network drives frontoparietal network activity, which differentially influences the default mode and sensorimotor networks depending on working memory load. These whole-brain causal interactions distinguish task conditions with high accuracy and predict working memory performance. Our framework demonstrates reproducible results across whole-brain parcellations, establishing MDSI-AVI as a robust tool for advancing our understanding of circuit dynamics in cognition and disease.

neuroscience↗

bioRxiv2025

NeuroConText: Contrastive Learning for Neuroscience Meta-Analysis with Rich Text Representation

Brain meta-analysis is the common way to gather information about human brain function across the existing literature in order to formulate hypotheses and contextualize new findings. However, automated meta-analysis tools face challenges such as inconsistent terminology and difficulties in analyzing long texts and capturing semantic meaning because they still rely on bag-of-words approaches; furthermore, sparse coordinate reporting in articles distorts the activation distribution due to incomplete data. This paper introduces NeuroConText, a predictive text-to-brain modeling framework designed to support brain meta-analysis by bridging neuroscience text, brain location coordinates, and brain images within a shared latent space. This framework follows the predictive brain meta-analysis paradigm: it learns a regression from text descriptions to whole-brain activation maps and also enables the retrieval of relevant studies through contrastive learning, optimizing a multi-objective loss that combines retrieval and reconstruction objectives. Furthermore, NeuroConText supports second-level statistical synthesis by providing activation associated with top-K retrieved studies that can serve as input to coordinate-based meta-analysis (CBMA) methods. NeuroConText also leverages large language models (LLMs) to capture neuroscientific information from full-text articles, plus an LLM-based text augmentation strategy to handle short-text inputs. Quantitative and qualitative analyses demonstrate NeuroConTexts ability to enhance text-to-brain retrieval performance and reconstruct brain maps from neuroscience texts. We also show that predictive brain meta-analysis tools can infer brain activations in regions discussed in articles but absent in reported coordinates, potentially addressing the challenge of sparse coordinate reporting.

neuroscience↗

bioRxiv2024

A Comparative Machine Learning Study of Connectivity-Based Biomarkers of Schizophrenia

Functional connectivity holds promise as a biomarker of psychiatric disorders. Yet, its high dimensionality, combined with small sample sizes in clinical research, increases the risk of overfitting when the aim is prediction. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw connectivity. Our study evaluates which connectome features -- functional connectivity, gradients, or gradient dispersion -- best identify schizophrenia. Figure 1 summarizes this work. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=41 SRC="FIGDIR/small/573898v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@12c0fdorg.highwire.dtl.DTLVardef@13c4702org.highwire.dtl.DTLVardef@59f8d7org.highwire.dtl.DTLVardef@e00f56_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Overview of the methods and main outcome of the paper. Schematic images: Flaticon.com. NC: neurotypical controls, SCZ: patients with schizophrenia. C_FIG Surprisingly, our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia. Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions.

neuroscience↗

bioRxiv2022

Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming

Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis. However, existing meta-analysis tools are limited to investigating simple neuroscience concepts and expressing a restricted range of questions. Here, we expand the scope of neuroimaging meta-analysis by designing NeuroLang: a domain-specific language to express and test hypotheses using probabilistic first-order logic programming. By leveraging formalisms found at the crossroads of artificial intelligence and knowledge representation, NeuroLang provides the expressivity to address a larger repertoire of hypotheses in a meta-analysis, while seamlessly modelling the uncertainty inherent to neuroimaging data. We demonstrate the languages capabilities in conducting comprehensive neuroimaging meta-analysis through use-case examples that address questions of structure-function associations. Specifically, we infer the specific functional roles of three canonical brain networks, support the role of the visual word-form area in visuospatial attention, and investigate the heterogeneous organization of the fronto-parietal control network.

neuroscience↗

bioRxiv2022

Functional gradients in the human lateral prefrontal cortex revealed by a comprehensive coordinate-based meta-analysis

The human lateral prefrontal cortex (LPFC) enables flexible goal-directed behavior. Yet, its organizing principles remain actively debated despite decades of research. Meta-analysis efforts to map the LPFC have either been restricted in scope or suffered from limited expressivity in meta-analysis tools. The latter short-coming hinders the complexity of questions that can be expressed in a meta-analysis and hence limits the specificity of structure-function associations. Here, we adopt NeuroLang, a novel approach to meta-analysis based on first-order probabilistic logic programming, to infer the organizing principles of the LPFC with greater specificity from 14,371 neuroimaging publications. Our results reveal a rostrocaudal and a dorsoventral gradient, respectively explaining the most and second-most variance in whole-brain meta-analytic connectivity in the LPFC. Moreover, we find a cross-study agreement on a spectrum of increasing abstraction from caudal to rostral LPFC both in specific network connectivity and structure-function associations that supports a domain-general role for the mid-LPFC. Furthermore, meta-analyzing inter-hemispheric asymmetries along the rostrocaudal gradient reveals specific associations with topics of language, memory, response inhibition, and error processing. Overall, we provide a comprehensive mapping of the organizing principles of task-dependent activity in the LPFC, grounding future hypothesis generation on a quantitative overview of past findings.

neuroscience↗

bioRxiv2020

Bridging the Gap: From Neuroanatomical Knowledge to Tractography of Brain Pathways

Despite recent advances in tractography, the gap remains wide between the descriptions of white-matter pathways in the literature and the methods to reconstruct and study them from dMRI images. Here, we tackle this challenge by proposing a language to define white matter tracts, namely WMQLT, and a tool to automatically reconstruct pathways from their WMQLT queries. Our method is performant, flexible enough to allow defining tracts using multiple modalities, and allows to extend ROI-based reconstruction methods. Leveraging our language, we define 19 major brain tracts, alongside their subdivisions, and reconstruct them in a large population. We show that the shape of the reconstructed pathways, as well as their connectivity and lateralizations are in accordance with the current neuroanatomical literature. Finally, we showcase our technique in two scenarios: computing the functional subdivisions of a tract, and assessing the role of handedness and gender in the lateralization of language-related tracts.

neuroscience↗

bioRxiv2019

Quantitative modeling links in vivo microstructural and macrofunctional organization of human and macaque insular cortex, and predicts cognitive control abilities

The human insular cortex is a heterogenous brain structure which plays an integrative role in guiding behavior. The cytoarchitectonic organization of the human insula has been investigated over the last century using postmortem brains but there has been little progress in noninvasive in vivo mapping of its microstructure and large-scale functional circuitry. Quantitative modeling of multi-shell diffusion MRI (dMRI) data from 440 HCP participants revealed that human insula microstructure differs significantly across its functionally defined dorsal anterior, ventral anterior, and posterior insula subdivisions that serve distinct cognitive and affective functions. The microstructural organization of the insula was mirrored in its functionally interconnected circuits with the anterior cingulate cortex that anchor the salience network, a system important for adaptive switching of cognitive control systems. Novel validation of the human insula findings came from quantitative dMRI modeling in macaques which revealed microstructural features consistent with known primate insula cytoarchitecture. Theoretical analysis and computer simulations, using realistic 3-dimensional models of neuronal morphology from postmortem tissue, demonstrated that dMRI signals reflect the cellular organization of cortical gray matter, and that these signals are sensitive to cell size and the presence of large neurons such as the von Economo neurons. Crucially, insular microstructural features were linked to behavior and predicted individual differences in cognitive control ability. Our findings open new possibilities for probing psychiatric and neurological disorders impacted by insular cortex dysfunction, including autism, schizophrenia, and fronto-temporal dementia.\n\nStatement of SignificanceThe human insular cortex is a heterogenous brain structure which plays an integrative role in identifying salient sensory, affective, and cognitive cues for guiding attention and behavior. It is also is one of the most widely activated brain regions in all of human neuroimaging research. Here we use novel quantitative tools with in vivo diffusion MRI in large group (N=440) of individuals to uncover several unique microstructural features of the human insula and its macrofunctional circuits. Crucially, microstructural properties of the insular cortex predicted human cognitive control abilities, in agreement with its crucial role in adaptive human behaviors. Our findings open new possibilities for probing psychiatric and neurological disorders impacted by insular dysfunction, including autism, schizophrenia, and fronto-temporal dementia.

neuroscience↗