Biology subjects

van Vliet, M.

Publications and source records attributed to van Vliet, M..

13 recordsLinked to original sources

bioRxiv2026

Unstable word form representations and greater reliance on right hemisphere during word learning in adults with a history of developmental language disorder

Impairment in acquiring new vocabulary is a persistent deficit in developmental language disorder (DLD), but its cortical substrates remain unclear. We investigated learning of new words in young adults with DLD and typically developing (TD) control participants using magnetoencephalography (MEG). Participants were exposed to spoken real words and two sets of pseudowords, with shared beginnings, in two MEG sessions. One set of pseudowords was actively trained for three days, paired with fantasy animal pictures. Both groups learned new pseudowords, but adults with DLD recalled fewer items and confused similar sounding pseudowords more often than TD adults. MEG recordings revealed robust left temporal learning effects in both groups, along with left inferior frontal involvement. The DLD group additionally showed stronger right-hemispheric involvement and a significant right temporal learning effect for the newly learned words. Furthermore, only the DLD group showed changes in the left and right temporal responses for the real words after the learning phase, indicating altered lexical access for familiar words after integration of new word forms with overlapping phonology into the mental lexicon. Together, the findings suggest persisting problems in creating and accessing stable and distinctive phonological memory representations for word forms in adults with DLD, along with altered hemispheric balance in processing.

neuroscience↗

bioRxiv2026

When meaning becomes decodable: Linking the N400 evoked response to semantic representations

In non-invasive studies of the human brain, semantic processing during language comprehension has been extensively studied using the N400, a component of the electrophysiological evoked response that is strongly modulated by semantic context. More recently, a complementary approach has emerged that uses multivariate pattern analysis to perform neural decoding of semantic vectors from brain activity, operating on the basis that semantic vectors that more closely align with representations in the brain can be more accurately decoded. To consolidate these two approaches, we investigated the relationship between N400 modulation and semantic decoding performance using magnetoencephalography (MEG) in a controlled priming experiment. Twenty-five native speakers of Finnish read word triplets, for which the semantic relatedness between the two primes and the target word was manipulated based on distance in a word2vec embedding space (highly related, moderately related, or unrelated). We found that words presented after unrelated primes elicited higher N400 responses and provided the best examples for training a decoder to map distributed MEG responses to semantic vectors. Semantic information was decodable from approximately 100 to 500 ms after stimulus onset, at all three levels of contextual support. Soon after the N400 peak, neural responses no longer seemed to encode information that could be mapped to context-invariant semantic vectors. This suggests that the end of the N400 window may correspond to a turning point where the representation shifts from being word-specific to encoding the greater semantic context.

neuroscience↗

bioRxiv2026

Predictive coding narrows the gap between convolutional networks and human brain function in misspelled-word reading

Humans can readily recognize words even when they are misspelled, though with slower responses. We investigated whether predictive coding could be a feasible computational mechanism to explain both the robustness and the additional processing time. By incorporating brain-inspired predictive coding dynamics into a convolutional neural network (CNN), we assessed whether the resulting interplay between feed-back predictions and feed-forward errors enhanced the models brain-likeness in misspelled-word reading. The initial CNN was trained to classify images of rendered text from a 1000-word Finnish vocabulary (supervised), and then enhanced with feedback predictive coding connections, which were trained with the learning objective of reconstructing the activity in the previous layer (unsupervised). The model, with and without the predictive coding dynamics enabled, was then evaluated using the same real and misspelled word stimuli that were presented to human participants during a magnetoencephalography (MEG) recording. The predictive coding dynamics improved model performance on misspelled words, particularly reducing the accuracy gap between real and word-like misspelled words, thereby aligning overall performance more closely with human behavioral patterns. Furthermore, representational similarity analysis (RSA) and multivariate regression showed a stronger correspondence between model activations and human MEG responses when predictive coding dynamics were enabled. These findings provide converging evidence for predictive coding dynamics as a biologically plausible computational mechanism for the brains ability to cope with misspelled words. Author summaryWhen we read, we can often understand a word even if it is misspelled, e.g, "lamguage". Hence, computational models of visual word recognition in the brain should also exhibit such flexibility. Convolutional neural networks (CNNs) are gaining popularity as models of visual processing in the brain, including reading, yet they typically fail when faced with misspelled words. In our study, we enhanced a CNN by adding feedback connections that perform predictive coding, i.e. continuously attempt to reconstruct the input from the initial output and adjust the output until it can do so. Through this predictive coding loop, the model continuously refines its internal representations, mimicking how the brain may "clean up" noisy inputs. We found that this predictive coding mechanism not only allowed the model to better identify the closest real word form of misspelled words, but also produced activity patterns the more closely resembling those measured from human brain using magnetoencephalography. These results suggest that predictive coding could be a key computational principle underlying the brains remarkable flexibility in reading and provide insights into how biological mechanisms could inspire brain-like computational models.

neuroscience↗

bioRxiv2025

Dynamic feedforward and feedback influences on left ventral occipitotemporal cortex: evidence from word and pseudoword reading

Left ventral occipitotemporal cortex (vOT) is crucial in reading, yet its functional role is viewed either as a prelexical feedforward hub or a bidirectional interface between sensory and higher-order linguistic systems. The two competing views have usually been considered mutually exclusive. To comprehensively explain the functional complexity of left vOT, we investigated the temporal and spectral dynamics of information flows involving left vOT during visual word and pseudoword reading using magnetoencephalography (MEG) and two directed connectivity metrics, i.e., phase slope index (PSI) and Granger causality (GC). Specifically, feedforward connectivity from low-level visual areas to vOT was observed for all conditions, with the strength of orthographic information flow to left superior temporal cortex (ST) modulated by stimulus word-likeness. Conversely, feedback flow from left ST to vOT appeared for pseudowords that allow top-down linguistic constraints to facilitate reading, and occurred later for word-like than complete pseudowords. Our findings suggest that left vOT during word reading functions in a hybrid manner: operating in efficient feedforward mode for familiar word recognition while flexibly recruiting bidirectional processing for unfamiliar pseudowords. By disentangling feedforward and feedback dynamics with high temporal and spectral resolution, we empirically reconcile competing theories of vOT function.

neuroscience↗

bioRxiv2024

Polyglutamine expansion induced dynamic misfolding of Androgen Receptor

Spinal bulbar muscular atrophy (SBMA) is caused by a polyglutamine expansion (pQe) in the N-terminal transactivation domain of human androgen receptor (AR-NTD), resulting in a combination of toxic gain- and loss-of-function mechanisms. The structural basis of these processes has not been resolved due to the disordered nature of the NTD, which hinders experimental analyses of its detailed conformations. Here, using extensive computational modelling, we show that AR-NTD forms dynamic compact regions, which upon pQe re-organize dynamically, mediated partly by direct pQ interaction with the Androgen N-Terminal Signature (ANTS) motif. The altered dynamics of the NTD result in a perturbation of interdomain interactions, with potential implications for binding of the receptor protein to its response element. Oligomeric aggregation of the dynamic misfolded NTD exposes pQe, but blocks tau-5 and the FQNLF motif, which could lead to aberrant receptor transcriptional activity. These observations suggest a structural mechanism for AR dysfunction in SBMA.

pathology↗

bioRxiv2023

Test-retest reliability of MEG functional brain connectivity related to language processing

The number of studies examining changes in functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of dynamic connectivity related to language processing in a picture naming test-retest paradigm, using data collected from the same participants on two separate days. We determined the connections that were reliable across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0-400 ms and 400-800 ms after stimulus onset) and gamma connectivity decrease in the bilateral motor regions (800-1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with the behavioral performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language-related functional connectivity and introduces practices that may increase reliability. AUTHOR SUMMARYResearch applying connectivity metrics in neuroimaging has increased rapidly during recent years. Hence, the focus has also been to define the best methods for increasing the reliability of connectivity estimation. This study determined reliable functional connectivity from MEG data related to language processing. Moreover, we defined what makes a connection reliable by studying the behavioral, functional, and structural properties underlying the reliable connections.

neuroscience↗

bioRxiv2022

Cortical time-course of evidence accumulation during semantic processing

Our understanding of the surrounding world and communication with other people are tied to mental representations of concepts. In order for the brain to recognize an object, it must determine which concept to access based on information available from sensory inputs. In this study, we combine magnetoencephalography and machine learning to investigate how concepts are represented and accessed in the brain over time. Using brain responses from a silent picture naming task, we track the dynamics of semantic processing, and show that the brain gradually accumulates semantic information before eventually reaching a plateau. The timing of this plateau point varies across individuals, indicating differences in the temporal domain of semantic processing, a new dimension of individual variability in the faculty of language.

neuroscience↗

bioRxiv2022

A large-scale computational model to accurately predict early brain activity in response to written words

Neuroimaging studies have provided a wealth of information about when and where changes in brain activity might be expected during reading. We sought to better understand the computational steps that give rise to such task-related modulations of neural activity by using a convolutional neural network to model the macro-scale computations necessary to perform single-word recognition. We presented the model with stimuli that had been shown to human volunteers in an earlier magnetoencephalography (MEG) experiment and evaluated whether the same experimental effects could be observed in both brain activity and model. In a direct comparison between model and MEG recordings, the model accurately predicted the amplitude changes of three evoked MEG response components commonly observed during single-word reading. In contrast to traditional models of reading, our model directly operates on the pixel values of an image containing text. This allowed us to simulate the whole gamut of processing from the detection and segmentation of letter shapes to word-form identification, with the deep learning architecture facilitating inclusion of a large vocabulary of 10k Finnish words. Interestingly, the key to achieving the desired behavior was to use a noisy activation function for the units in the model as well as to obey word frequency statistics when repeating stimuli during training. We conclude that the deep learning techniques that revolutionized models of object recognition can also create models of reading that can be straightforwardly compared to neuroimaging data, which will greatly facilitate testing and refining theories on language processing in the brain.

neuroscience↗

bioRxiv2019

Post-hoc modification of linear models: combining machine learning with domain information to make solid inferences from noisy data

Linear machine learning models \"learn\" a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example dataset, which in neuroimaging studies is often notoriously noisy and small. In these cases, it is desirable to fine-tune the learned linear model using domain information beyond the example dataset. To this end, we present a framework that decomposes the weight matrix of a fitted linear model into three subcomponents: the data covariance, the identified signal of interest, and a normalizer. Inspecting these subcomponents in isolation provides an intuitive way to inspect the inner workings of the model and assess its strengths and weaknesses. Furthermore, the three subcomponents may be altered, which provides a straightforward way to inject prior information and impose additional constraints. We refer to this process as \"post-hoc modification\" of a model and demonstrate how it can be used to achieve precise control over which aspects of the model are fitted to the data through machine learning and which are determined through domain information. As an example use case, we decode the associative strength between words from electroencephalography (EEG) reading data. Our results show how the decoding accuracy of two example linear models (ridge regression and logistic regression) can be boosted by incorporating information about the spatio-temporal nature of the data, domain information about the N400 evoked potential and data from other participants.\n\nHighlightsO_LIWe present a framework to decompose any linear model into three subcomponents that are straightforward to interpret.\nC_LIO_LIBy modifying the subcomponents before re-assembling them into a linear model, prior information and further constraints may be injected into the model.\nC_LIO_LIAs an example, we boost the performance of a linear regressor and classifier by injecting knowledge about the spatio-temporal nature of the data, the N400 evoked potential and data from other participants.\nC_LI

neuroscience↗

bioRxiv2018

Reconstructing meaning from bits of information

We can easily identify a dog merely by the sound of barking or an orange by its citrus scent. In this work, we study the neural underpinnings of how the brain combines bits of information into meaningful object representations. Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of individual semantic features (e.g., \"barks\", \"has citrus scent\"). Here, participants received clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We used machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. We discovered that the recorded brain patterns were best decoded using a combination of not only the three semantic features that were presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to observe how fragmented information is combined into a complete semantic representation of an object and suggest neuroanatomical underpinnings for this process.

neuroscience↗

bioRxiv2018

Cracking the problem of neural representations of abstract words: grounding word meanings in language itself

In order to describe how humans represent meaning in the brain, one must be able to account for not just concrete words but, critically, also abstract words which lack a physical referent. Hebbian formalism and optimization are basic principles of brain function, and they provide an appealing approach for modeling word meanings based on word co-occurrences. Here, we built a model of the semantic space based on word statistics in a large text corpus, which was able to decode items from brain signals. In the model, word abstractness emerged from the statistical regularities of the language environment. This salient property of the model co-varied, at 280-420 ms after word presentation, with activity in the left-hemisphere frontal, anterior temporal and superior parietal cortex that have been linked with processing of abstract words. In light of these results, we propose that the neural encoding of word meanings is importantly grounded in language through statistical regularities.

neuroscience↗

bioRxiv2018

Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 \"familiar vs. unfamiliar vs. scrambled faces\" dataset. The goal is to educate both novice and experienced data analysts with the \"tricks of the trade\" necessary to successfully perform this type of analysis on their own data.

neuroscience↗

bioRxiv2017

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

bioinformatics↗