Trajectories of self-reported fatigue following initiation of multiple sclerosis disease-modifying therapy.

BACKGROUND: We analysed the COMparison Between All immunoTherapies for Multiple Sclerosis (NCT03193866), a Swedish nationwide observational study in relapsing-remitting multiple sclerosis (RRMS), to identify trajectories of fatigue and their association with physical disability following start of disease-modifying therapy (DMT). METHODS: Using a group-modelling approach, we assessed trajectories of fatigue with the Fatigue Scale for Motor and Cognitive Functions and physical disability with Expanded Disability Status Scale among 1587 and 1818 individuals who initiated a first DMT and had a first DMT switch, respectively, followed during 2011-2022. We investigated predictors of fatigue trajectories using group membership as a multinomial outcome and calculated conditional probabilities linking membership across the trajectories. RESULTS: We identified five trajectories of fatigue in participants who initiated their first DMT: no fatigue (mean starting values=23.7; 18.2% of population), low (35.5; 23.9%), mild (49.0; 21.6%), moderate (61.3; 20.1%) and severe (78.7; 16.1%). While no, low, mild and severe fatigue trajectories remained stable, the moderate trajectory increased to severe fatigue. Similarly, we identified six fatigue trajectories among participants who did a DMT switch, all indicating stable values over time. Women initiating a first DMT were more likely than men to display a severe fatigue trajectory, relative to the no fatigue one. There was a strong association between fatigue and physical disability trajectories. CONCLUSIONS: In this cohort of people with actively treated RRMS, self-reported fatigue remained stable or increased over the years following DMT start. There was a strong association between fatigue and disability after DMT start.

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 visual and semantic information processing, and show that the brain gradually accumulates information on different levels before eventually reaching a plateau. The timing of this plateau point varies across individuals and feature models, indicating notable temporal variation in visual object recognition and semantic processing.

Semantic feature norms: a cross-method and cross-language comparison.

The ability to assign meaning to perceptual stimuli forms the basis of human behavior and the ability to use language. The meanings of things have primarily been probed using behavioral production norms and corpus-derived statistical methods. However, it is not known to what extent the collection method and the language being probed influence the resulting semantic feature vectors. In this study, we compare behavioral with corpus-based norms, across Finnish and English, using an all-to-all approach. To complete the set of norms required for this study, we present a new set of Finnish behavioral production norms, containing both abstract and concrete concepts. We found that all the norms provide largely similar information about the relationships of concrete objects and allow item-level mapping across norms sets. This validates the use of the corpus-derived norms which are easier to obtain than behavioral norms, which are labor-intensive to collect, for studies that do not depend on subtle differences in meaning between close semantic neighbors.

Processing of an Audiobook in the Human Brain Is Shaped by Cultural Family Background.

Perception of the same narrative can vary between individuals depending on a listener's previous experiences. We studied whether and how cultural family background may shape the processing of an audiobook in the human brain. During functional magnetic resonance imaging (fMRI), 48 healthy volunteers from two different cultural family backgrounds listened to an audiobook depicting the intercultural social life of young adults with the respective cultural backgrounds. Shared cultural family background increased inter-subject correlation of hemodynamic activity in the left-hemispheric Heschl's gyrus, insula, superior temporal gyrus, lingual gyrus and middle temporal gyrus, in the right-hemispheric lateral occipital and posterior cingulate cortices as well as in the bilateral middle temporal gyrus, middle occipital gyrus and precuneus. Thus, cultural family background is reflected in multiple areas of speech processing in the brain and may also modulate visual imagery. After neuroimaging, the participants listened to the narrative again and, after each passage, produced a list of words that had been on their minds when they heard the audiobook during neuroimaging. Cultural family background was reflected as semantic differences in these word lists as quantified by a word2vec-generated semantic model. Our findings may depict enhanced mutual understanding between persons who share similar cultural family backgrounds.

Supramodal Sentence Processing in the Human Brain: fMRI Evidence for the Influence of Syntactic Complexity in More Than 200 Participants.

This study investigated two questions. One is: To what degree is sentence processing beyond single words independent of the input modality (speech vs. reading)? The second question is: Which parts of the network recruited by both modalities is sensitive to syntactic complexity? These questions were investigated by having more than 200 participants read or listen to well-formed sentences or series of unconnected words. A largely left-hemisphere frontotemporoparietal network was found to be supramodal in nature, i.e., independent of input modality. In addition, the left inferior frontal gyrus (LIFG) and the left posterior middle temporal gyrus (LpMTG) were most clearly associated with left-branching complexity. The left anterior temporal lobe showed the greatest sensitivity to sentences that differed in right-branching complexity. Moreover, activity in LIFG and LpMTG increased from sentence onset to end, in parallel with an increase of the left-branching complexity. While LIFG, bilateral anterior temporal lobe, posterior MTG, and left inferior parietal lobe all contribute to the supramodal unification processes, the results suggest that these regions differ in their respective contributions to syntactic complexity related processing. The consequences of these findings for neurobiological models of language processing are discussed.

The neural representation of abstract words may arise through grounding word meaning 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. We provide proof of concept that a statistical model of the semantic space can account for neural representations of both concrete and abstract words, using MEG. Here, we built a statistical model using word embeddings extracted from a text corpus. This statistical model was used to train a machine learning algorithm to successfully decode the MEG signals evoked by written words. In the model, word abstractness emerged from the statistical regularities of the language environment. Representational similarity analysis further showed that this salient property of the model co-varies, at 280-420 ms after visual word presentation, with activity in regions that have been previously linked with processing of abstract words, namely the left-hemisphere frontal, anterior temporal and superior parietal cortex. In light of these results, we propose that the neural encoding of word meanings can arise through statistical regularities, that is, through grounding in language itself.

Sensory Modality-Independent Activation of the Brain Network for Language.

The meaning of a sentence can be understood, whether presented in written or spoken form. Therefore, it is highly probable that brain processes supporting language comprehension are at least partly independent of sensory modality. To identify where and when in the brain language processing is independent of sensory modality, we directly compared neuromagnetic brain signals of 200 human subjects (102 males) either reading or listening to sentences. We used multiset canonical correlation analysis to align individual subject data in a way that boosts those aspects of the signal that are common to all, allowing us to capture word-by-word signal variations, consistent across subjects and at a fine temporal scale. Quantifying this consistency in activation across both reading and listening tasks revealed a mostly left-hemispheric cortical network. Areas showing consistent activity patterns included not only areas previously implicated in higher-level language processing, such as left prefrontal, superior and middle temporal areas, and anterior temporal lobe, but also parts of the control network as well as subcentral and more posterior temporal-parietal areas. Activity in this supramodal sentence-processing network starts in temporal areas and rapidly spreads to the other regions involved. The findings indicate not only the involvement of a large network of brain areas in supramodal language processing but also that the linguistic information contained in the unfolding sentences modulates brain activity in a word-specific manner across subjects.SIGNIFICANCE STATEMENT The brain can extract meaning from written and spoken messages alike. This requires activity of both brain circuits capable of processing sensory modality-specific aspects of the input signals as well as coordinated brain activity to extract modality-independent meaning from the input. Using traditional methods, it is difficult to disentangle modality-specific activation from modality-independent activation. In this work, we developed and applied a multivariate methodology that allows for a direct quantification of sensory modality-independent brain activity, revealing fast activation of a wide network of brain areas, both including and extending beyond the core network for language.

Toward Robust Functional Neuroimaging Genetics of Cognition.

A commonly held assumption in cognitive neuroscience is that, because measures of human brain function are closer to underlying biology than distal indices of behavior/cognition, they hold more promise for uncovering genetic pathways. Supporting this view is an influential fMRI-based study of sentence reading/listening by Pinel et al. (2012), who reported that common DNA variants in specific candidate genes were associated with altered neural activation in language-related regions of healthy individuals that carried them. In particular, different single-nucleotide polymorphisms (SNPs) of FOXP2 correlated with variation in task-based activation in left inferior frontal and precentral gyri, whereas a SNP at the KIAA0319/TTRAP/THEM2 locus was associated with variable functional asymmetry of the superior temporal sulcus. Here, we directly test each claim using a closely matched neuroimaging genetics approach in independent cohorts comprising 427 participants, four times larger than the original study of 94 participants. Despite demonstrating power to detect associations with substantially smaller effect sizes than those of the original report, we do not replicate any of the reported associations. Moreover, formal Bayesian analyses reveal substantial to strong evidence in support of the null hypothesis (no effect). We highlight key aspects of the original investigation, common to functional neuroimaging genetics studies, which could have yielded elevated false-positive rates. Genetic accounts of individual differences in cognitive functional neuroimaging are likely to be as complex as behavioral/cognitive tests, involving many common genetic variants, each of tiny effect. Reliable identification of true biological signals requires large sample sizes, power calculations, and validation in independent cohorts with equivalent paradigms.SIGNIFICANCE STATEMENT A pervasive idea in neuroscience is that neuroimaging-based measures of brain function, being closer to underlying neurobiology, are more amenable for uncovering links to genetics. This is a core assumption of prominent studies that associate common DNA variants with altered activations in task-based fMRI, despite using samples (10-100 people) that lack power for detecting the tiny effect sizes typical of genetically complex traits. Here, we test central findings from one of the most influential prior studies. Using matching paradigms and substantially larger samples, coupled to power calculations and formal Bayesian statistics, our data strongly refute the original findings. We demonstrate that neuroimaging genetics with task-based fMRI should be subject to the same rigorous standards as studies of other complex traits.

Statistical models of morphology predict eye-tracking measures during visual word recognition.

We studied how statistical models of morphology that are built on different kinds of representational units, i.e., models emphasizing either holistic units or decomposition, perform in predicting human word recognition. More specifically, we studied the predictive power of such models at early vs. late stages of word recognition by using eye-tracking during two tasks. The tasks included a standard lexical decision task and a word recognition task that assumedly places less emphasis on postlexical reanalysis and decision processes. The lexical decision results showed good performance of Morfessor models based on the Minimum Description Length optimization principle. Models which segment words at some morpheme boundaries and keep other boundaries unsegmented performed well both at early and late stages of word recognition, supporting dual- or multiple-route cognitive models of morphological processing. Statistical models based on full forms fared better in late than early measures. The results of the second, multi-word recognition task showed that early and late stages of processing often involve accessing morphological constituents, with the exception of short complex words. Late stages of word recognition additionally involve predicting upcoming morphemes on the basis of previous ones in multimorphemic words. The statistical models based fully on whole words did not fare well in this task. Thus, we assume that the good performance of such models in global measures such as gaze durations or reaction times in lexical decision largely stems from postlexical reanalysis or decision processes. This finding highlights the importance of considering task demands in the study of morphological processing.

A 204-subject multimodal neuroimaging dataset to study language processing.

This dataset, colloquially known as the Mother Of Unification Studies (MOUS) dataset, contains multimodal neuroimaging data that has been acquired from 204 healthy human subjects. The neuroimaging protocol consisted of magnetic resonance imaging (MRI) to derive information at high spatial resolution about brain anatomy and structural connections, and functional data during task, and at rest. In addition, magnetoencephalography (MEG) was used to obtain high temporal resolution electrophysiological measurements during task, and at rest. All subjects performed a language task, during which they processed linguistic utterances that either consisted of normal or scrambled sentences. Half of the subjects were reading the stimuli, the other half listened to the stimuli. The resting state measurements consisted of 5 minutes eyes-open for the MEG and 7 minutes eyes-closed for fMRI. The neuroimaging data, as well as the information about the experimental events are shared according to the Brain Imaging Data Structure (BIDS) format. This unprecedented neuroimaging language data collection allows for the investigation of various aspects of the neurobiological correlates of language.

Publisher Correction: A 204-subject multimodal neuroimaging dataset to study language processing.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Reconstructing meaning from bits of information.

Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., "dog" would include "barks"). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact 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 demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.

How the brain makes sense beyond the processing of single words - An MEG study.

Human language processing involves combinatorial operations that make human communication stand out in the animal kingdom. These operations rely on a dynamic interplay between the inferior frontal and the posterior temporal cortices. Using source reconstructed magnetoencephalography, we tracked language processing in the brain, in order to investigate how individual words are interpreted when part of sentence context. The large sample size in this study (n = 68) allowed us to assess how event-related activity is associated across distinct cortical areas, by means of inter-areal co-modulation within an individual. We showed that, within 500 ms of seeing a word, the word's lexical information has been retrieved and unified with the sentence context. This does not happen in a strictly feed-forward manner, but by means of co-modulation between the left posterior temporal cortex (LPTC) and left inferior frontal cortex (LIFC), for each individual word. The co-modulation of LIFC and LPTC occurs around 400 ms after the onset of each word, across the progression of a sentence. Moreover, these core language areas are supported early on by the attentional network. The results provide a detailed description of the temporal orchestration related to single word processing in the context of ongoing language.

Information properties of morphologically complex words modulate brain activity during word reading.

Neuroimaging studies of the reading process point to functionally distinct stages in word recognition. Yet, current understanding of the operations linked to those various stages is mainly descriptive in nature. Approaches developed in the field of computational linguistics may offer a more quantitative approach for understanding brain dynamics. Our aim was to evaluate whether a statistical model of morphology, with well-defined computational principles, can capture the neural dynamics of reading, using the concept of surprisal from information theory as the common measure. The Morfessor model, created for unsupervised discovery of morphemes, is based on the minimum description length principle and attempts to find optimal units of representation for complex words. In a word recognition task, we correlated brain responses to word surprisal values derived from Morfessor and from other psycholinguistic variables that have been linked with various levels of linguistic abstraction. The magnetoencephalography data analysis focused on spatially, temporally and functionally distinct components of cortical activation observed in reading tasks. The early occipital and occipito-temporal responses were correlated with parameters relating to visual complexity and orthographic properties, whereas the later bilateral superior temporal activation was correlated with whole-word based and morphological models. The results show that the word processing costs estimated by the statistical Morfessor model are relevant for brain dynamics of reading during late processing stages.

Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon.

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole-word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole-word model. The results support dual-route models that assume that both decomposed and full-form representations are utilized to optimally process complex words within the mental lexicon.

Frequency-specific directed interactions in the human brain network for language.

The brain's remarkable capacity for language requires bidirectional interactions between functionally specialized brain regions. We used magnetoencephalography to investigate interregional interactions in the brain network for language while 102 participants were reading sentences. Using Granger causality analysis, we identified inferior frontal cortex and anterior temporal regions to receive widespread input and middle temporal regions to send widespread output. This fits well with the notion that these regions play a central role in language processing. Characterization of the functional topology of this network, using data-driven matrix factorization, which allowed for partitioning into a set of subnetworks, revealed directed connections at distinct frequencies of interaction. Connections originating from temporal regions peaked at alpha frequency, whereas connections originating from frontal and parietal regions peaked at beta frequency. These findings indicate that the information flow between language-relevant brain areas, which is required for linguistic processing, may depend on the contributions of distinct brain rhythms.

Neural activity during sentence processing as reflected in theta, alpha, beta, and gamma oscillations.

We used magnetoencephalography (MEG) to explore the spatiotemporal dynamics of neural oscillations associated with sentence processing in 102 participants. We quantified changes in oscillatory power as the sentence unfolded, and in response to individual words in the sentence. For words early in a sentence compared to those late in the same sentence, we observed differences in left temporal and frontal areas, and bilateral frontal and right parietal regions for the theta, alpha, and beta frequency bands. The neural response to words in a sentence differed from the response to words in scrambled sentences in left-lateralized theta, alpha, beta, and gamma. The theta band effects suggest that a sentential context facilitates lexical retrieval, and that this facilitation is stronger for words late in the sentence. Effects in the alpha and beta bands may reflect the unification of semantic and syntactic information, and are suggestive of easier unification late in a sentence. The gamma oscillations are indicative of predicting the upcoming word during sentence processing. In conclusion, changes in oscillatory neuronal activity capture aspects of sentence processing. Our results support earlier claims that language (sentence) processing recruits areas distributed across both hemispheres, and extends beyond the classical language regions.

Producing speech with a newly learned morphosyntax and vocabulary: an magnetoencephalography study.

Ten participants learned a miniature language (Anigram), which they later employed to verbally describe a pictured event. Using magnetoencephalography, the cortical dynamics of sentence production in Anigram was compared with that in the native tongue from the preparation phase up to the production of the final word. At the preparation phase, a cartoon image with two animals prompted the participants to plan either the corresponding simple sentence (e.g., "the bear hits the lion") or a grammar-free list of the two nouns ("the bear, the lion"). For the newly learned language, this stage induced stronger left angular and adjacent inferior parietal activations than for the native language, likely reflecting a higher load on lexical retrieval and STM storage. The preparation phase was followed by a cloze task where the participants were prompted to produce the last word of the sentence or word sequence. Production of the sentence-final word required retrieval of rule-based inflectional morphology and was accompanied by increased activation of the left middle superior temporal cortex that did not differ between the two languages. Activation of the right temporal cortex during the cloze task suggested that this area plays a role in integrating word meanings into the sentence frame. The present results indicate that, after just a few days of exposure, the newly learned language harnesses the neural resources for multiword production much the same way as the native tongue and that the left and right temporal cortices seem to have functionally different roles in this processing.

Long-term phonological learning begins at the level of word form.

Incidental learning of phonological structures through repeated exposure is an important component of native and foreign-language vocabulary acquisition that is not well understood at the neurophysiological level. It is also not settled when this type of learning occurs at the level of word forms as opposed to phoneme sequences. Here, participants listened to and repeated back foreign phonological forms (Korean words) and new native-language word forms (Finnish pseudowords) on two days. Recognition performance was improved, repetition latency became shorter and repetition accuracy increased when phonological forms were encountered multiple times. Cortical magnetoencephalography responses occurred bilaterally but the experimental effects only in the left hemisphere. Superior temporal activity at 300-600 ms, probably reflecting acoustic-phonetic processing, lasted longer for foreign phonology than for native phonology. Formation of longer-term auditory-motor representations was evidenced by a decrease of a spatiotemporally separate left temporal response and correlated increase of left frontal activity at 600-1200 ms on both days. The results point to item-level learning of novel whole-word representations.

Differences in word recognition between early bilinguals and monolinguals: behavioral and ERP evidence.

We investigated the behavioral and brain responses (ERPs) of bilingual word recognition to three fundamental psycholinguistic factors, frequency, morphology, and lexicality, in early bilinguals vs. monolinguals. Earlier behavioral studies have reported larger frequency effects in bilinguals' nondominant vs. dominant language and in some studies also when compared to corresponding monolinguals. In ERPs, language processing differences between bilinguals vs. monolinguals have typically been found in the N400 component. In the present study, highly proficient Finnish-Swedish bilinguals who had acquired both languages during childhood were compared to Finnish monolinguals during a visual lexical decision task and simultaneous ERP recordings. Behaviorally, we found that the response latencies were overall longer in bilinguals than monolinguals, and that the effects for all three factors, frequency, morphology, and lexicality were also larger in bilinguals even though they had acquired both languages early and were highly proficient in them. In line with this, the N400 effects induced by frequency, morphology, and lexicality were larger for bilinguals than monolinguals. Furthermore, the ERP results also suggest that while most inflected Finnish words are decomposed into stem and suffix, only monolinguals have encountered high frequency inflected word forms often enough to develop full-form representations for them. Larger behavioral and neural effects in bilinguals in these factors likely reflect lower amount of exposure to words compared to monolinguals, as the language input of bilinguals is divided between two languages.