Biology subjects

Diedrichsen, J.

Publications and source records attributed to Diedrichsen, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Encoding of bimanual movement directions across the human sensorimotor system

Bimanual coordination requires the control of each hand to account for movements of the other hand. To enable such coordination, it has been hypothesized that the brain has a representation of bimanual movements that supersedes a linear combination of the unimanual movements. To test for such a representation, we mapped the encoding of movement direction during unimanual and bimanual wrist movements in the human brain using functional magnetic resonance imaging (fMRI). For unimanual movements, we found that both the contralateral and ipsilateral movement directions were represented across most cortical motor regions. The representations were similar for mirror-symmetric movements across the hands, revealing an intrinsic (body-centric) code in premotor and parietal regions. For bimanual movements, the neural representations of movement directions could be explained by three components. First, the contralateral movement direction was represented as in the unimanual condition, whereas the representation of the ipsilateral movement direction disappeared. Second, in higher-order regions (rostral premotor and posterior parietal cortex) we found relatively elevated activity when the two movements were directed in unrelated directions. This activity was likely caused by the extra effort to deal with incongruent spatial targets across the hands. Third, and independent of this congruency effect, we found a non-linear interaction between contra- and ipsilateral movement that encoded the specific bimanual movement combination across sensorimotor, premotor, and parietal regions. This representation was suited to facilitate bimanual coordination and learning.

neuroscience↗

bioRxiv2026

Cerebellar influences on neocortical development in humans and mice

The cerebellum has long been considered a late-maturing structure subordinate to neocortical development, therefore its potential role as an early driver of cortical organization remains largely unexplored. Using two large longitudinal neuroimaging cohorts of developing children together with lesion experiments in mice, we show that early cerebellar morphology may drive neocortical maturation in a regionally specific manner. These cross-species findings implicate the cerebellum as a possible regulator of neocortical organization.

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bioRxiv2026

Individualized parcellation reveals functional boundaries in human prefrontal cortex

Human prefrontal cortex (PFC) supports diverse higher-order cognitive functions during task execution. Increasing evidence suggests that these functions are organized along large-scale gradients, such as a rostro-caudal axis supporting progressively abstract-to-concrete information processing. At the same time, regional specialization within PFC, including focal patches selective for specific stimulus categories, suggests the presence of discrete functional boundaries. Whether PFC organization is best characterized by continuous gradients or discrete subdivisions remains unresolved. Here we show that task-evoked functional organization in PFC exhibits substantial inter-individual variability, limiting the usefulness of conventional group atlases in testing for functional boundaries. To address this challenge, we estimated individualized functional parcellations by combining a group atlas with task-evoked fMRI data spanning diverse cognitive tasks. The group atlas revealed large-scale functional gradients across PFC, whereas individualized parcellations additionally uncovered sharp functional boundaries obscured by group averaging. Moreover, functional organization in PFC was substantially more fine-grained than in other association cortices, consistent with its integrative role in cognitive control. Together, these findings suggest that PFC organization reflects an individualized mosaic of fine-grained functional subdivisions embedded within broader large-scale gradients, with important implications for defining core constructs such as the multiple-demand system.

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bioRxiv2026

Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion

Individual-specific cortical parcellations enable the characterization of brain network organization that is often obscured by population-level atlases, with broad implications for both basic neuroscience and translational applications. However, existing methods rely primarily on resting-state fMRI and underutilize task-evoked data, which provide complementary information about functional specialization. This limitation partly reflects the challenge of integrating heterogeneous datasets that differ in task design, sample size, and cortical coverage. Here, we present mRBM-HBP, a scalable hierarchical Bayesian framework that incorporates a multinomial restricted Boltzmann machine to model spatial dependencies, enabling efficient and flexible integration of resting-state and task fMRI across diverse datasets and inference of both group-level and individual-level cortical parcellations. We show that mRBM-HBP achieves performance comparable to state-of-the-art resting-state-based parcellation methods while substantially reducing computational cost. By integrating large-scale task-fMRI datasets, we derive a task-based parcellation and demonstrate that resting-state and task conditions reveal largely consistent macroscopic networks, while task data provide state-specific refinements of functional boundaries. Moreover, a fused rest-task group-level atlas improves the accuracy, reliability, and individual specificity of inferred parcellations, particularly when individual-level data are limited. These results indicate that integrating resting-state and task fMRI enhances precision mapping of functional brain organization.

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bioRxiv2026

SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan

The human cerebellum plays a central role in motor, emotional, and cognitive functions, and is implicated in many brain disorders. To improve the analysis of functional and anatomical imaging from the cerebellum, we introduce SUITPy, an improved and fully revised Python implementation of the widely used SUIT toolbox. For this new version, we developed a U-Net based model to automatically isolate the cerebellum from adjacent cortical tissue, which achieves higher fidelity than existing algorithms. The isolation works robustly without manual corrections for imaging data across the lifespan. We show that isolation and subsequent normalization to a cerebellum-only template lead to a more precise alignment of cerebellar structures across participants compared to normalization using a whole-brain template. We also show the utility of the cerebellar mask to prevent contamination of cerebellar functional data from surrounding cortical structures. The toolbox also provides functionality for visualizing cerebellar data on a flatmap, along with a range of anatomical and functional cerebellar atlases, thereby offering an essential tool that enables accurate cerebellar analysis across the lifespan.

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bioRxiv2026

Dissecting heterogeneous brain development and aging using voxelwise normative models

Brain disorders are often characterized by biological heterogeneity that is poorly captured by group-average analyses. Normative modeling has emerged as a promising tool to parse out such heterogeneity, yet existing lifespan reference models rely on coarse parcellations, which may obscure individual variability. Using an aggregated reference sample (n=58,597 scans from n=51,107 participants), we provide openly available normative models of brain morphometry at the voxel level across the lifespan, and we illustrate their potential utility with two complementary applications. First, we investigated long-term brain development after preterm birth across two independent cohorts (n=284; n=304) and found individualized, replicable and persistent brain alterations. Second, we extracted high-resolution patient-level morphometric deviations in two samples with rare, genetic neurodegenerative disorders (spinocerebellar ataxia type 1 and 3; n=29, n=15), which showed marked heterogeneity. Together, our findings highlight that voxelwise normative modeling can detect clinically relevant, individualized deviations from a reference model with high spatial precision.

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bioRxiv2026

Testing hypotheses about correlations between brain activation patterns

Many functional magnetic resonance imaging (fMRI) studies conclude that two conditions engage "overlapping, yet partly distinct" patterns of activation. Yet, there is currently no commonly accepted method for determining the extent of this overlap. While correlations between activation patterns can serve as a measure of their correspondence, empirical correlations are strongly biased towards zero due to measurement noise, preventing their use in testing hypotheses about the actual degree of pattern correspondence. In this paper, we derive the maximum-likelihood estimate for the correlation of the true (noise-less) activation patterns and examine its behavior in the low signal-to-noise regime that is typical for fMRI studies. We show that although the maximum-likelihood estimate corrects for much of the influence of measurement noise, it is ultimately biased. We examine different ways of drawing inferences about the size of the underlying true correlations. We find that a subject-wise bootstrap on the maximum-likelihood group estimate performs best over the tested conditions. We extend the proposed method to test more general hypotheses about the representational geometry of activation patterns for more conditions, and highlight best practices, as well as common pitfalls and problems, in testing such hypotheses.

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bioRxiv2026

Multi-Task Batteries for Precision Functional Mapping

Functional brain mapping is an important tool to understand the organization of the human brain, both at the group level, but also to an increasing degree at the level of the individual. There are currently two main approaches to do so. Resting-state fMRI relies on inter-regional correlations of random fluctuations of the signal. In contrast, task-based localizers typically use a single-contrast between a task of interest and a matched control task to identify the location of a functional region in an individual brain. In this paper, we propose and evaluate a third approach: the use of multi-task batteries for both localization of a single functional region and parcellation of multiple functional regions. We show that multi-task localizers produce more consistent estimation of a single functional region across subjects than the single-contrast approach using the same amount of fMRI data. Furthermore, we demonstrate that the multi-task approach is sensitive to true inter-individual differences in region size, and does not suffer the same influence of signal-to-noise ratio that biases the single-contrast localizer. We then address the question of how to select tasks for the battery, and present a data-driven strategy that optimizes the characterization of a brain structure of interest. We show that such batteries outperform randomly selected batteries both for building individual parcellations as well as individual connectivity models. Finally, we demonstrate that an interspersed design - where all tasks are presented in each imaging run - yields more reliable results than splitting the tasks across different runs. We present an open source toolbox for the implementation of multi-task batteries, along with a library containing group-averaged activity patterns that can be used to optimize battery selection for different brain structures of interest.

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bioRxiv2026

Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain

Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer the intrinsic functional organization of the brain, yet it remains unclear how well this approach can predict the structure of brain activity observed across a diverse set of mental states. Here we compare resting-state to task-based fMRI using diverse task batteries within the same individuals. We find that multi-task fMRI data consistently outperform resting-state estimates in predicting functional organization during novel tasks. This advantage persists across preprocessing strategies, brain regions, and independent datasets. While task activation estimates do show task-dependency when using only few tasks, increasing task diversity reduced task-specific bias, with convergence achieved using modest task sets. These improvements translate into superior individual parcellations and connectivity models. Together, our results dissociate reliability from validity in neuroimaging and challenge the prevailing assumption that rest provides a privileged window into intrinsic brain organization. Instead, functional architecture appears most faithfully revealed when the brain is actively driven through diverse task states.

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bioRxiv2026

Neural representations of speech production in neocortical and cerebellar regions

Speech production requires the coordinated control of the supralaryngeal articulators (e.g., the lips, tongue, and soft palate) together with laryngeal control of phonation. While cortical sensorimotor regions are known to encode phonetic features, how the cerebellum represents speech and how cerebellar representations integrate within cortico-cerebellar circuits remain poorly understood. Using 7T functional MRI and representational similarity analysis, we examined neural representations of speech production during overt syllable production varying in place of articulation and voice onset time. We found reliable, syllable-specific activity patterns across both cortical and cerebellar speech regions. Ventral primary sensorimotor cortex distinguished syllables by place of articulation, whereas dorsal sensorimotor cortex showed sensitivity to voice onset time, consistent with a functional dissociation between articulatory and phonatory control. We also found secondary speech areas that encoded phonetic features. Specifically, the parietal operculum showed sensitivity to both place of articulation and voice onset time, positioning it as an integrative node within the speech motor network. In the cerebellum we found superior and inferior speech areas, which did not differ in their representational geometry. Surprisingly, the cerebellar syllable representations were most similar to those found in the parietal operculum rather than in primary motor cortex, with both regions encoding both phonetic features. These findings indicate that cerebellar speech representations are not solely determined by a single primary sensorimotor area but instead align more closely with the representations in the parietal operculum, suggesting that cerebellar speech areas contribute to the coordination of phonation and articulation.

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bioRxiv2026

Sensory expectations and prediction error during feedback control in the human brain

External disturbances to the body are better counteracted when their nature can be predicted in advance. Here, we investigated the neural mechanisms through which probabilistic predictions shape feedback responses using functional magnetic resonance imaging (fMRI) in humans. We show that, prior to a mechanical perturbation applied to a finger, the primary motor (M1) and somatosensory (S1) cortices receive a signal that linearly encodes the expected sensory input. When perturbations reach these areas, expectations are combined with the sensory input through a simple additive mechanism, yielding motor commands that reflect a weighted sum of the two signals. At the same time, M1 and S1 receive a prediction error signal encoding the difference between expectations and actual sensory input. This signal is visible in fMRI data in humans and in the local field potentials in non-human primates, but not in M1 or S1 spiking activity.

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bioRxiv2025

Reverse Spatiotemporal Hierarchy during Cross-modal Memory Recall and Imagery

Recalling past events is often accompanied by mental imagery of those experiences. Based on previous research, this process engages memory- and sensory-related brain areas. However, the underlying spatiotemporal dynamics remain poorly investigated. Here, we used naturalistic videos of audiovisual events and recorded fMRI data during the tasks in which human participants recalled visual contents when hearing associated sounds and recalled sounds when watching silent videos, after they had well memorized the video contents. With time-resolved fMRI multivariate pattern analyses, we observed reverse spatiotemporal hierarchy during the visual memory recall and imagery: the neural activity in primary visual cortex was delayed compared with high-order visual areas. A similar pattern was found during auditory memory recall and imagery, where the bottom-up progression from the mid-level planum temporale to the high-level superior temporal gyrus observed during auditory perception was absent. However, the primary auditory area was not involved, suggesting modality differences in the role of primary sensory areas in corresponding memory recall. We also observed the activity of the hippocampus, the parahippocampal cortex, the retrosplenial cortex, and the precuneus and examined their temporal dynamics. Overall, our study provided both spatial and temporal accounts of neural activity during the cross-modal memory recall and imagery.

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bioRxiv2025

Sequence preparation is not always associated with a reaction time cost

The extent to which a sequence of movements is prepared before initiating the first movement is a longstanding question in motor neuroscience. The observation that reaction time (RT) increases for longer sequences has been used as evidence of sequence preparation - reflecting the additional demands of preparing multiple movements before initiating a sequential action. However, many processes contribute to RT, making it unclear whether the observed RT increases specifically reflect sequence preparation. For example, with longer sequences, participants face greater ambiguity in selecting their first movement in the sequence. Here, we test how much of the observed RT increases can be explained by the first-target ambiguity when reaching toward spatial targets. In our paradigm, we independently manipulate: (i) the number of future targets displayed, (ii) the number of targets to be acquired, and (iii) the spatial arrangement of the targets. This approach allows us to vary the demands of sequence preparation and first-target ambiguity, thereby enabling a direct assessment of their respective influence on RT. We report that RT increases with additional sequence elements but that this effect is fully explained by the ambiguity in selecting the first reach target. That is, sequence preparation causes no RT increase. In fact, when first-target ambiguity is eliminated, RT is constant across the number of displayed targets even though kinematic analysis reveals that participants have prepared a sequence. Together, these results indicate that preparing multiple reaches to spatial targets does not impose additional temporal costs relative to preparing a single reach.

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bioRxiv2025

Reading ability in both deaf and hearing adults is linked to neural representations of abstract phonology derived from visual speech

Reading is central to academic and vocational success. Some deaf children face reading challenges due to limited access to spoken or signed language. Robust phonological representations are key to reading development in hearing children. Spoken language phonology may be one of many contributors to reading development in deaf children. Indeed, speechreading ability correlates with reading skill in both deaf and hearing individuals, suggesting it is linked to reading development regardless of hearing status. Further support for this hypothesis would be provided by evidence that similar neural representations of speech phonology are evoked by visual speech and other language forms (auditory speech and text), and that these neural representations are related to reading proficiency. We used fMRI and Representational Similarity Analysis (RSA) to identify shared neural representations of spoken language phonological structure. A group of deaf adult participants (N=22), with a mixture of sign language and spoken language backgrounds and reading abilities, were presented with single lexical items as visual speech and dynamic text (cursive text, revealed letter-by-letter to promote a phonological reading strategy). Adult hearing participants (N=25) were presented with the same words, but as visual speech and auditory speech. Shared neural representations of phonological structure of English words were found in each group in the superior and middle temporal cortex (STC/MTC) and these abstract representations were more similar across different language forms in better readers. Our data provide neurobiological evidence of the contribution of visual speech to abstract phonological representations of spoken language, that relate to reading proficiency, in both deaf and hearing adults. Significance StatementReading is an essential skill, yet some deaf children face reading challenges due to reduced access to signed or spoken language. In hearing children, successful reading depends on abstract phonological representations, but whether spoken language phonology relates to reading in deaf individuals remains unclear. Using fMRI and RSA, we show that deaf and hearing adults recruit neural representations of phonology that are shared by visual speech and other language forms (visual/auditory speech in hearing; visual speech/dynamic text in deaf) in the superior and middle temporal cortex. Critically, greater cross-modal alignment of neural representations of phonological structure was associated with better reading in both groups. These findings provide neurobiological evidence that visual speech contributes to phonological representations that relate to reading, regardless of hearing status.

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bioRxiv2025

Integration of memory and sensory information in skilled sequence production

Sequential movements rely on two information sources: external sensory cues and internal memory representations. Although often both sources jointly drive sequential behavior, previous research has primarily examined them in isolation. To address this, we trained participants to perform sequences of rapid finger presses in response to numerical cues. Sensory influence was measured by varying the number of visible cues, and memory influence by comparing repeating and random sequences. Early in learning, participants integrated sensory and memory information: repeating sequences were performed more quickly when more cues were visible. After learning, when repeating sequences were predictable with certainty, participants relied solely on memory and ignored sensory cues. However, when this certainty was manipulated by introducing occasional violations within repeating sequences, participants reverted to integrating memory with sensory cues. We propose a computational model that successfully predicted both speed and accuracy of individual presses. Critically, this model relied on the assumption that multiple movements are planned independently of each other. This independence assumption was then validated by examining response patterns to isolated violations in repeating sequences. Finally, we provide evidence into how sequence memories can be flexibly deactivated and reactivated in response to these violations. Together, these results reveal how brain dynamically integrates sensory and memory information to produce sequences of movements.

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bioRxiv2025

Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavioral outcomes

The cerebellums involvement in cognitive functions is increasingly recognized, yet its developmental contribution to cognition remains poorly understood. The cerebellum undergoes rapid development in early life, paralleling major cognitive and behavioral changes. Although clinical studies have linked early cerebellar disruptions to profound developmental deficits, it remains largely unclear how typical cerebellar maturation supports the development of cognitive functions and how it interacts with broader brain development. Here, we apply a normative modeling framework to map cerebellar volumetric growth from infancy to young adulthood (N = 751; ages 1-21 years). Using lobular and functional cerebellar parcellations, we comprehensively characterize typical cerebellar development and examine how it aligns with cerebral development and behavioral outcomes. Across parcellations, posterior higher association areas consistently show steeper growth trajectories than anterior sensorimotor areas. Cerebellar and cerebral areas with similar functional roles demonstrate coordinated maturation, and volumetric growth in the posterior cerebellum relates to individual differences in socio-linguistic behaviors. These findings establish a comprehensive reference for typical cerebellar development, highlight cerebellar co-maturation with the cerebral cortex, and underscore the cerebellums role in supporting emerging higher cognitive functions.

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bioRxiv2025

Compositional neural dynamics during reaching

The complex mechanics of the arm make the neural control of reaching inherently posture dependent. Because previous reaching studies confound reach direction with final posture, it remains unknown how neural population dynamics in the motor cortex account for arm posture. Here we address this gap with high-density neural recordings and a reaching task in which the same targets serve as start points on some trials and end points on others. We show that neural population dynamics in monkey primary motor cortex and dorsal premotor cortex exhibit a compositional structure with three components that enable posture-dependent control: first, a posture subspace containing fixed points visited whenever the arm is in a specific posture; second, rotational dynamics that transition between these fixed points, systematically organized so that similar rotations produce similar movements while continuously updating the posture representation; third, a condition-independent shift dimension that tracks trial progression across all movements. This compositional structure advances the population-level account of how motor cortical dynamics support skilled reaching.

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bioRxiv2025

A Python Toolbox for Representational Similarity Analysis

Representational similarity analysis (RSA) is a method to characterize neural representations and evaluate computational models based on neural representational geometries. Here we present a wave of recent methodological advances, including improved measures of representational distances, evaluators for representational models, and statistical inference methods, which are available to the community in a new open-source toolbox in Python. The rsatoolbox enables neuroscientists to explore neural representational geometries and to evaluate neural network models, connecting theory to experiment in the new era of big models and big data.

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