Biology subjects

Shahbazi, M.

Publications and source records attributed to Shahbazi, M..

9 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

A Context-Free Model of Savings in Motor Learning

Learning to adapt voluntary movements to an external perturbation, whether mechanical or visual, is faster during a second encounter than during the first. The mechanisms underlying this phenomenon, known as savings, remain unclear. Recent studies propose that the high dimensionality of neural control enables the retention of learning traces that may facilitate savings. To test this idea we used MotorNet, a framework for training recurrent neural networks (RNNs) to control biomechanical models of the human upper limb. RNNs were trained to perform reaching movements with a velocity-dependent force field (FF) and without (NF) in the sequence NF1 (baseline), FF1 (adaptation), NF2 (washout), and FF2 (re-adaptation). RNNs showed behavioural signatures of savings in the absence of any explicit contextual input signalling the presence or absence of the FF. Savings was more robust in RNNs with larger numbers of units. We identified a component of RNN activity associated with savings--a shift in preparatory activity that persisted even after washout. Displacing this preparatory activity in the direction of the shift enhanced savings, whereas perturbations in the opposite direction reduced or eliminated savings. These findings suggest a potential neural basis for motor memory retention underlying savings that is reliant on the high dimensionality of neural circuits for control, and is independent of cognitive or strategic learning.

neuroscience↗

bioRxiv2024

Repetition effects reveal the sub-sequence representation of actions

When a movement sequence is repeated, the second execution is faster than the first. This demonstrates that the brain retains some trace of the just-executed sequence, the earliest form of sequence memory. Currently, it is unclear whether this memory trace is represented at the level of (a) transitions between movements, (b) chunks of multiple movements, or (c) the entire sequence. To answer this question, we instructed human participants to generate sequences of 11 finger presses in a delayed response paradigm. From one trial to the next, segments of variable length (1, 2, 4, 6, 11) could be repeated from the previous trial. We observed that repetition benefits appeared when a segment of 4 consecutive finger presses or longer was repeated from the previous trial. This suggests that the benefit of repetition is not merely the sum of improvements in individual transitions, nor does it require the entire sequence to be repeated. The repetition benefit was small for the first transition of a repeated segment and increased with additional repetitions. This suggests that the memory supporting the repetition effect is mainly activated when a series of past movements matches the memory trace. Planned future movements had less of an effect on the repetition effect. Our results provide insight into the structure of the earliest memory traces for motor sequences. Significance StatementMany motor skills involve concatenating single movements into complex sequences. Already after a single execution of a sequence, humans retain a memory trace that accelerates the execution of the sequence if it is repeated in the next trial. Our results show a repetition benefit even when only a small portion of movements from the previous trial is repeated, suggesting that full sequence repetition is not necessary. Detailed analyses indicate that the memory trace is activated when the last 2-3 movements match the current execution. Our work, therefore, sheds light on the structure of the earliest sequence memory and will interest researchers studying motor skill learning and the neural basis of movement control.

neuroscience↗

bioRxiv2023

Cortical areas for planning sequences before and during movement

Production of rapid movement sequences relies on preparation before (pre-planning) and during (online planning) movement. Here, we asked how different cortical sensorimotor areas contribute to these processes. Human participants performed three single-finger and three multi-finger sequences in a delayed movement paradigm. During preparation, 7T functional MRI revealed that primary motor (M1) and somatosensory (S1) areas showed pre-activation of the first movement, even though the overall activation level did not change from baseline. During production, the activity in M1 and S1 could be explained by temporal summation of activity patterns corresponding to constituent fingers. In contrast, dorsal premotor (PMd) and anterior superior parietal lobule (aSPL) showed substantial activation during preparation of multi-finger as compared to single-finger sequences. The same regions were also more activated during production of multi-finger sequences, suggesting that the same areas are involved in both pre- and online planning. Nonetheless, we observed small but robust differences between the two contrasts, suggesting preferential involvements of these areas in pre- and online planning. Multivariate analysis revealed sequence-specific representations in both PMd and aSPL, which remained stable across both preparation and production phases. This suggests that these areas maintain a sequence-specific representation before and during sequence production, likely guiding the execution-related areas. Significance StatementUnderstanding how the brain orchestrates complex behavior remains a core challenge in human neuroscience. Here, we combine high-resolution neuroimaging and a carefully crafted design to study the neural control of rapid sequential finger movements, like typing or playing the piano. Advancing prior research, we show that the brain areas involved in planning these movements maintain those representations throughout the execution of the sequence. This representational stability across planning and execution suggests an intricate connection between these processes. Our results shed light on the nuanced contributions of different cortical areas to different aspects of coordinating skilled movement. This work is well placed to inform future research in animal models and the development of targeted interventions against movement disorders.

neuroscience↗

bioRxiv2023

A signalling rheostat controls chromosome segregation fidelity during early lineage specification and neurogenesis by modulating DNA replication stress

The development and homeostasis of organisms rely on the correct replication, maintenance and segregation of their genetic blueprints. How these intracellular processes are monitored across generations of different human cellular lineages, and why the spatio-temporal distribution of mosaicism varies during development remain unknown. Here, we identify several lineage specification signals that regulate chromosome segregation fidelity in both human and mouse pluripotent stem cells. Through epistatic analyses, we find that that WNT, BMP and FGF form a signalling "rheostat" upstream of ATM that monitors replication fork velocity, origin firing and DNA damage during S-phase in pluripotency, which in turn controls spindle polymerisation dynamics and faithful chromosome segregation in the following mitosis. Cell signalling control of chromosome segregation fidelity declines together with ATM activity after pluripotency exit and specification into the three human germ layers, or further differentiation into meso- and endoderm lineages, but re-emerges during neuronal lineage specification. In particular, we reveal that a tug-of-war between FGF and WNT signalling in neural progenitor cells results in DNA damage and chromosome missegregation during cortical neurogenesis, which could provide a rationale for the high levels of mosaicism in the human brain. Our results highlight a moonlighting role of morphogens, patterning signals and growth factors in genome maintenance during pluripotency and lineage specification, which could have important implications for our understanding on how mutations and aneuploidy arise during human development and disease. One sentence summaryDevelopmental signals link genome maintenance to cell fate

cell biology↗

bioRxiv2023

Neural correlates of online action preparation

When performing movements in rapid succession, the brain needs to coordinate preparation of an upcoming action with ongoing execution. Here we identify the processes and brain areas involved in this ability. Human participants performed pairs of single-finger presses or three-finger chords in settings where they had to prepare the second movement before or after the first response. Despite matched perceptual and movement requirements, 7T functional MRI revealed increased brain activity in regions along the intra-parietal sulcus and ventral visual stream when actions overlapped. Multivariate analyses suggested that these areas were involved in stimulus identification and action selection. In contrast, the dorsal premotor cortex, known to be involved in planning upcoming movements, showed no discernible signs of heightened activity. This observation suggests that the bottleneck during simultaneous action control and preparation arises at the level of stimulus identification and action selection, whereas movement planning in the premotor cortex can unfold concurrently with execution without interference between the two processes. SummaryThe brains ability to prepare for upcoming actions while controlling ongoing movements is a crucial evolutionary adaptation of the action system. However, the neural basis of online action preparation remains largely unknown. We found that superior-parietal and occipito-temporal areas exhibited heightened activation during online preparation. Surprisingly, the dorsal premotor cortex, known to be a crucial structure in motor planning, did not display additional activation for overlapping actions. These findings imply that while motor planning within the premotor cortex can occur in parallel with the execution of ongoing movement, the parallel stimulus identification and action selection in the posterior parietal cortex requires additional neural processes. HighlightsO_LIIndividuals prepare upcoming actions while simultaneously controlling ongoing movement C_LIO_LIWhen these two processes overlap, superior-parietal and occipito-temporal areas show increased activation C_LIO_LIMultivariate analysis suggests that increased activation arises to resolve simultaneous stimulus identification and action selection C_LIO_LIThe premotor cortex, known to be involved in motor planning, coordinates simultaneous planning and execution without extra neural engagement C_LI

neuroscience↗

bioRxiv2023

Interaction of multiple future movement plans in sequential reaching

Real world actions often comprise of a series of movements that cannot be entirely planned before initiation. When these actions are executed rapidly, the planning of multiple future movements needs to occur simultaneously with the ongoing action. How the brain solves this task remains unknown. Here we address this question with a new sequential arm reaching paradigm that manipulates how many future reaches are available for planning while controlling execution of the ongoing reach. We show that participants plan at least two future reaches simultaneously with an ongoing reach. Further, the planning processes of the two future reaches are not independent of one another. Evidence that the planning processes interact is two-fold. First, correcting for a visual perturbation of the ongoing reach target is slower when more future reaches are planned. Second, the curvature of the current reach is modified based on the next reach only when their planning processes temporally overlap. These interactions between future planning processes may enable smooth production of sequential actions by linking individual segments of a long sequence at the level of motor planning.

neuroscience↗

bioRxiv2020

Using distance on the Riemannian manifold to compare representations in brain and models

Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product matrix or the correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI data and considering a wide range of metrics, we show that the Riemannian distance is least susceptible to sampling bias, results in larger intra-subject reliability, and affords searchlight mapping with high sensitivity and specificity. Furthermore, we show that the Riemannian distance can be used for measuring multi-dimensional connectivity. This measure captures both univariate and multivariate connectivity and is also more sensitive to nonlinear regional interactions compared to the state-of-the-art measures. Applying our proposed metric to neural network representations of natural images, we demonstrate that it also possesses outstanding performance in quantifying similarity in models. Taken together, our results lend credence to the proposition that RSA should consider the manifold of the representational matrices to summarize response patterns in the brain and models.

neuroscience↗