Biology subjects

Diedrichsen, J.

Publications and source records attributed to Diedrichsen, J..

10 recordsLinked to original sources

bioRxiv2018

A Multi-Domain Task Battery Reveals Functional Boundaries in the Human Cerebellum

There is compelling evidence that the human cerebellum is engaged in a wide array of motor and cognitive tasks. A fundamental question centers on whether the cerebellum is organized into distinct functional sub-regions. To address this question, we employed a rich task battery, designed to tap into a broad range of cognitive processes. During four functional magnetic resonance imaging (fMRI) sessions, participants performed a battery of 26 diverse tasks comprising 47 unique conditions. Using the data from this multi-domain task battery, we derived a comprehensive functional parcellation of the cerebellar cortex, and evaluated it by predicting functional boundaries in a novel set of tasks. The new parcellation successfully identified distinct functional sub-regions, providing significant improvements over existing parcellations derived from task-free data. Lobular boundaries, commonly used to summarize functional data, did not coincide with functional subdivisions. This multi-domain task approach offers novel insights into the functional heterogeneity of the cerebellar cortex.

neuroscience↗

bioRxiv2018

Parcellation of motor sequence representations in the human neocortex

While previous studies have revealed an extended network of cortical regions associated with motor sequence production, the specific role of each of these areas is still elusive. To address this issue, we designed a novel behavioural paradigm that allowed us to experimentally manipulate the structure of motor sequences representations in individual participants. We then conducted fMRI while participants executed 8 trained sequences to examine how this structure is reflected in the associated activity patterns. Both model-based and model-free approaches revealed a clear distinction between primary and non-primary motor cortices in their representational contents, with M1 specifically representing individual finger movements, and premotor and parietal cortices showing a mixture of chunk, sequence and finger transition representations. Using model-free representational parcellation, we could divide these non-primary motor cortices into separate clusters, each with a unique representation along the stimulus-to-action gradient. These results provide new insights into how human neocortex organizes movement sequences.

neuroscience↗

bioRxiv2018

The effects of habits on motor skill learning

Skill learning involves the formation of stable motor patterns. In musical and athletic training, however, these stable motor habits can also impede the attainment of higher levels of performance. We developed an experimental paradigm to induce a specific motor pattern in the context of a discrete sequence production task and to investigate how these habits affect performance over a 3-week training period. Participants initially practiced small segments of 2 to 3 finger movements ("chunks") and then learned longer sequences composed of these chunks. This initial training induced a persistent temporal pattern during execution, with shorter inter-press-intervals within a chunk and longer ones at chunk boundaries. This pattern remained stable during the subsequent 10 days of training, in which participants were asked to produce the sequence as fast as possible from memory. The habit was also preserved when the sequences were directly displayed, removing the need for memory recall. We were able to induce chunking patterns that were either beneficial or detrimental to performance by taking into consideration the biomechanical constraints of the sequences. While we observed an overall reduction in the detrimental effect of the disadvantageous chunking instructions with training, our results show that the degree to which these detrimental chunk structures were maintained, was predictive of lower levels of final performance. In sum, we were able to induce beneficial and detrimental motor habits in a motor sequence production task and show that these initial instructions influenced performance outcomes over a prolonged period of time. Significance StatementA habit is defined as an automatized action that resists modification once sufficiently established. Preventing bad habits, while reinforcing good habits, is a key objective when teaching new motor skills. While habit formation is an integral part of motor skill acquisition, previous research has focused on habit formation in terms of action selection. In this paper, we examine habit formation in terms of motor skill execution, after the action has been selected. We were able to induce beneficial or detrimental motor habits in the production of motor sequences. Habits were stable over a prolonged training period. Our results demonstrate how cognitive instruction can lead to persistent motor habits and we explore how these habits are potentially modified with training.

neuroscience↗

bioRxiv2018

Ipsilateral finger representations are engaged in active movement, but not sensory processing

Hand and finger movements are mostly controlled through crossed corticospinal projections from the contralateral hemisphere. During unimanual movements, activity in the contralateral hemisphere is increased while the ipsilateral hemisphere is suppressed below resting baseline. Despite this suppression, unimanual movements can be decoded from ipsilateral activity alone. This indicates that ipsilateral activity patterns represent parameters of ongoing movement, but the origin and functional relevance of these representations is unclear. Here, we asked whether human ipsilateral representations are caused by active movement, or whether they are driven by sensory input. Participants alternated between performing single finger presses and having fingers passively stimulated, while we recorded brain activity using high-field (7T) functional imaging. We contrasted active and passive finger representations in sensorimotor areas of ipsilateral and contralateral hemispheres. Finger representations in the contralateral hemisphere were equally strong under passive and active conditions, highlighting the importance of sensory information in feedback control. In contrast, ipsilateral finger representations were stronger during active presses. Furthermore, the spatial distribution of finger representations differed between hemispheres: the contralateral hemisphere showed the strongest finger representations in Brodmann area 3a and 3b, while the ipsilateral hemisphere exhibited stronger representations in premotor and parietal areas. This suggests that finger representations in the two hemispheres have different origins - contralateral representations are driven by both active movement and sensory stimulation, whereas ipsilateral representations are mainly engaged during active movement. This suggests that a possible contribution of the ipsilateral hemisphere lies in movement planning, rather than in the dexterous feedback control of the movement.\n\nSignificance statementMovements of the human body are mostly controlled by contralateral cortical regions. However, activity in ipsilateral sensorimotor regions is also modulated during active movements. The origin and functional relevance of these ipsilateral representations is unclear. Here we used high-field neuroimaging to investigate how human contralateral and ipsilateral hemispheres represent active finger presses and passive finger stimulation. We report that while the contralateral hemisphere was equally strongly recruited during active and passive conditions, the ipsilateral hemisphere was mostly recruited during active movement. We propose that the ipsilateral hemisphere may play a role in bilateral movement planning.

neuroscience↗

bioRxiv2018

Stability of representational geometry across a wide range of fMRI activity levels

Fine-grained activity patterns, as measured with functional magnetic resonance imaging (fMRI), are thought to reflect underlying neural representations. Multivariate analysis techniques, such as representational similarity analysis (RSA), can be used to test models of brain representation by quantifying the representational geometry (the collection of pair-wise dissimilarities between activity patterns). One important caveat, however, is that non-linearities in the coupling between neural activity and the fMRI signal may lead to significant distortions in the representational geometry estimated from fMRI activity patterns. Here we tested the stability of representational dissimilarity measures in primary sensory-motor (S1 and M1) and early visual regions (V1/V2) across a large range of activation levels. Subjects were visually cued with different letters to perform single finger presses with one of the 5 fingers at a rate of 0.3-2.6 Hz. For each stimulation frequency, we quantified the difference between the 5 activity patterns in M1, S1, and V1/V2. We found that the representational geometry remained stable, even though the average activity increased over a large dynamic range. These results indicate that the representational geometry of fMRI activity patterns can be reliably assessed, largely independent of the average activity in the region. This has important methodological implications for RSA and other multivariate analysis approaches that use the representational geometry to make inferences about brain representations.

neuroscience↗

bioRxiv2018

Binding during sequence learning does not alter cortical representations of individual actions

As a movement sequence is learned, serially ordered actions get bound together into sets in order to reduce computational complexity during planning and execution. Here we examined how the binding of serial actions alters the cortical representations of individual movements. Across five weeks of practice, healthy human subjects learned either a complex 32-item sequence of finger movements (Trained group, N=9) or randomly ordered actions (Control group, N=9). After five weeks of training, responses during sequence production in the Trained group were correlated, consistent with being bound together under a common command. These behavioral changes, however, did not coincide with plasticity in the multivariate representations of individual finger movements, assessed using fMRI, at any level of the cortical motor hierarchy. This suggests that the representations of individual actions remain stable, even as the execution of those same actions become bound together in the context of producing a well learned sequence.

neuroscience↗

bioRxiv2017

Does human primary motor cortex represent sequences of finger movements?

Human primary motor cortex (M1) is an essential structure for the production of dexterous hand movements. While distinct sub-populations of neurons are activated during single finger movements, it remains unknown whether M1 also represents sequences of multiple finger movements. Using novel multivariate fMRI analysis techniques, we show here that even after 5 days of intense practice there was little or no evidence for a true sequence representation in M1. Rather, the activity patterns for sequences in M1 could be explained by linear combination of patterns associated with the constituent individual finger movements, with the strongest weight on the finger making the first response of the sequence. These results suggest that M1 only represents single finger movements, but receives increased input at the start of a sequence. In contrast, the reliable differences between different sequences in premotor and parietal areas could not be explained by a strong weighting of the first finger, supporting the view that these regions exhibit a true representation of sequences.

neuroscience↗

bioRxiv2017

Finger recruitment patterns during mirror movements suggest two systems for hand recovery after stroke

Accumulating behavioural and neurophysiological evidence suggests that upper-limb control relies on contributions from both cortical and subcortical motor circuits, with cortical inputs providing fine-finger function and subcortical inputs providing the ability for gross movements, respectively. During recovery of function after stroke, the relative contributions from these pathways may shift. Here we propose that mirror movements that appear after stroke provide a non-invasive assay through which relative contributions from cortical and subcortical pathways towards hand recovery can be studied. We hypothesized that mirror movements, like hand function, are generated by summed contributions from cortical and subcortical pathways, and suggest that subcortical contributions should be characterized by a broad recruitment of fingers, while cortical contributions primarily recruit the homologous finger in the passive hand. In a longitudinal stroke recovery study (Xu et al., 2016), we quantified mirror movements and paretic hand function in 53 stroke patients in the year following unilateral stroke. Mirror movements in the non-paretic hand were exaggerated early after damage (week 2), with paretic finger presses broadly recruiting multiple fingers in the non-paretic hand. On average, however, mirroring in homologous fingers was 1.76 times larger than in non-homologous fingers. Over the year, mirroring in the non-paretic hand progressively normalized with a time-course that mimicked that for the fine-finger deficits in the paretic hand. In comparison, during non-paretic finger presses, the homologous component of mirroring in the paretic hand was reduced early after stroke (week 2) but progressively normalized. Altogether, we conclude that the pattern of mirror movements across homologous and non-homologous fingers reflect the summed contributions of both cortical and subcortical systems, and we discuss the implications of our results towards hand recovery after stroke.

neuroscience↗

bioRxiv2017

Pattern Component Modeling: A Flexible Approach For Understanding The Representational Structure Of Brain Activity Patterns

Representational models specify how complex patterns of neural activity relate to visual stimuli, motor actions, or abstract thoughts. Here we review pattern component modeling (PCM), a practical Bayesian approach for evaluating such models. Similar to encoding models, PCM evaluates the ability of models to predict novel brain activity patterns. In contrast to encoding models, however, the activity of individual voxels across conditions (activity profiles) are not directly fitted. Rather, PCM integrates over all possible activity profiles and computes the marginal likelihood of the data under the activity profile distribution specified by the representational model. By using an analytical expression for the marginal likelihood, PCM allows the fitting of flexible representational models, in which the relative strength and form of the encoded features can be estimated from the data. We discuss here a number of different forms with which such flexible representational models can be specified, and how models of different complexity can be compared. We then provide a number of practical examples from our recent work in motor control, ranging from fixed models to more complex non-linear models of brain representations. The code for the fitting and cross-validation of representational models is provided in a open-source Matlab toolbox.

neuroscience↗

bioRxiv2016

Cooperation not competition: bihemispheric tDCS and fMRI show role for ipsilateral hemisphere in motor learning

What is the role of ipsilateral motor and pre-motor areas in motor learning? One view supposes that ipsilateral activity suppresses contralateral motor cortex, and thus needs to be inhibited to improve motor learning. Alternatively, the ipsilateral motor cortex may play an active role in the control and learning of unilateral hand movements. We approached this question by applying double-blind bihemispheric transcranial direct current stimulation (tDCS) over both contralateral and ipsilateral motor cortex in a between-group design during four days of unimanual explicit sequence training. Independently of whether the anode was placed over contralateral or ipsilateral motor cortex, bihemispheric stimulation yielded substantial performance gains relative to unihemispheric or sham stimulation. This performance advantage appeared to be supported by plastic changes in both hemispheres: First, we found that behavioral advantages generalized strongly to the untrained hand, suggesting that tDCS strengthened effector-independent representations. Secondly, functional imaging during speed-matched execution of trained sequences conducted 48 h after training revealed sustained, polarity-independent increases in activity in both motor cortices relative to the sham group. These results suggest a cooperative rather than competitive interaction of the two motor cortices during skill learning and suggest that bihemispheric brain stimulation during unimanual skill learning may be beneficial, because it harnesses plasticity in the ipsilateral hemisphere.\n\nSignificance statementMany neurorehabilitation approaches are based on the idea that is beneficial to boost excitability in the contralateral hemisphere while attenuating that of the ipsilateral cortex to reduce interhemispheric inhibition. We observed that bihemispheric tDCS with the excitatory anode either over contralateral or ipsilateral motor cortex facilitated motor learning nearly twice as strongly as unihemispheric tDCS. These increases in motor learning were accompanied by increases in fMRI activation in both motor cortices that outlasted the stimulation period, as well as increased generalization to the untrained hand. Collectively, our findings suggest a cooperative--rather than competitive--role of the hemispheres and imply that it is most beneficial to harness plasticity in both hemispheres in neurorehabilitation of motor deficits.

neuroscience↗