Biology subjects

Diedrichsen, J.

Publications and source records attributed to Diedrichsen, J..

60 records · Page 4Linked to original sources

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.

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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.

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