Using generative models to make probabilistic statements about hippocampal engagement in MEG

Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports it on both the cortical and hippocampal surfaces. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is -20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts

The Neurodynamic Decision Variable in Human Multi-Alternative Perceptual Choice

The neural dynamics underpinning binary perceptual decisions and their transformation into actions are well studied, but real-world decisions typically offer more than two response alternatives. How does decision-related evidence accumulation dynamically influence multiple action representations in humans? The heightened conservatism required in multiple compared to binary choice scenarios suggests a mechanism which compensates for increased uncertainty when multiple choices are present by supressing baseline activity. Here, we tracked action representations using corticospinal excitability during four and two-choice perceptual decisions, and modelled them using a sequential sampling framework. We found that the predictions made by leaky competing accumulator models in order to accommodate multiple choices (i.e. reduced baseline activity to compensate increased uncertainty) were borne out by dynamic changes in human action representations. This suggests a direct and continuous influence of interacting evidence accumulators, each favouring a different decision alternative, on downstream corticospinal excitability during complex choice

Adaptive deep brain stimulation for Parkinson's disease demonstrates reduced speech side effects compared to conventional stimulation in the acute setting.

Deep brain stimulation (DBS) for Parkinson's disease (PD) is currently limited by costs, partial efficacy and surgical and stimulation-related side effects. This has motivated the development of adaptive DBS (aDBS) whereby stimulation is automatically adjusted according to a neurophysiological biomarker of clinical state, such as β oscillatory activity (12–30 Hz). aDBS has been studied in parkinsonian primates and patients and has been reported to be more energy efficient and effective in alleviating motor symptoms than conventional DBS (cDBS) at matched amplitudes

Neural dynamics of illusory tactile pulling sensations

Directional tactile pulling sensations are integral to everyday life, but their neural mechanisms remain unknown. Prior accounts hold that primary somatosensory (SI) activity is sufficient to generate pulling sensations, with alternative proposals suggesting that amodal frontal or parietal regions may be critical. We combined high-density EEG with asymmetric vibration, which creates an illusory pulling sensation, thereby unconfounding pulling sensations from unrelated sensorimotor processes. Oddballs that created opposite direction pulls to common stimuli were compared to the same oddballs after neutral common stimuli (symmetric vibration) and to neutral oddballs. We found evidence against the sensory-frontal N140 and in favor of the midline P200 tracking the emergence of pulling sensations, specifically contralateral parietal lobe activity 264-320ms, centered on the intraparietal sulcus. This suggests that SI is not sufficient to generate pulling sensations, which instead depend on the parietal association cortex, and may reflect the extraction of orientation information and related spatial processing

Transcranial direct current stimulation facilitates motor learning post-stroke: a systematic review and meta-analysis.

Transcranial direct current stimulation (tDCS) is an attractive protocol for stroke motor recovery. The current systematic review and meta-analysis investigated the effects of tDCS on motor learning post-stroke. Specifically, we determined long-term learning effects by examining motor improvements from baseline to at least 5 days after tDCS intervention and motor practise. 17 studies reported long-term retention testing (mean retention interval=43.8 days; SD=56.6 days) and qualified for inclusion in our meta-analysis. Assessing primary outcome measures for groups that received tDCS and motor practise versus sham control groups created 21 valid comparisons: (1) 16 clinical assessments and (2) 5 motor skill acquisition tests. A random effects model meta-analysis showed a significant overall effect size=0.59 (p<0.0001; low heterogeneity, T(2)=0.04; I(2)=22.75%; and high classic fail-safe N=240). 4 moderator variable analyses revealed beneficial effects of tDCS on long-term motor learning: (1) stimulation protocols: anodal on the ipsilesional hemisphere, cathodal on the contralesional hemisphere, or bilateral; (2) recovery stage: subacute or chronic stroke; (3) stimulation timing: tDCS before or during motor practise; and (4) task-specific training or conventional rehabilitation protocols. This robust meta-analysis identified novel long-term motor learning effects with tDCS and motor practise post-stroke

Dopamine, affordance and active inference.

The role of dopamine in behaviour and decision-making is often cast in terms of reinforcement learning and optimal decision theory. Here, we present an alternative view that frames the physiology of dopamine in terms of Bayes-optimal behaviour. In this account, dopamine controls the precision or salience of (external or internal) cues that engender action. In other words, dopamine balances bottom-up sensory information and top-down prior beliefs when making hierarchical inferences (predictions) about cues that have affordance. In this paper, we focus on the consequences of changing tonic levels of dopamine firing using simulations of cued sequential movements. Crucially, the predictions driving movements are based upon a hierarchical generative model that infers the context in which movements are made. This means that we can confuse agents by changing the context (order) in which cues are presented. These simulations provide a (Bayes-optimal) model of contextual uncertainty and set switching that can be quantified in terms of behavioural and electrophysiological responses. Furthermore, one can simulate dopaminergic lesions (by changing the precision of prediction errors) to produce pathological behaviours that are reminiscent of those seen in neurological disorders such as Parkinson's disease. We use these simulations to demonstrate how a single functional role for dopamine at the synaptic level can manifest in different ways at the behavioural level

Long-latency modulation of motor cortex excitability by ipsilateral posterior inferior frontal gyrus and pre-supplementary motor area

The primary motor cortex (M1) is strongly influenced by several frontal regions. Dual-site transcranial magnetic stimulation (dsTMS) has highlighted the timing of early (<40 ms) prefrontal/premotor influences over M1. Here we used dsTMS to investigate, for the first time, longer-latency causal interactions of the posterior inferior frontal gyrus (pIFG) and pre-supplementary motor area (pre-SMA) with M1 at rest. A suprathreshold test stimulus (TS) was applied over M1 producing a motor-evoked potential (MEP) in the relaxed hand. Either a subthreshold or a suprathreshold conditioning stimulus (CS) was administered over ipsilateral pIFG/pre-SMA sites before the TS at different CS-TS inter-stimulus intervals (ISIs: 40-150 ms). Independently of intensity, CS over pIFG and pre-SMA (but not over a control site) inhibited MEPs at an ISI of 40 ms. The CS over pIFG produced a second peak of inhibition at an ISI of 150 ms. Additionally, facilitatory modulations were found at an ISI of 60 ms, with supra-but not subthreshold CS intensities. These findings suggest differential modulatory roles of pIFG and pre-SMA in M1 excitability. In particular, the pIFG-but not the pre-SMA-exerts intensity-dependent modulatory influences over M1 within the explored time window of 40-150 ms, evidencing fine-tuned control of M1 output

Increasing human motor skill acquisition by driving theta-gamma coupling

Skill learning is a fundamental adaptive process, but the mechanisms remain poorly understood. Some learning paradigms, particularly in the memory domain, are closely associated with gamma activity that is amplitude-modulated by the phase of underlying theta activity, but whether such nested activity patterns also underpin skill learning is unknown. Here we addressed this question by using transcranial alternating current stimulation (tACS) over sensorimotor cortex to modulate theta-gamma activity during motor skill acquisition, as an exemplar of a non-hippocampal-dependent task. We demonstrated, and then replicated, a significant improvement in skill acquisition with theta-gamma tACS, which outlasted the stimulation by an hour. Our results suggest that theta-gamma activity may be a common mechanism for learning across the brain and provides a putative novel intervention for optimising functional improvements in response to training or therapy

Incomplete evidence that increasing current intensity of tDCS boosts outcomes

BACKGROUND: Transcranial direct current stimulation (tDCS) is investigated to modulate neuronal function by applying a fixed low-intensity direct current to scalp. OBJECTIVES: We critically discuss evidence for a monotonic response in effect size with increasing current intensity, with a specific focus on a question if increasing applied current enhance the efficacy of tDCS. METHODS: We analyzed tDCS intensity does-response from different perspectives including biophysical modeling, animal modeling, human neurophysiology, neuroimaging and behavioral/clinical measures. Further, we discuss approaches to design dose-response trials. RESULTS: Physical models predict electric field in the brain increases with applied tDCS intensity. Data from animal studies are lacking since a range of relevant low-intensities is rarely tested. Results from imaging studies are ambiguous while human neurophysiology, including using transcranial magnetic stimulation (TMS) as a probe, suggests a complex state-dependent non-monotonic dose response. The diffusivity of brain current flow produced by conventional tDCS montages complicates this analysis, with relatively few studies on focal High Definition (HD)-tDCS. In behavioral and clinical trials, only a limited range of intensities (1-2 mA), and typically just one intensity, are conventionally tested; moreover, outcomes are subject brain-state dependent. Measurements and models of current flow show that for the same applied current, substantial differences in brain current occur across individuals. Trials are thus subject to inter-individual differences that complicate consideration of population-level dose response. CONCLUSION: The presence or absence of simple dose response does not impact how efficacious a given tDCS dose is for a given indication. Understanding dose-response in human applications of tDCS is needed for protocol optimization including individualized dose to reduce outcome variability, which requires intelligent design of dose-response studies