Dopamine substitution alters effective connectivity of cortical prefrontal, premotor, and motor regions during complex bimanual finger movements in Parkinson's disease.

Bimanual coordination is impaired in Parkinson's disease (PD), affecting patients' quality of life. Besides dysfunction of the basal ganglia network, alterations of cortical oscillatory coupling, particularly between prefrontal and (pre-)motoric areas, are thought to underlie this impairment. Here, we studied 16 PD patients OFF and ON medication and age-matched healthy controls recording high-resolution electroencephalography (EEG) during performance of spatially coupled and uncoupled bimanual finger movements. Dynamic causal modeling (DCM) for induced responses was used to infer task-induced effective connectivity within a network comprising bilateral prefrontal cortex (PFC), lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1). Performing spatially coupled movements, excitatory left-hemispheric PFC to lPM coupling was significantly stronger in controls compared to unmedicated PD patients. Levodopa-induced enhancement of this connection correlated with increased movement accuracy. During performance of spatially uncoupled movements, PD patients OFF medication exhibited inhibitory connectivity from left PFC to SMA. Levodopa intake diminished these inhibitory influences and restored excitatory PFC to lPM coupling. This restoration, however, did not improve motor function. Concluding, our results indicate that lateralization of prefrontal to premotor connectivity in PD can be augmented by levodopa substitution and is of compensatory nature up to a certain extent of complexity.

Ageing changes effective connectivity of motor networks during bimanual finger coordination.

Bimanual finger coordination declines with age. However, relatively little is known about the neurophysiological alterations in the motor-system causing this decline. In the present study, we used 128-channel electroencephalography (EEG) to evaluate causal interactions of cortical, motor-related brain areas. Right-handed young and elderly subjects performed complex temporally and spatially coupled as well as temporally coupled and spatially uncoupled finger tappings. Employing dynamic causal modelling (DCM) for induced responses, we inferred task-induced effective connectivity within a core motor network comprising bilateral primary motor cortex (M1), lateral premotor cortex (lPM), supplementary motor area (SMA), and prefrontal cortex (PFC). Behavioural analysis showed significantly increased error rates and performance times for elderly subjects, confirming that motor functions decrease with ageing. Additionally, DCM analysis revealed that this age-related decline can be associated with specific alterations of interhemispheric and prefrontal to premotor connectivity. Young and elderly subjects exhibited inhibitory left to right M1-M1 coupling during performance of temporally and spatially coupled movements. Effects of ageing on interhemispheric connectivity particularly emerged when movements became spatially uncoupled. Here, elderly participants still expressed inhibitory left to right M1-M1 coupling, whereas no such connection was present in the young. Furthermore, ageing affected prefrontal to premotor connectivity. In all conditions, elderly subjects showed significant couplings from left PFC to left lPM. In contrast, young participants exhibited left PFC to SMA connections. These results demonstrate that (i) in spatially uncoupled movements interhemispheric M1-connectivity increases with age and (ii) support the idea that ageing is associated with enhanced lateral prefrontal to premotor coupling (PFC to lPM) and hypoactivation of a medial pathway (PFC to SMA) within the dominant hemisphere.

Model-based feature construction for multivariate decoding.

Conventional decoding methods in neuroscience aim to predict discrete brain states from multivariate correlates of neural activity. This approach faces two important challenges. First, a small number of examples are typically represented by a much larger number of features, making it hard to select the few informative features that allow for accurate predictions. Second, accuracy estimates and information maps often remain descriptive and can be hard to interpret. In this paper, we propose a model-based decoding approach that addresses both challenges from a new angle. Our method involves (i) inverting a dynamic causal model of neurophysiological data in a trial-by-trial fashion; (ii) training and testing a discriminative classifier on a strongly reduced feature space derived from trial-wise estimates of the model parameters; and (iii) reconstructing the separating hyperplane. Since the approach is model-based, it provides a principled dimensionality reduction of the feature space; in addition, if the model is neurobiologically plausible, decoding results may offer a mechanistically meaningful interpretation. The proposed method can be used in conjunction with a variety of modelling approaches and brain data, and supports decoding of either trial or subject labels. Moreover, it can supplement evidence-based approaches for model-based decoding and enable structural model selection in cases where Bayesian model selection cannot be applied. Here, we illustrate its application using dynamic causal modelling (DCM) of electrophysiological recordings in rodents. We demonstrate that the approach achieves significant above-chance performance and, at the same time, allows for a neurobiological interpretation of the results.

Neural correlates of sensorimotor gating: a metabolic positron emission tomography study in awake rats.

Impaired sensorimotor gating occurs in neuropsychiatric disorders such as schizophrenia and can be measured using the prepulse inhibition (PPI) paradigm of the acoustic startle response. This assay is frequently used to validate animal models of neuropsychiatric disorders and to explore the therapeutic potential of new drugs. The underlying neural network of PPI has been extensively studied with invasive methods and genetic modifications. However, its relevance for healthy untreated animals and the functional interplay between startle- and PPI-related areas during a PPI session is so far unknown. Therefore, we studied awake rats in a PPI paradigm, startle control and background noise control, combined with behavioral [(18)F]fluoro-2-deoxyglucose positron emission tomography (FDG-PET). Subtractive analyses between conditions were used to identify brain regions involved in startle and PPI processing in well-hearing Black hooded rats. For correlative analysis with regard to the amount of PPI we also included hearing-impaired Lister hooded rats that startled more often, because their hearing threshold was just below the lowest prepulses. Metabolic imaging showed that the brain areas proposed for startle and PPI mediation are active during PPI paradigms in healthy untreated rats. More importantly, we show for the first time that the whole PPI modulation network is active during "passive" PPI sessions, where no selective attention to prepulse or startle stimulus is required. We conclude that this reflects ongoing monitoring of stimulus significance and constant adjustment of sensorimotor gating.

Mismatch responses in the awake rat: evidence from epidural recordings of auditory cortical fields.

Detecting sudden environmental changes is crucial for the survival of humans and animals. In the human auditory system the mismatch negativity (MMN), a component of auditory evoked potentials (AEPs), reflects the violation of predictable stimulus regularities, established by the previous auditory sequence. Given the considerable potentiality of the MMN for clinical applications, establishing valid animal models that allow for detailed investigation of its neurophysiological mechanisms is important. Rodent studies, so far almost exclusively under anesthesia, have not provided decisive evidence whether an MMN analogue exists in rats. This may be due to several factors, including the effect of anesthesia. We therefore used epidural recordings in awake black hooded rats, from two auditory cortical areas in both hemispheres, and with bandpass filtered noise stimuli that were optimized in frequency and duration for eliciting MMN in rats. Using a classical oddball paradigm with frequency deviants, we detected mismatch responses at all four electrodes in primary and secondary auditory cortex, with morphological and functional properties similar to those known in humans, i.e., large amplitude biphasic differences that increased in amplitude with decreasing deviant probability. These mismatch responses significantly diminished in a control condition that removed the predictive context while controlling for presentation rate of the deviants. While our present study does not allow for disambiguating precisely the relative contribution of adaptation and prediction error processing to the observed mismatch responses, it demonstrates that MMN-like potentials can be obtained in awake and unrestrained rats.

Dynamic causal models and physiological inference: a validation study using isoflurane anaesthesia in rodents.

Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABA(A) receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.