High Thalamocortical Theta Coherence in Patients with Parkinson's Disease

Thalamocortical interplay

Scalp EEG and thalamic LFP, the two signals presented in this study, are generated by neural networks of drastically different scales. The scalp EEG is generated by dipole-like dendrites of cortical pyramidal cells arranged in parallel and space averaged over several square centimeters of the cortex (Nunez et al., 2001). In the thalamus, ellipsoid dendritic arbors of thalamocortical cells generate a “closed field” in the sense that there is no aligned structure of dipoles as in the cortex. For the LFP recorded from our electrode, we assume a contribution of cells in a cube of side length 100 μm (Engel et al., 1990), which corresponds to a sample of approximately four to five cells (Mai et al., 1997; A. Morel, personal communication). Because multielectrode LFP recordings in the thalamus show high correlation between distant sites (Contreras et al., 1996), even a small sample of cells may be representative for thalamic LFP activity.

The magnitude of thalamocortical theta coherence is comparable to the magnitude of coherence between EEG electrodes (Fig. 2A). This is much larger than the magnitude of coherence between subdural recordings, which amounts to only 20% at 3 cm and decreases with distance (Bullock et al., 1995). For the same distance, coherence in scalp EEG exceeds 80% (Fig. 2A, adjacent electrodes). Thus, the spatial averaging, which is at the basis of the EEG, may extract those components of cortical activity that are highly coherent over large distances (Nunez et al., 2001). Our finding of high thalamocortical coherence supports the notion that the thalamus is involved in synchronizing activity between distant cortical areas. This agrees with the anatomy of the thalamocortical network that is characterized by widely divergent thalamocortical and corticothalamic connections. In this way, a very focal thalamic activity as reflected in the LFP can show high coherence with widespread cortical areas. Our data also indicates that the two electrode types, microelectrode and scalp EEG electrode, are well adapted to record manifestations of the thalamocortical network interaction at the two given sites. The comparable magnitude of thalamocortical and EEG coherence, together with the symmetric spread of latencies, suggests that the thalamus and cortex are both relevant partners in the genesis of thalamocortical rhythmicity.

The strength of the thalamocortical coupling allows to consider thalamocortical modules as functional units in brain function. Because high thalamocortical correlation is also observed experimentally in healthy brains (Lopes da Silva et al., 1973; Schürmann et al., 2000; Schreckenberger et al., 2004), it can be assumed that the concept of thalamocortical modules (Llinas et al., 1998, 1999; Destexhe, 2000) is also valid in healthy human subjects. Nevertheless, the differences between LFP and EEG spectra underline that scalp EEG is not simply driven by the thalamus but depends also on cortical influences.

Could volume conduction between LFP and EEG electrodes account for the high coherence between the signals (Wennberg and Lozano, 2003)? Against this possibility speak (1) the marked frequency dependence of coherence in Figure 1, (2) the inhomogeneous topographical distribution of coherence in Figure 3A, and (3) the variability of coherence values between subjects in Figure 3B. This is evidence that thalamocortical coherence is mediated functionally through axonal projections.

For thalamocortical theta coherence, the scalp topography was found to be maximal with frontal EEG electrodes (Fig. 3). The LFP was recorded in the thalamic nuclei VA and VLa, which are part of the pallidal-recipient thalamus (Morel et al., 1997). The nuclei VA and VLa project to the premotor and prefontal cortices (Rouiller et al., 1999). This is evidence that thalamocortical coherence, a physiological measure, is related to the anatomical distribution of thalamocortical pathways. Maximal theta coherence was not strictly ipsilateral, thus suggesting that the oscillatory activity between the thalamus and cortex may not be confined to monosynaptic hemispheric projections.

In the β band, we found dominant coherence with ipsilateral central EEG electrodes (Fig. 4, Table 1). This coherence may be mediated monosynaptically by the afferents from nucleus VLa to motor, premotor, and supplementary motor areas (Morel et al., 2005). The magnitude of β coherence, however, was below 16% and thereby much lower than in the theta band. Values of similar magnitude were reported for β coherence between scalp EEG and recording sites in the cerebellar thalamus (Marsden et al., 2000; Paradiso et al., 2004) and basal ganglia (Marsden et al., 2001; Williams et al., 2002; Fogelson et al., 2006), indicating that activity at these sites also interacts with cortical activity. Anatomically and functionally, the main output of the basal ganglia occurs via the pallidothalamic and nigrothalamic tract to the thalamus. If the magnitude of coherence is taken to indicate the strength of the functional coupling, β band coherence may appear as a secondary effect in the mechanism of PD pathophysiology.

Mechanism of PD symptoms

The pathophysiology underlying PD is initiated by the loss of dopaminergic neurons in the substantia nigra (Lozano et al., 1998). We integrate the treatment of PD in the context of a pathophysiological framework globally described as thalamocortical dysrhythmia (TCD) (Llinas et al., 1998, 1999). The following sequential set of events (Fig. 5) was proposed to explain how thalamic overinhibition leads to parkinsonian symptoms.

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Figure 5.

Schema of thalamocortical circuits. We consider the three thalamocortical modules (A–C) to be identical. Module A is depicted in more detail (Jones, 2001). One module includes the following: (1) cortical layers (I, IV, V, and VI) with pyramidal cells and one gray GABAergic inhibitory cell; (2) nucleus reticularis (RT; gray GABAergic inhibitory cells); and (3) thalamus (TH), represented by one specific cell (e.g., in VA, VL) and one non-specific cell (e.g., in CL). The specific thalamic cell projects to the apical dendrites of both layer V and VI pyramidal cells, and collaterals sustain reticular feedback and cortical feedforward inhibitions. The nonspecific thalamic cell projects to the RT and to the layer V pyramidal neuron and has a divergent connection onto the neighboring module. The corticothalamic feedback connection is depicted as intramodular onto the RT and its thalamic relay cell and divergent intra- and cross-modular onto three thalamic relay cells. There are also divergent cross-modular reticulothalamic projections. The open arrows underline the cross-modular passages through thalamocortical, corticothalamic, reticulothalamic, and corticocortical pathways.

1. As a consequence of the lack of dopamine input to the basal ganglia, the output nucleus globus pallidus is excessively active (Lozano et al., 1998) and exerts overinhibition on pallidal-recipient thalamic nuclei (VLa, VA) via the pallido–thalamic tract (Magnin et al., 2000; Anderson et al., 2003). This overinhibition results in thalamic cell membrane hyperpolarization.

2. In the hyperpolarized state, deinactivation of calcium T-channels causes thalamic neurons to fire low-threshold calcium spike (LTS) bursts (Llinas and Jahnsen, 1982; Jeanmonod et al., 1996; Llinas and Steriade, 2006). In awake experimental animals free of TCD, some authors report very few spontaneous LTS (Woody et al., 2003; Ruiz et al., 2006), whereas others have found sizeable numbers in the higher-order thalamic nuclei (Ramcharan et al., 2005). Still, compared with this latter study, LTS bursts occur with more than twice higher prevalence in the central lateral and VLa/VA thalamic nuclei of PD patients (Jeanmonod et al., 1996; Magnin et al., 2000). The rhythmic LTS bursting was found with an interburst frequency of ∼4 Hz. As an example, Figure 6 shows a LTS burst recorded in VA.

   

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   Figure 6.

   LTS burst in thalamic nucleus VA. A, LTS bursting cell recorded in thalamic nucleus VA. Within the burst, the duration of each successive interspike interval increases progressively. The amplitude of the second spike was diminished with respect to the first in 80% of bursts (for discussion, see Llinas and Steriade, 2006). B, Two-dimensional interspike interval plot, for which the abscissa indicates the interspike interval to the previous spike and the ordinate indicates the interspike interval to the subsequent spike. The points at the bottom right represent the first spikes of bursts. C, The interspike interval histogram shows a bimodal distribution of a short intraburst interspike interval and a long interburst interspike interval of ∼225 ms. D, Mean and average of all interspike intervals grouped with respect to burst length show the progressive lengthening of the interspike interval and its small variation over all bursts. The number of bursts in each category is noted below the respective curve. E, The length of the first interspike interval decreases logarithmically (r² = 0.99) as a function of the number of spikes in the burst. F, The rhythmicity of the LTS burst activity, with a lag of ∼225 ms, is confirmed by the autocorrelation histogram. ISI, Interspike interval.

3. Bursting thalamic relay neurons exert a rhythmic influence on thalamocortical modules in the theta frequency band (Hughes et al., 2004). The tight anatomical (Van Horn et al., 2000) and functional coupling between the thalamus and cortex is confirmed by the high theta coherence between the two (Fig. 1C). This coupling is sustained by thalamocorticothalamic and also by thalamoreticulothalamic and corticoreticulothalamic recurrent projections. The tendency of the thalamocortical network to maintain a given functional modality reinforces the hyperpolarized state over time (Pedroarena and Llinas, 1997). Thus, in thalamocortical modules, the presence of LTS bursts results in overproduction of theta rhythmic activity (Fig. 5A).

4. Divergent thalamocortical, corticothalamic, and reticulothalamic projections provide the anatomical substrate for diffusion of the theta overproduction to an increasing number of neighboring thalamocortical modules (Fig. 5B, theta cross-modular spread). After a sufficiently large number of thalamocortical modules have been recruited to the theta mode, this state of reduced activity may lead to negative clinical symptoms (e.g., akinesia). A theta or δ power increase may also become measurable in EEG (Fig. 1 A, Table 1) (Soikkeli et al., 1991; Stanzione et al., 1996; Tanaka et al., 2000) and MEG (Llinas et al., 1999; Salenius et al., 2002) and thalamic LFP (Fig. 1 B).

   Why does the dominant EEG peak appear in the theta range? The similarity of peak frequencies in EEG and LFP and the high thalamocortical coherence (Table 1) suggest that thalamocortical interaction has a major influence on EEG rhythmicity. LTS exert influence on the thalamocortical system, but also cortical determinants affect the EEG, for example refractory periods, axonal transmission latencies, or cortical columnar organization (Nunez et al., 2001). This could explain why the presence of LTS in patients does not result in a sharp 4 Hz spectral peak but in excess EEG oscillations spread over the entire theta band.

   Enhanced low-frequency oscillations also occur during sleep and cognitive tasks (Kahana et al., 2001), where they are considered normal. It is the long-lasting and widespread overproduction of slow rhythms in the waking brain that characterizes the pathophysiology.

5. In the final step, positive symptoms are produced (e.g., tremor) (Volkmann et al., 1992, 1996). This step is thought to be related to the reciprocal corticocortical inhibition mediated by GABAergic interneurons, which is a general feature of cortical organization (Fig. 5C). Thalamocortical modules in theta mode are in a state of reduced activity and therefore exert less collateral inhibition on other cortical modules. These modules are thereby overactivated in high (β/γ) frequencies. The importance of β/γ activity in human EEG is well documented (Neuper and Pfurtscheller, 2001; Schoffelen et al., 2005). We also find predominant thalamocortical β coherence between the thalamic site and EEG electrodes over the ipsilateral motor cortex (Fig. 4, Table 1). The interaction between modules in theta mode and modules in β/γ mode has been termed “edge effect” (Llinas et al., 1999, 2005). The mechanism has been inspired by the effect of lateral inhibition in the retina. Support for such an effect was first provided by the increased interfrequency covariation between theta and β ranges in MEG (Llinas et al., 1999). Recently, a spatiotemporal increase of β/γ activation caused by the presence of a conjoined low-frequency activation could be demonstrated in a slice preparation (Llinas et al., 2005). In this way, the negative and positive neurological symptoms are intricately related.

Conclusions

Making use of the opportunity to record LFP from the human pallidal-recipient thalamus during therapeutic intervention, our findings add to the long-standing debate on the respective contributions of the thalamus and cortex to the rhythmicity recorded in scalp EEG (Nunez et al., 2001; Schreckenberger et al., 2004). Because the recordings stem from patients with PD, these results add to the physiological basis of our understanding of parkinsonian pathophysiology (Llinas, 1984; Llinas and Pare, 1994; Jeanmonod et al., 1996; Llinas et al., 1999), which has important consequences for therapy. Selective stereotactic ablation of the pallido-thalamic tract is thought to reduce overinhibition in the thalamus and thereby abolish excessive LTS and EEG theta power during waking, both central elements of TCD. In patients with severe forms of PD resistant to drug treatment, this therapeutic lesion results in ∼60% improvement of clinical symptoms over the medicated state (Magnin et al., 2001).