Nonlinear interactions in the thalamocortical loop in essential tremor: A model-based frequency domain analysis.

There is increasing evidence to suggest that essential tremor has a central origin. Different structures appear to be part of the central tremorogenic network, including the motor cortex, the thalamus and the cerebellum. Some studies using electroencephalogram (EEG) and magnetoencephalography (MEG) show linear association in the tremor frequency between the motor cortex and the contralateral tremor electromyography (EMG). Additionally, high thalamomuscular coherence is found with the use of thalamic local field potential (LFP) recordings and tremulous EMG in patients undergoing surgery for deep brain stimulation (DBS). Despite a well-established reciprocal anatomical connection between the thalamus and cortex, the functional association between the two structures during "tremor-on" periods remains elusive. Thalamic (Vim) LFPs, ipsilateral scalp EEG from the sensorimotor cortex and contralateral tremor arm EMG recordings were obtained from two patients with essential tremor who had undergone successful surgery for DBS. Coherence analysis shows a strong linear association between thalamic LFPs and contralateral tremor EMG, but the relationship between the EEG and the thalamus is much less clear. These measurements were then analyzed by constructing a novel parametric nonlinear autoregressive with exogenous input (NARX) model. This new approach uncovered two distinct and not overlapping frequency "channels" of communication between Vim thalamus and the ipsilateral motor cortex, defining robustly "tremor-on" versus "tremor-off" states. The associated estimated nonlinear time lags also showed non-overlapping values between the two states, with longer corticothalamic lags (exceeding 50ms) in the tremor active state, suggesting involvement of an indirect multisynaptic loop. The results reveal the importance of the nonlinear interactions between cortical and subcortical areas in the central motor network of essential tremor. This work is important because it demonstrates for the first time that in essential tremor the functional interrelationships between the cortex and thalamus should not be sought exclusively within individual frequencies but more importantly between cross-frequency nonlinear interactions. Should our results be successfully reproduced on a bigger cohort of patients with essential tremor, our approach could be used to create an on-demand closed-loop DBS device, able to automatically activate when the tremor is on

The role of oscillation population activity in cortico-basal ganglia circuits.

The basal ganglia (BG) are a group of subcortical brain nuclei that are anatomically situated between the cortex and thalamus. Hitherto, models of basal ganglia function have been based solely on the anatomical connectivity and changes in the rate of neurons mediated by inhibitory and excitatory neurotransmitter interactions and modulated by dopamine. Depletion of striatal dopamine as occurs in Parkinson's Disease (PD) however, leads primarily to changes in the rhythmicity of basal ganglia neurons. The general aim of this thesis is to use frontal electrocorticogram (ECoG) and basal ganglia local field potential (LFP) recordings in the rat to further investigate the putative role for oscillations and synchronisation in these structures in the healthy and dopamine depleted brain. In the awake animal, lesion of the SNc lead to a dramatic increase in the power and synchronisation of P-frequency band oscillations in the cortex and subthalamic nucleus (STN) compared to the sham lesioned animal. These results are highly similar to those in human patients and provide further evidence for a direct pathophysological role for p-frequency band oscillations in PD. In the healthy, anaesthetised animal, LFPs recorded in the STN, globus pallidus (GP) and substantia nigra pars reticulata (SNr) were all found to be coherent with the ECoG. A detailed analysis of the interdependence and direction of these activities during two different brain states, prominent slow wave activity (SWA) and global activation, lead to the hypothesis that there were state dependant changes in the dominance of the cortico-subthalamic and cortico-striatal pathways. Multiple LFP recordings in the striatum and GP provided further evidence for this hypothesis, as coherence between the ECoG and GP was found to be dependent on the striatum. Together these results suggest that oscillations and synchronisation may mediate information flow in cortico-basal ganglia networks in both health and disease

Motor imagery and motor illusion: from plasticity to a translational approach

Motor imagery e illusione motoria: dalla plasticit\ue0 ad un approccio traslazional

Characterization of neurological disorders using evolutionary algorithms

The life expectancy increasing, in the last few decades, leads to a large diffusion of neurodegenerative age-related diseases such as Parkinson’s disease. Neurodegenerative diseases are part of the huge category of neurological disorders, which comprises all the disorders affecting the central nervous system. These conditions have a terrible impact on life quality of both patients and their families, but also on the costs associated to the society for their diagnosis and management. In order to reduce their impact on individuals and society, new better strategies for the diagnosis and monitoring of neurological disorders need to be considered. The main aim of this study is investigating the use of artificial intelligence techniques as a tool to help the doctors in the diagnosis and the monitoring of two specific neurological disorders (Parkinson’s disease and dystonia), for which no objective clinical assessments exist. The evolutionary algorithms are chosen as the artificial intelligence technique to evolve the best classifiers. The classifiers evolved by the chosen technique are then compared with those evolved by two popular well-known techniques: artificial neural network and support vector machine. All the evolved classifiers are not only able to distinguish among patients and healthy subjects but also among different subgroups of patients. For Parkinson’s disease: two different cognitive impairment subgroups of patients are considered, with the aim of an early diagnosis and a better monitoring. For dystonia: two kinds of dystonia patients are considered (organic and functional) to have a better insight in the division of the two groups. The results obtained for Parkinson’s disease are encouraging and evidenced some differences among the cognitive impairment subgroups. Dystonia results are not satisfactory at this stage, but the study presents some limitations that could be overcome in future work

Functional imaging of response selection

The functions of the prefrontal cortex remain controversial. Electrophysio- logical and lesion studies in monkeys have emphasised a role in working memory. In contrast, human functional neuroimaging studies and neuropsychology have emphasised a role in executive processes and volition. An alternative interpretation of the role of the prefrontal cortex is proposed in this thesis: that the prefrontal cortex mediates the attentional selection of sensory, mnemonic and motor representations in non-prefrontal cortex. This hypothesis is tested in a series of functional imaging experiments. In the first two experiments (chapters 4 and 5), event-related functional magnetic resonance imaging (fMRI) was used to re-examine the role of the prefrontal cortex in spatial and spatio-temporal working memory. Maintenance of information in memory was associated with activation of posterior prefrontal cortex (area 8). In contrast, the selection of an item from several remembered items was associated with activation of the middle and anterior parts of the prefrontal cortex (including area 46). To test the generalisation of 'selection' as a function of prefrontal cortex, experiment three (chapter 6) required subjects to select either a finger to move, or a colour from a multicolour display. Free selection was associated with activation of the prefrontal cortex (area 46) bilaterally, regardless of sensory or motor modality. The selection of voluntary actions has been proposed to depend on top-down modulation of motor regions by prefrontal cortex. The fourth and fifth experiments used structural equation modelling of fMRI time -series to measure the effective connectivity among prefrontal, premotor and parietal cortex. In young (chapter 7) and old (chapter 8) normal subjects, attention to action specifically enhanced coupling between prefrontal and premotor regions. This effect was not seen in patients with Parkinson's disease (chapter 8). Lastly, positron emission tomography was used to study planning in the Tower of London task, a common clinical measure of prefrontal function. Several variants of the task were developed, to distinguish the neural basis of the task's multiple cognitive components (chapter 9). The prefrontal cortex was activated in association with generation, selection or memory for moves, rather than planning towards a specified goal. The results support a generalised role in attentional selection of neuronal representations, whether stimuli, actions, or remembered items. The hypothesised attentional selection of responses is consistent with the activation of prefrontal cortex in working memory tasks and during attention to voluntary action. This role is compatible with the neurophysiological properties of individual neurons in the prefrontal cortex and the results of neuroimaging and lesion studies

Objective assessment of upper limb motor symptoms in Parkinson's Disease using body-worn sensors

MD ThesisBackground There is a need for an objective method of symptom assessment in Parkinson's disease (PD) to enable better treatment decisions and to aid evaluation of new treatments. Current assessment methods; patient-completed symptom diaries and clinical rating scales, have limitations. Accelerometers (sensors capable of capturing data on human movement) and analysis using artificial neural networks (ANNs) have shown potential as a method of motor symptom evaluation in PD. It is unknown whether symptom monitoring with body-worn sensors is acceptable to PD patients due to a lack of previous research. Methods 34 participants with PD wore bilateral wrist-worn accelerometers for 4 hours in a research facility (phase 1) and then for 7 days in their homes (phase 2) whilst also completing symptom diaries. An ANN designed to predict a patient’s motor status, was developed and trained based on accelerometer data during phase 2. ANN performance was evaluated (leave-one-out approach) against patient-completed symptom diaries during phase 2, and against clinician rating of disease state during phase 1 observations. Participants’ views regarding the sensors were obtained via a Likert-style questionnaire completed after each phase. Differences in responses between phases were assessed for using the Wilcoxon rank-sum test. Results ANN-derived values of the proportion of time in each disease state (phase 2), showed strong, significant correlations with values derived from patient-completed symptom diaries. ANN disease state recognition during phase 1 was sub-optimal. High concordance with sensors was seen. Prolonged wearing of the sensors did not adversely affect participants’ opinions on the wearability of the sensors, when compared to their responses following phase 1 Conclusions Accelerometers and ANNs produced results comparable to those of symptom diaries. Our findings suggest that long-term monitoring with wrist-worn sensors is acceptable to PD patients

Approche expérimentale de la physiopathologie des dyskinésies L-Dopa induites dans la maladie de Parkinson : comparaison de la cible classique, le striatum avec l’ensemble du cerveau

The gold standard treatment for Parkinson’s disease (PD) remains the dopamine precursor L- 3,4-dihydroxyphenylalanine (L-Dopa). Long-term L-Dopa treatment systematically leads to abnormal involuntary movements (AIMs) called L-Dopa-induced dyskinesia (LID). These manifestations first led to investigate the neuronal dysfunctions in the motor regions of thebasal ganglia and unravelled an overexpression of ΔFosB, ARC, Zif268 and FRA2 immediate-early genes (IEG) in the dopamine-depleted striatum of dyskinetic rats. However, other several dopaminoceptive structures, likely affected by the exogenously produced dopamine, have been neglected although they might play a key role in mediating LID. Hence, we assessed the expression of ΔFosB, ARC, FRA2 and Zif268 IEGs in the whole brain of dyskinetic rats compared to non-dyskinetic ones. Such approach shed light notably upon 9 structures located outside of the basal ganglia displaying an IEG overexpression. Among them, the dorsolateral bed nucleus of the stria terminalis (dlBST) and the lateralhabenula (LHb) displayed a significant correlation between ΔFosB expression and LID severity. We therefore postulated that these structures might play a role in LID manifestation. Therefore, to assess dlBST and LHb causal roles upon LID severity, we inhibited the electrical activity of FosB/ΔFosB-expressing neurons using the selective Daun02/β- galactosidase inactivation method that we previously validated in a well known structure involve in LID: the striatum. Interestingly, the inactivation of dlBST and LHb ΔfosBexpressing neurons alleviated LID severity and increased the beneficial effect of L-Dopa in dyskinetic rats. Remarkably, BST involvement in LID was confirmed in the gold standard model of LID, the dyskinetic MPTP-lesioned macaque. Altogether, our results highlight for the first time the functional involvement of 2 structures.Le traitement de référence de la maladie de Parkinson (MP) reste l’utilisation du précurseurdirect de la dopamine: la L-3,4-dihydroxyphenylalanine (L-Dopa). Le traitement chroniquedes patients parkinsoniens à la L-Dopa induit, en revanche, systématiquement desmouvements involontaires anormaux que l’on qualifie de dyskinésies induites par la L-Dopa(DIL). L’étude de l’expression des dyskinésies s’est essentiellement focalisée sur lesdysfonctions neuronales engendrées dans les régions motrices des ganglions de la base et apermis de révéler une surexpression significative de gènes de réponse précoce (GRP) tels que: ΔFosB, ARC, Zif268 et FRA2 dans le striatum de rats dyskinétiques traités chroniquement à la L–Dopa. En revanche, plusieurs autres régions dopaminoceptives, probablement affectées par la dopamine exogène nouvellement synthétisée, ont été négligées alors qu’elles pourraient jouer un rôle clé dans l’expression des dyskinésies. Par conséquent, nous avons quantifié l’expression de ΔFosB, ARC, FRA2 et Zif268 dans l’ensemble du cerveau de rats dyskinétiques que nous avons comparée à des rats non-dyskinétiques. Cette approche nous a permis d’identifier 9 structures, localisées en dehors des ganglions de la base, présentant une surexpression d’au moins 3 des GRPs cités ci-dessus. Parmi ces structures, le domaine dorsolatéral du « bed nucleus of the stria terminalis » (dlBST) et l’habenula latérale (LHb) montrent une corrélation significative entre l’expression de ΔFosB et la sévérité des dyskinésies. Nous avons donc fait l’hypothèse que ces 2 structures pouvaient être impliquées dans l’expression des dyskinésies. Par conséquent, pour évaluer le rôle potentiel du dlBST et de la LHb dans les dyskinésies, nous avons inhibé l’activité électrique des neurones exprimant FosB/ΔFosB en utilisant la méthode d’inactivation sélective du Daun02/ß-galactosidase que nous avons précédemment validée dans une structure bien connue pour être impliquée dans les dyskinésies: le striatum. Nous avons démontré que l’inhibition de ces neurones, à la fois dans le dlBST et la LHb, diminuait la sévérité des dyskinésies sans affecter l’effet bénéfique de la L-Dopa chez les rats dyskinétiques. Nous avons ensuite pu confirmer l’implication du dlBST grâce au model de référence des dyskinésies: le macaque dyskinétique lésé au MPTP. L’ensemble de ces résultats nous a ainsi permis de montrer, pour la première fois, l’implication fonctionnelle de 2 structures externes aux ganglions de la base dans l’expression des dyskinésies, offrant de nouvelles perspectives thérapeutiques

Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson’s Disease

It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms