Substantia nigra activity level predicts trial-to-trial adjustments in cognitive control

Effective adaptation to the demands of a changing environment requires flexible cognitive control. The medial and the lateral frontal cortices are involved in such control processes, putatively in close interplay with the BG. In particular, dopaminergic projections from the midbrain (i.e., from the substantia nigra [SN] and the ventral tegmental area) have been proposed to play a pivotal role in modulating the activity in these areas for cognitive control purposes. In that dopaminergic involvement has been strongly implicated in reinforcement learning, these ideas suggest functional links between reinforcement learning, where the outcome of actions shapes behavior over time, and cognitive control in a more general context, where no direct reward is involved. Here, we provide evidence from functional MRI in humans that activity in the SN predicts systematic subsequent trial-to-trial RT prolongations that are thought to reflect cognitive control in a stop-signal paradigm. In particular, variations in the activity level of the SN in one trial predicted the degree of RT prolongation on the subsequent trial, consistent with a modulating output signal from the SN being involved in enhancing cognitive control. This link between SN activity and subsequent behavioral adjustments lends support to theoretical accounts that propose dopaminergic control signals that shape behavior both in the presence and in the absence of direct reward. This SN-based modulatory mechanism is presumably mediated via a wider network that determines response speed in this task, including frontal and parietal control regions, along with the BG and the associated subthalamic nucleus

Involvement of the cortico-basal ganglia-thalamocortical loop in developmental stuttering

Stuttering is a complex neurodevelopmental disorder that has to date eluded a clear explication of its pathophysiological bases. In this review, we utilize the Directions Into Velocities of Articulators (DIVA) neurocomputational modeling framework to mechanistically interpret relevant findings from the behavioral and neurological literatures on stuttering. Within this theoretical framework, we propose that the primary impairment underlying stuttering behavior is malfunction in the cortico-basal ganglia-thalamocortical (hereafter, cortico-BG) loop that is responsible for initiating speech motor programs. This theoretical perspective predicts three possible loci of impaired neural processing within the cortico-BG loop that could lead to stuttering behaviors: impairment within the basal ganglia proper; impairment of axonal projections between cerebral cortex, basal ganglia, and thalamus; and impairment in cortical processing. These theoretical perspectives are presented in detail, followed by a review of empirical data that make reference to these three possibilities. We also highlight any differences that are present in the literature based on examining adults versus children, which give important insights into potential core deficits associated with stuttering versus compensatory changes that occur in the brain as a result of having stuttered for many years in the case of adults who stutter. We conclude with outstanding questions in the field and promising areas for future studies that have the potential to further advance mechanistic understanding of neural deficits underlying persistent developmental stuttering.R01 DC007683 - NIDCD NIH HHS; R01 DC011277 - NIDCD NIH HHSPublished versio

Hitting the right target : noninvasive localization of the subthalamic nucleus motor part for specific deep brain stimulation

Deep brain stimulation of the subthalamic nucleus (STN) has gained momentum as a therapy for advanced Parkinson’s disease. The stimulation effectively alleviates the patients’ typical motor symptoms on a long term, but can give rise to cognitive and psychiatric adverse effects as well. Based on primate studies, the STN has been divided into three functionally different parts, which were distinguished by their afferent and efferent connections. The largest part is the motor area, followed by an associative and a limbic area. The serious adverse effects on cognition and behavior occurring after deep brain stimulation are assumed to be caused by electrical current spread to the associative and limbic areas of the STN. Therefore, selective stimulation of the motor part of the STN seems crucial, both to obtain the best possible therapeutic effect on the motor symptoms and to minimize the debilitating effects on cognition and behavior. However, current medical imaging techniques do not yet facilitate the required accurate identification of the STN itself, let alone its different functional areas. The final target for DBS is still often adjusted using intraoperative electrophysiology. Therefore, in this thesis we aimed to improve imaging for deep brain stimulation using noninvasive MRI protocols, in order to identify the STN and its motor part. We studied the advantages and drawbacks of already available noninvasive methods to target the STN. This review did not lead to a straightforward conclusion; identification of the STN motor part remained an open question. In follow-up on this question, we investigated the possibility to distinguish the different functional STN parts based on their connectivity information. Three types of information were carefully analyzed in this thesis. First, we looked into the clustering of local diffusion information within the STN region. We visually inspected the complex diffusion profiles, derived from postmortem rat brain data with high angular resolution, and augmented this manual segmentation method using k-means and graph cuts clustering. Because the weighing of different orders of diffusion information in the traditionally used L2 norm on the orientation distribution functions (ODFs) remained an open issue, we developed a specialized distance measure, the so-called Sobolev norm. This norm does not only take into account the amplitudes of the diffusion profiles, but also their extrema. We showed it to perform better than the L2 norm on synthetic phantom data and real brain (thalamus) data. The research done on this topic facilitates better classification by clustering of gray matter structures in the (deep) brain. Secondly, we were the first to analyze the STN’s full structural connectivity, based on probabilistic fiber tracking in diffusion MRI data of healthy volunteers. The results correspond well to topical literature on STN projections. Furthermore, we assessed the structural connectivity per voxel of the STN seed region and discovered a gradient in connectivity to the premotor cortex within the STN. While going from the medial to the lateral part of the STN, the connectivity increases, confirming the expected lateral location of the STN motor part. Finally, the connectivity analysis produced evidence for the existence of a "hyperdirect" pathway between the motor cortex and the STN in humans, which is very useful for future research into stimulation targets. The results of these experiments indicate that it is possible to find the motor part of the STN as specific target for deep brain stimulation using structural connectivity information acquired in a noninvasive way. Third and last, we studied functional connectivity using resting state functional MRI data of healthy volunteers. The resulting significant clusters provided us with the first complete description of the STN’s resting state functional connectivity, which corresponds with the expectations based on available literature. Moreover, we performed a reverse regression procedure with the average time series signals in motor and limbic areas as principal regressors. The results were analyzed for each STN voxel separately and also showed mediolateral gradients in functional connectivity within the STN. The lateral STN part exhibited more motor connectivity, while the medial part seemed to be more functionally connected to limbic brain areas, as described in neuronal tracer studies. These results show that functional connectivity analysis also is a viable noninvasive method to find the motor part of the STN. The work on noninvasive MRI methods for identification of the STN and its functional parts, as presented in this thesis, thus contributes to future specific stimulation of the motor part of the STN for deep brain stimulation in patients with Parkinson’s disease. This may help to maximize the motor effects and minimize severe cognitive and psychiatric side effects

The thalamus as a putative biomarker in neurodegenerative disorders

Objective: This review provides a brief account of the clinically relevant functional neuroanatomy of the thalamus, before considering the utility of various modalities utilised to image the thalamus and technical challenges therein, and going on to provide an overview of studies utilising structural imaging techniques to map thalamic morphology in the spectrum of neurodegenerative disorders. Methods: A systematic search was conducted for peer-reviewed studies involving structural neuroimaging modalities investigating the morphology (shape and/ or size) of the thalamus in the spectrum of neurodegenerative disorders. Results: Whilst the precise role of the thalamus in the healthy brain remains unclear, there is a large body of knowledge accumulating which defines more precisely its functional connectivity within the connectome, and a burgeoning literature implicating its involvement in neurodegenerative disorders. It is proposed that correlation of clinical features with thalamic morphology (as a component of a quantifiable subcortical connectome) will provide a better understanding of neuropsychiatric dysfunction in various neurodegenerative disorders, potentially yielding clinically useful endophenotypes and disease biomarkers. Conclusions: Thalamic biomarkers in the neurodegenerative disorders have great potential to provide clinically meaningful knowledge regarding not only disease onset and progression, but may yield targets of and perhaps a way of gauging response to future disease-modifying modalities

Functional role of basal ganglia in normal and pathological behaviour

The basal ganglia (BG) appear to exert their major influence on motor functions and their related different behavioral activities. It has been proposed that the BG subserve relatively automatic responses to sensory inputs involving high-level functions like behavioural learning and procedural memory. Moreover, BG play a key role in the processes driving motor performance including emotion, motivation and reward. Severe neurological and neuropsychiatric disorders such as Parkinson’s disease (PD), ballism, Huntington’s chorea, Tourette’s syndrome and obsessive-compulsive disorder have been linked to BG dysfunctions. This article emphasizes the role of the BG in appropriate behavioural response to environmental cues suggesting that the inability to execute specific behavioural sequences may be explained by localized deficits as well as by alterations affecting complex cortico-basal ganglia circuits.peer-reviewe

Experimental and Model-based Approaches to Directional Thalamic Deep Brain Stimulation

University of Minnesota Ph.D. dissertation. September 2016. Major: Biomedical Engineering. Advisor: Matthew Johnson. 1 computer file (PDF); xii, 181 pages.Deep brain stimulation (DBS) is an effective surgical procedure for the treatment of several brain disorders. However, the clinical successes of DBS hinges on several factors. Here, we describe the development of tools and methodologies in the context of thalamic DBS for essential tremor (ET) to address three key challenges: 1) accurate localization of nuclei and fiber pathways for stimulation, 2) model-based programming of high-density DBS electrode arrays (DBSA) and 3) in vivo assessment of computational DBS model predictions. We approached the first challenge through a multimodal imaging approach, utilizing high-field (7T) susceptibility-weighted imaging and diffusion-weighted imaging data. A nonlinear image deformation algorithm was used in conjunction with probabilistic fiber tractography to segment individual thalamic sub-nuclei and reconstruct their afferent fiber pathways. We addressed the second challenge by developing subject-specific computational model-based algorithms built on maximizing population activating function values within a target region using convex optimization principles. The algorithms converged within seconds and only required as many finite-element simulations as the number of electrodes on the DBSA being modeled. For the third challenge, we recorded (in two non-human primates) unit-spike data from neurons in the vicinity of chronically implanted thalamic DBSAs before, during and after high-frequency stimulation. A novel entropy-based method was developed to quantify the degree and significance of stimulation-induced changes in neuronal firing pattern. Results indicated that neurons modulated by thalamic DBS were distributed and not confined to the immediate proximity of the active electrode. For those that were modulated by DBS, their responses increasingly shifted from firing rate modulation to firing pattern modulation with increased stimulation amplitude. Additionally, strong low-pass filtering effect was observed where <4% of DBS pulses produced phase-locked spikes in cells exhibiting significant excitatory firing pattern modulation. Finally, we quantified the spatial distribution of neurons modulated by DBS by developing a novel spherical statistical framework for analysis. Together, these tools and methodologies are poised to improve our understanding of DBS mechanisms and improve the efficacy and efficiency of DBS therapy

Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders

Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically-informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key grey matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson’s disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only grey matter landmarks. Finally we describe how this approach could be used in planning DBS treatment of psychiatric disorders

Functional contribution of the mesencephalic locomotor region to locomotion

Parce qu'il est naturel et facile de marcher, il peut sembler que cet acte soit produit aussi facilement qu'il est accompli. Au contraire, la locomotion nécessite une interaction neurale complexe entre les neurones supraspinaux, spinaux et périphériques pour obtenir une locomotion fluide et adaptée à l'environnement. La région locomotrice mésencéphalique (MLR) est un centre locomoteur supraspinal situé dans le tronc cérébral qui a notamment pour rôle d'initier la locomotion et d'induire une transition entre les allures locomotrices. Cependant, bien que cette région ait initialement été identifiée comme le noyau cunéiforme (CnF), un groupe de neurones glutamatergiques, et le noyau pédonculopontin (PPN), un groupe de neurones glutamatergiques et cholinergiques, son corrélat anatomique est encore un sujet de débat. Et alors qu'il a été prouvé que, que ce soit lors d’une stimulation de la MLR ou pour augmenter la vitesse locomotrice, la plupart des quadrupèdes présentent un large éventail d'allures locomotrices allant de la marche, au trot, jusqu’au galop, la gamme exacte des allures locomotrices chez la souris est encore inconnue. Ici, en utilisant l'analyse cinématique, nous avons d'abord décidé d'identifier d’évaluer les allures locomotrices des souris C57BL / 6. Sur la base de la symétrie de la démarche et du couplage inter-membres, nous avons identifié et caractérisé 8 allures utilisées à travers un continuum de fréquences locomotrices allant de la marche au trot puis galopant avec différents sous-types d'allures allant du plus lent au plus rapide. Certaines allures sont apparues comme attractrices d’autres sont apparues comme transitionnelles. En utilisant une analyse graphique, nous avons également démontré que les transitions entre les allures n'étaient pas aléatoires mais entièrement prévisibles. Nous avons ensuite décidé d'analyser et de caractériser les contributions fonctionnelles des populations neuronales de CnF et PPN au contrôle locomoteur. En utilisant des souris transgéniques exprimant une opsine répondant à la lumière dans les neurones glutamatergiques (Glut) ou cholinergiques (CHAT), nous avons photostimulé (ou photo-inhibé) les neurones glutamatergiques du CnF ou du PPN ou les neurones cholinergiques du PPN. Nous avons découvert que les neurones glutamatergiques du CnF initient et modulent l’allure locomotrice et accélèrent le rythme, tandis que les neurones glutamatergiques et cholinergiques du PPN le ralentissent. En initiant, modulant et en accélérant la locomotion, notre étude identifie et caractérise des populations neuronales distinctes de la MLR. Définir et décrire en profondeur la MLR semble d’autant plus urgent qu’elle est devenue récemment une cible pour traiter les symptômes survenant après une lésion de la moelle épinière ou liés à la maladie de Parkinson.Because it is natural and easy to walk, it could seem that this act is produced as easily as it is accomplished. On the contrary, locomotion requires an intricate and complex neural interaction between the supraspinal, spinal and peripheric neurons to obtain a locomotion that is smooth and adapted to the environment. The Mesencephalic Locomotor Region (MLR) is a supraspinal brainstem locomotor center that has the particular role of initiating locomotion and inducing a transition between locomotor gaits. However, although this region was initially identified as the cuneiform nucleus (CnF), a cluster of glutamatergic neurons, and the pedunculopontine nucleus (PPN), a cluster of glutamatergic and cholinergic neurons, its anatomical correlate is still a matter of debate. And while it is proven that, either under MLR stimulation or in order to increase locomotor speed, most quadrupeds exhibit a wide range of locomotor gaits from walk, to trot, to gallop, the exact range of locomotor gaits in the mouse is still unknown. Here, using kinematic analysis we first decided to identify to assess locomotor gaits C57BL/6 mice. Based on the symmetry of the gait and the inter-limb coupling, we identified and characterized 8 gaits during locomotion displayed through a continuum of locomotor frequencies, ranging from walk to trot and then to gallop with various sub-types of gaits at the slowest and highest speeds that appeared as attractors or transitional gaits. Using graph analysis, we also demonstrated that transitions between gaits were not random but entirely predictable. Then we decided to analyze and characterize the functional contributions of the CnF and PPN’s neuronal populations to locomotor control. Using transgenic mice expressing opsin in either glutamatergic (Glut) or cholinergic (CHAT) neurons, we photostimulated (or photoinhibited) glutamatergic neurons of the CnF or PPN or cholinergic neurons of the PPN. We discovered that glutamatergic CnF neurons initiate and modulate the locomotor pattern, and accelerate the rhythm, while glutamatergic and cholinergic PPN neurons decelerate it. By initiating, modulating, and accelerating locomotion, our study identifies and characterizes distinct neuronal populations of the MLR. Describing and defining thoroughly the MLR seems all the more urgent since it has recently become a target for spinal cord injury and Parkinson’s disease treatment