Understanding Parkinsonian Handwriting Through a Computational Model of Basal Ganglia

Garipelli, Gangadhar; Joseph, Denny; Chakravarthy, V. Srinivasa

doi:10.1162/neco.2008.03-07-498

Cited by 24 publications

(18 citation statements)

References 39 publications

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“…Also, 3/5 bvFTD(CBD) patients were found to have micrographia, while no bvFTD(Pick’s) patients exhibited this symptom. Micrographia has been associated with white matter lesions (Scolding & Lees, 1994; Ishihara et al, 2006) and with basal ganglia lesions (Kuoppamaki et al, 2005; Gangadhar, Joseph, & Chakravarthy, 2008), though such lesions were seen in both bvFTD groups in this study. There was also a trend towards bvFTD(CBD) patients having more typical features of parkinsonism, but the numbers were so small it was not clear whether this was truly a signal.…”

Section: Discussioncontrasting

confidence: 70%

Behavioral Variant Frontotemporal Dementia with Corticobasal Degeneration Pathology: Phenotypic Comparison to bvFTD with Pick’s Disease

et al. 2011

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Patients with corticobasal degeneration (CBD) pathology present with diverse clinical syndromes also associated with other neuropathologies, including corticobasal syndrome, progressive nonfluent aphasia, and an Alzheimer’s-type dementia. Some present with behavioral variant frontotemporal dementia (bvFTD), though this subtype still requires more detailed phenotypic characterization. All patients with CBD pathology and clinical assessment were reviewed (N=17) and selected if they initially met criteria for bvFTD [bvFTD(CBD): N=5]. Available bvFTD patients with Pick’s [bvFTD(Pick’s): N=5] were selected as controls. Patients were also compared to healthy older controls [N=53] on neuropsychological and neuroimaging measures. At initial presentation, bvFTD(CBD) showed few neuropsychological or motor differences from bvFTD(Pick’s). Neuropsychiatrically, they were predominantly apathetic with less florid social disinhibition and eating disturbances, and were more anxious than bvFTD(Pick’s) patients. Voxel-based morphometry revealed similar patterns of predominantly frontal atrophy between bvFTD groups, though overall degree of atrophy was less severe in bvFTD(CBD), who also showed comparative preservation of the frontoinsular rim, with dorsal > ventral frontal atrophy, and sparing of temporal and parietal structures relative to bvFTD(Pick’s) patients. Despite remarkable overlap between the two patient types, bvFTD patients with underlying CBD pathology show subtle clinical features that may distinguish them from patients with Pick’s disease neuropathology.

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Section: Discussioncontrasting

confidence: 70%

Behavioral Variant Frontotemporal Dementia with Corticobasal Degeneration Pathology: Phenotypic Comparison to bvFTD with Pick’s Disease

et al. 2011

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“…Experimental studies of activity in the STN and the GPe revealed that under dopamine-depleted circumstances (analogous to Parkinsonian conditions), activity of these nuclei exhibited, though not much reduction in firing rate, a dramatic increase in correlations among neurons (Bergman et al 1994;Brown et al 2001). In modeling studies, correlated activity of neurons of the STN-GPe loop has been functionally linked to Parkinsonian tremor frequencies (Terman et al 2002) and to Parkinsonian handwriting distortions (Gangadhar et al 2008).…”

Section: 1mentioning

confidence: 99%

“…As an extension to the motor preparation studies presented in (Gangadhar et al 2008), our group had recently studied preparation in Parkinsonian conditions (unpublished results). In the model, we identify the drive signal from BG to SMA in the model with the phasic dopamine burst which is known to occur in the beginning of or before movement.…”

Section: Motor Preparationmentioning

confidence: 99%

What do the basal ganglia do? A modeling perspective

2010

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Basal ganglia (BG) constitute a network of seven deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing, etc. In spite of the immense amount of data available today, researchers continue to wonder how a single deep brain circuit performs such a bewildering range of functions. Computational models of BG have focused on individual functions and fail to give an integrative picture of BG function. A major breakthrough in our understanding of BG function is perhaps the insight that activities of mesencephalic dopaminergic cells represent some form of 'reward' to the organism. This insight enabled application of tools from 'reinforcement learning,' a branch of machine learning, in the study of BG function. Nevertheless, in spite of these bright spots, we are far from the goal of arriving at a comprehensive understanding of these 'mysterious nuclei.' A comprehensive knowledge of BG function has the potential to radically alter treatment and management of a variety of BG-related neurological disorders (Parkinson's disease, Huntington's chorea, etc.) and neuropsychiatric disorders (schizophrenia, obsessive compulsive disorder, etc.) also. In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei.

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“…Interestingly, the proposed approach to Basal ganglia modeling wherein the Direct Pathway subserves exploitation while the Indirect Pathway subserves exploration, has been applied in the past to explain a range of PD motor deficits like those observed in handwriting [55], reaching movements [22], and saccadic movements [56]. The same approach adapted to model avian homolog of basal ganglia, was able to explain impairments in bird song generation [57].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Contributions of Basal Ganglia and Hippocampus to Spatial Navigation Using Reinforcement Learning

2012

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A computational neural model that describes the competing roles of Basal Ganglia and Hippocampus in spatial navigation is presented. Model performance is evaluated on a simulated Morris water maze explored by a model rat. Cue-based and place-based navigational strategies, thought to be subserved by the Basal ganglia and Hippocampus respectively, are described. In cue-based navigation, the model rat learns to directly head towards a visible target, while in place-based navigation the target position is represented in terms of spatial context provided by an array of poles placed around the pool. Learning is formulated within the framework of Reinforcement Learning, with the nigrostriatal dopamine signal playing the role of Temporal Difference Error. Navigation inherently involves two apparently contradictory movements: goal oriented movements vs. random, wandering movements. The model hypothesizes that while the goal-directedness is determined by the gradient in Value function, randomness is driven by the complex activity of the SubThalamic Nucleus (STN)-Globus Pallidus externa (GPe) system. Each navigational system is associated with a Critic, prescribing actions that maximize value gradients for the corresponding system. In the integrated system, that incorporates both cue-based and place-based forms of navigation, navigation at a given position is determined by the system whose value function is greater at that position. The proposed model describes the experimental results of [1], a lesion-study that investigates the competition between cue-based and place-based navigational systems. The present study also examines impaired navigational performance under Parkinsonian-like conditions. The integrated navigational system, operated under dopamine-deficient conditions, exhibits increased escape latency as was observed in experimental literature describing MPTP model rats navigating a water maze.

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Understanding Parkinsonian Handwriting Through a Computational Model of Basal Ganglia

Cited by 24 publications

References 39 publications

Behavioral Variant Frontotemporal Dementia with Corticobasal Degeneration Pathology: Phenotypic Comparison to bvFTD with Pick’s Disease

Behavioral Variant Frontotemporal Dementia with Corticobasal Degeneration Pathology: Phenotypic Comparison to bvFTD with Pick’s Disease

What do the basal ganglia do? A modeling perspective

Modeling the Contributions of Basal Ganglia and Hippocampus to Spatial Navigation Using Reinforcement Learning

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