Brain Imaging in Catatonia: Current Findings and a Pathophysiologic Model

CNS Spectrums ◽  
2000 ◽  
Vol 5 (7) ◽  
pp. 34-46 ◽  
Author(s):  
Georg Northoff
Keyword(s):  
Neural Network ◽  
Basal Ganglia ◽  
Brain Imaging ◽  
Posterior Parietal Cortex ◽  
Subjective Experiences ◽  
Behavioral Alterations ◽  
Subcortical Structures ◽  
Posterior Parietal ◽  
Clinical Phenomenology ◽  
In The Beginning

AbstractKarl Ludwig Kahlbaum originally described catatonia as a psychomotor disease that encompassed motor, affective, and behavioral symptoms. In the beginning of the 20th century, catatonia was considered to be the motoric manifestation of schizophrenia; therefore, neuropathologic research mostly focused on neuroanatomic substrates (ie, the basal ganglia underlying the generation of movements). Even though some alterations were found in basal ganglia, the findings in these subcortical structures are not consistent. Recently, there has been a reemergence of interest into researching catatonia. Brain imaging studies have shown major and specific alterations in a right hemispheric neural network that includes the medial and lateral orbitofrontal and posterior parietal cortex. This neural network may be abnormally modulated by altered functional interactions between γ-aminobutyric acid (GABA)-ergic and glutamatergic transmission. This may account for the interrelationship among motor, emotional, and behavioral alterations observed in both clinical phenomenology and the subjective experiences of patients with catatonia. Such functional interrelationships should be explored in further detail in catatonia, which may also serve as a paradigmatic model for the investigation of psychomotor and brain function in general.

scholarly journals Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

2019 ◽  
Author(s):  
Benyamin Haghi ◽  
Spencer Kellis ◽  
Sahil Shah ◽  
Maitreyi Ashok ◽  
Luke Bashford ◽  
...  
Keyword(s):  
Neural Network ◽  
Posterior Parietal Cortex ◽  
Ground Truth ◽  
Open Loop ◽  
Neural Data ◽  
State Dynamic ◽  
History Of ◽  
Posterior Parietal ◽  
Machine Interface ◽  
Memory Accesses

AbstractWe present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

A Neural Network Model of Cortical Activity during Reaching

1993 ◽  
Vol 5 (1) ◽  
pp. 14-33 ◽  
Author(s):  
Ronald Kettner ◽  
Joanne Marcario ◽  
Nicholas Port
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Cortical Neurons ◽  
Posterior Parietal Cortex ◽  
Large Population ◽  
Association Cortex ◽  
Preferred Direction ◽  
The Neural Network ◽  
Posterior Parietal

A neural network model that produces many of the directional and spatial response properties that have been observed for cortical neurons in monkeys moving toward targets in space is described. These include motor cortex units with broad tuning in a single preferred direction, approximately linear variation in activity for different hold positions, and approximate invariance in preferred direction for different starting points in space. Association cortex units in the model are sometimes irregular and reminiscent of neurons observed in visually responsive brain areas such as the posterior parietal cortex. The model is also compatible with population analyses performed on motor cortical neurons. Across network units, the distribution of preferred directions is uniformly distributed in directional space, and the degree of tuning and response magnitude vary from unit to unit. A population code used to predict accurately the direction of arm movements from a large population of coarsely tuned individual neurons allows predictions using a simulated population of unit responses obtained from the neural network model. This code works for different starting locations in space using the same parameters.

Sensorimotor cerebral activation during optokinetic nystagmus

Neurology ◽  
1997 ◽  
Vol 49 (5) ◽  
pp. 1370-1377 ◽  
Author(s):  
Stefan F. Bucher ◽  
Marianne Dieterich ◽  
Klaus C. Seelos ◽  
Thomas Brandt
Keyword(s):  
Posterior Parietal Cortex ◽  
Optokinetic Nystagmus ◽  
Object Motion ◽  
Cortical Activation ◽  
Subcortical Structures ◽  
Cerebral Activation ◽  
Activation Patterns ◽  
Medial Superior Temporal ◽  
Posterior Parietal ◽  
Self Motion

Self-motion or object motion can elicit optokinetic nystagmus (OKN), which is an integral part of dynamic spatial orientation. We used functional MR imaging during horizontal OKN to study cerebral activation patterns in sensory and ocular motor areas in 10 subjects. We found activation bilaterally in the primary visual cortex, the motion-sensitive areas in the occipitotemporal cortex (the middle temporal and medial superior temporal areas), and in areas known to control several types of saccades such as the precentral and posterior median frontal gyrus, the posterior parietal cortex, and the medial part of the superior frontal gyrus (frontal, parietal, and supplementary eye fields). Additionally, we observed cortical activation in the anterior and posterior parts of the insula and in the prefrontal cortex. Bilateral activation of subcortical structures such as the putamen, globus pallidus, caudate nucleus, and the thalamus traced the efferent pathways of OKN down to the brainstem. Functional MRI during OKN revealed a complex cerebral network of sensorimotor cortical and subcortical activation.

scholarly journals Spreading Waves of a Reduced Diffusion Coefficient of Water in Normal and Ischemic Rat Brain

1995 ◽  
Vol 15 (2) ◽  
pp. 179-187 ◽  
Author(s):  
Yasuhiro Hasegawa ◽  
Lawrence L. Latour ◽  
James E. Formato ◽  
Christopher H. Sotak ◽  
Marc Fisher
Keyword(s):  
Diffusion Coefficient ◽  
Magnetic Resonance ◽  
Potassium Chloride ◽  
Rat Brain ◽  
Posterior Parietal Cortex ◽  
Focal Cerebral Ischemia ◽  
Cortical Stimulation ◽  
Subcortical Structures ◽  
Ischemic Lesion ◽  
Posterior Parietal

Using echo planar diffusion-weighted magnetic resonance imaging, we measured three-dimensional changes in the apparent diffusion coefficient (ADC) of water in eight contiguous coronal slices, encompassing the entire rat brain, before and after local cortical stimulation. We applied chemical (potassium chloride application; n = 6) and mechanical (needle stab; n = 4) stimulations to the right posterior parietal rat cortex. In all animals in which potassium chloride or the needle stab was applied, a region of decreased ADC values to a mean of 0.45 ± 0.03 × 10−5cm2/s occurred. These reduced ADC levels appeared in the posterior parietal cortex within 1 min after cortical stimulation and the change recovered within 1 min. Then a ripple-like movement of similar changes developed across the unilateral cortex. This change was localized to the cortex and no significant ADC changes occurred in subcortical structures. The propagating speed of this movement was 3.4 ± 0.5 mm/min. These findings are compatible with spreading depression as observed electrophysiologically. Similar ADC changes occurred in areas distinct from the ischemic lesion in 3 of 12 animals subjected to focal cerebral ischemia. This magnetic resonance method could detect spreading ADC decline if it occurred in human diseases including brain ischemia.

Posterior parietal cortex mediates encoding processes in change blindness

2009 ◽  
Author(s):  
Philip Tseng ◽  
Cassidy Sterling ◽  
Adam Cooper ◽  
Bruce Bridgeman ◽  
Neil G. Muggleton ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Change Blindness ◽  
Posterior Parietal

The Grasp Cell Hypothesis: Near-miss effect points to common neurological machinery in posterior parietal cortex for controllable objects and concepts

2018 ◽  
Author(s):  
Imogen M Kruse
Keyword(s):  
Parietal Cortex ◽  
Posterior Parietal Cortex ◽  
Near Miss ◽  
Illusion Of Control ◽  
Gambling Behaviour ◽  
Drug Seeking ◽  
Spatial Influence ◽  
Reward Probability ◽  
Posterior Parietal ◽  
Action Value

The near-miss effect in gambling behaviour occurs when an outcome which is close to a win outcome invigorates gambling behaviour notwithstanding lack of associated reward. In this paper I postulate that the processing of concepts which are deemed controllable is rooted in neurological machinery located in the posterior parietal cortex specialised for the processing of objects which are immediately actionable or controllable because they are within reach. I theorise that the use of a common machinery facilitates spatial influence on the perception of concepts such that the win outcome which is 'almost complete' is perceived as being 'almost within reach'. The perceived realisability of the win increases subjective reward probability and the associated expected action value which impacts decision-making and behaviour. This novel hypothesis is the first to offer a neurological model which can comprehensively explain many empirical findings associated with the near-miss effect as well as other gambling phenomena such as the ‘illusion of control’. Furthermore, when extended to other compulsive behaviours such as drug addiction, the model can offer an explanation for continued drug-seeking following devaluation and for the increase in cravings in response to perceived opportunity to self-administer, neither of which can be explained by simple reinforcement models alone. This paper therefore provides an innovative and unifying perspective for the study and treatment of behavioural and substance addictions.

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