scholarly journals Formulaic Language in Parkinson's Disease and Alzheimer's Disease: Complementary Effects of Subcortical and Cortical Dysfunction

2015 ◽  
Vol 58 (5) ◽  
pp. 1493-1507 ◽  
Author(s):  
Diana Van Lancker Sidtis ◽  
JiHee Choi ◽  
Amy Alken ◽  
John J. Sidtis
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Right Hemisphere ◽  
Conversational Speech ◽  
Formulaic Language ◽  
Healthy Control ◽  
The Right

Purpose The production of formulaic expressions (conversational speech formulas, pause fillers, idioms, and other fixed expressions) is excessive in the left hemisphere and deficient in the right hemisphere and in subcortical stroke. Speakers with Alzheimer's disease (AD), having functional basal ganglia, reveal abnormally high proportions of formulaic language. Persons with Parkinson's disease (PD), having dysfunctional basal ganglia, were predicted to show impoverished formulaic expressions in contrast to speakers with AD. This study compared participants with PD, participants with AD, and healthy control (HC) participants on protocols probing production and comprehension of formulaic expressions. Method Spontaneous speech samples were recorded from 16 individuals with PD, 12 individuals with AD, and 18 HC speakers. Structured tests were then administered as probes of comprehension. Results The PD group had lower proportions of formulaic expressions compared with the AD and HC groups. Comprehension testing yielded opposite contrasts: participants with PD showed significantly higher performance compared with participants with AD and did not differ from HC participants. Conclusions The finding that PD produced lower proportions of formulaic expressions compared with AD and HC supports the view that subcortical nuclei modulate the production of formulaic expressions. Contrasting results on formal testing of comprehension, whereby participants with AD performed significantly worse than participants with PD and HC participants, indicate differential effects on procedural and declarative knowledge associated with these neurological conditions.

The Effect of Neurodegeneration on Visuomotor Behavior in Alzheimer’s Disease and Parkinson’s Disease

Motor Control ◽  
2016 ◽  
Vol 20 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Casper de Boer ◽  
Johannes van der Steen ◽  
Francesco Mattace-Raso ◽  
Agnita J.W. Boon ◽  
Johan J.M. Pel
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Cognitive Complexity ◽  
Quantitative Description ◽  
Visuomotor Behavior ◽  
Healthy Control ◽  
Hand Coordination ◽  
Disease Specific

The early stages of neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) involve deterioration of specific (visuo)motor functions. The aim of the current study was to investigate differences in visuomotor behavior between age-matched groups of 17 patients with AD, 17 patients with PD, and 20 healthy control subjects across three eye-hand-coordination tasks of different cognitive complexity. In two of three tasks, timing and execution parameters of eyes and hand significantly differed between groups. Timing and execution parameters of the eyes and hands could potentially give a quantitative description of disease specific deficits in the spatial and temporal domains and may serve as a tool to monitor disease progression in AD and PD populations.

scholarly journals Elevated caudate connectivity in cognitively normal Parkinson’s disease patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Natalie Wright ◽  
Abrar Alhindi ◽  
Colleen Millikin ◽  
Mandana Modirrousta ◽  
Sean Udow ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Functional Connectivity ◽  
Caudate Nucleus ◽  
Strong Positive Correlation ◽  
Healthy Controls ◽  
Cognitively Normal ◽  
The Right

Abstract Mild cognitive impairment (MCI) is common in Parkinson’s disease patients. However, its underlying mechanism is not well understood, which has hindered new treatment discoveries specific to MCI. The aim of this study was to investigate functional connectivity changes of the caudate nucleus in cognitively impaired Parkinson’s patients. We recruited 18 Parkinson’s disease patients—10 PDNC [normal cognition Parkinson’s disease; Montreal Cognitive Assessment (MoCA) ≥ 26], 8 PDLC (low cognition Parkinson’s disease; MoCA < 26) —and 10 age-matched healthy controls. All subjects were scanned with resting-state functional magnetic resonance imaging (MRI) and perfusion MRI. We analyzed these data for graph theory metrics and Alzheimer’s disease-like pattern score, respectively. A strong positive correlation was found between the functional connectivity of the right caudate nucleus and MoCA scores in Parkinson’s patient groups, but not in healthy control subjects. Interestingly, PDNC’s functional connectivity of the right caudate was significantly higher than both PDLC and healthy controls, while PDLC and healthy controls were not significantly different from each other. We found that Alzheimer’s disease-like metabolic/perfusion pattern score correlated with MoCA scores in healthy controls, but not in Parkinson’s disease. Increased caudate connectivity may be related to a compensatory mechanism found in cognitively normal patients with Parkinson’s disease. Our findings support and complement the dual syndrome hypothesis.

Neurobehavioral Consequences Associated with Long Term Tramadol Utilization and Pathological Mechanisms

2020 ◽  
Vol 18 (10) ◽  
pp. 758-768 ◽  
Author(s):  
Khadga Raj ◽  
Pooja Chawla ◽  
Shamsher Singh
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Serotonin Reuptake ◽  
Selective Serotonin Reuptake Inhibitors ◽  
Serotonin Syndrome ◽  
Dopamine Synthesis ◽  
Synthetic Analog

: Tramadol is a synthetic analog of codeine used to treat pain of moderate to severe intensity and is reported to have neurotoxic potential. At therapeutic dose, tramadol does not cause major side effects in comparison to other opioid analgesics, and is useful for the management of neurological problems like anxiety and depression. Long term utilization of tramadol is associated with various neurological disorders like seizures, serotonin syndrome, Alzheimer’s disease and Parkinson’s disease. Tramadol produces seizures through inhibition of nitric oxide, serotonin reuptake and inhibitory effects on GABA receptors. Extensive tramadol intake alters redox balance through elevating lipid peroxidation and free radical leading to neurotoxicity and produces neurobehavioral deficits. During Alzheimer’s disease progression, low level of intracellular signalling molecules like cGMP, cAMP, PKC and PKA affect both learning and memory. Pharmacologically tramadol produces actions similar to Selective Serotonin Reuptake Inhibitors (SSRIs), increasing the concentration of serotonin, which causes serotonin syndrome. In addition, tramadol also inhibits GABAA receptors in the CNS has been evidenced to interfere with dopamine synthesis and release, responsible for motor symptoms. The reduced level of dopamine may produce bradykinesia and tremors which are chief motor abnormalities in Parkinson’s Disease (PD).

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

Generalized Rhythmic Delta Activity Frontally Predominant Differentiates Dementia With Lewy Bodies From Alzheimer's Disease and Parkinson's Disease Dementia: A Conventional Electroencephalography Visual Analysis

2021 ◽  
pp. 155005942199714
Author(s):  
Lucia Zinno ◽  
Anna Negrotti ◽  
Chiara Falzoi ◽  
Giovanni Messa ◽  
Matteo Goldoni ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Dementia With Lewy Bodies ◽  
Visual Analysis ◽  
Lewy Bodies ◽  
Delta Activity ◽  
Rhythmic Delta Activity

Introduction. An easily accessible and inexpensive neurophysiological technique such as conventional electroencephalography may provide an accurate and generally applicable biomarker capable of differentiating dementia with Lewy bodies (DLB) from Alzheimer’s disease (AD) and Parkinson’s disease-associated dementia (PDD). Method. We carried out a retrospective visual analysis of resting-state electroencephalography (EEG) recording of 22 patients with a clinical diagnosis of 19 probable and 3 possible DLB, 22 patients with probable AD and 21 with PDD, matched for age, duration, and severity of cognitive impairment. Results. By using the grand total EEG scoring method, the total score and generalized rhythmic delta activity frontally predominant (GRDAfp) alone or, even better, coupled with a slowing of frequency of background activity (FBA) and its reduced reactivity differentiated DLB from AD at an individual level with an high accuracy similar to that obtained with quantitative EEG (qEEG). GRDAfp alone could also differentiate DLB from PDD with a similar level of diagnostic accuracy. AD differed from PDD only for a slowing of FBA. The duration and severity of cognitive impairment did not differ between DLB patients with and without GRDAfp, indicating that this abnormal EEG pattern should not be regarded as a disease progression marker. Conclusions. The findings of this investigation revalorize the role of conventional EEG in the diagnostic workup of degenerative dementias suggesting the potential inclusion of GRDAfp alone or better coupled with the slowing of FBA and its reduced reactivity, in the list of supportive diagnostic biomarkers of DLB.

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