scholarly journals Specific pattern of melanin-concentrating hormone (MCH) neuron degeneration in Alzheimer’s disease and possible clinical implications

2021 ◽  
Author(s):  
Mihovil Mladinov ◽  
Jun Yeop Oh ◽  
Cathrine Petersen ◽  
Rana Eser ◽  
Song Hua Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Progressive Supranuclear Palsy ◽  
Functional Relation ◽  
Lateral Hypothalamic Area ◽  
Future Research ◽  
Hypothalamic Area ◽  
Lateral Hypothalamic ◽  
New Findings ◽  
Melanin Concentrating Hormone

ABSTRACTStudy ObjectivesThe lateral hypothalamic area (LHA) is one of the key regions orchestrating sleep and wake control. It is the site of wake-promoting orexinergic and sleep-promoting melanin-concentrating hormone (MCH) neurons, which share a close anatomical and functional relation. The aim of the study was to investigate the degeneration of MCH neurons in Alzheimer’s disease (AD) and progressive supranuclear palsy (PSP), and relate the new findings to our previously reported pattern of degeneration of wake-promoting orexinergic neuronsMethodsPost-mortem human brain tissue of subjects with AD, PSP and controls was examined using unbiased stereology. Double immunohistochemistry with MCH- and tau-antibodies on formalin-fixed, celloidin embedded tissue was performed.ResultsThere was no difference in the total number of MCH neurons between AD, PSP and controls, but a significant loss of non-MCH neurons in AD patients (p=0.019). The proportion of MCH neurons was significantly higher in AD (p=0.0047). No such a difference was found in PSP. In PSP, but not AD, the proportion of tau+ MCH neurons was lower than the proportion of tau+ non-MCH neurons (p=0.002). When comparing AD to PSP, the proportion of tau+MCH neurons was higher in AD (p<0.001).ConclusionsMCH neurons are more vulnerable to AD than PSP pathology. High burden of tau-inclusions, but comparably milder loss of MCH neurons in AD, together with previously reported orexinergic neuronal loss may lead to a hyperexcitability of the MCH system in AD, contributing to wake-sleep disorders in AD. Further experimental research is needed to understand why MCH neurons are more resistant to tau-toxicity compared to orexinergic neurons.STATEMENT OF SIGNIFICANCEThis is the first study to investigate the involvement of melanin-concentrating hormone (MCH) neurons in patients with Alzheimer’s disease and progressive supranuclear palsy. MCH neurons are key regulators of sleep and metabolic functions, and one of the major neuronal populations of the lateral hypothalamic area (LHA), but still underexplored in humans. Uncovering the pathology of this neuronal population in neurodegenerative disorders will improve our understanding of the complex neurobiology of the LHA and the interaction between MCH and orexinergic neurons. This new knowledge may open new strategies for treatment interventions. Further, this study represents a fundament for future research on MCH neurons and the LHA in tauopathies.

scholarly journals Lateral Hypothalamic Area Glutamatergic Neurons and Their Projections to the Lateral Habenula Modulate the Anesthetic Potency of Isoflurane in Mice

2021 ◽  
Author(s):  
Shiyi Zhao ◽  
Rui Li ◽  
Huiming Li ◽  
Sa Wang ◽  
Xinxin Zhang ◽  
...  
Keyword(s):  
Large Population ◽  
Lateral Hypothalamic Area ◽  
Hypothalamic Area ◽  
Lateral Habenula ◽  
Genetic Ablation ◽  
Glutamatergic Neurons ◽  
Lateral Hypothalamic ◽  
Melanin Concentrating Hormone ◽  
Anesthetic Potency ◽  
Eeg Features

AbstractThe lateral hypothalamic area (LHA) plays a pivotal role in regulating consciousness transition, in which orexinergic neurons, GABAergic neurons, and melanin-concentrating hormone neurons are involved. Glutamatergic neurons have a large population in the LHA, but their anesthesia-related effect has not been explored. Here, we found that genetic ablation of LHA glutamatergic neurons shortened the induction time and prolonged the recovery time of isoflurane anesthesia in mice. In contrast, chemogenetic activation of LHA glutamatergic neurons increased the time to anesthesia and decreased the time to recovery. Optogenetic activation of LHA glutamatergic neurons during the maintenance of anesthesia reduced the burst suppression pattern of the electroencephalogram (EEG) and shifted EEG features to an arousal pattern. Photostimulation of LHA glutamatergic projections to the lateral habenula (LHb) also facilitated the emergence from anesthesia and the transition of anesthesia depth to a lighter level. Collectively, LHA glutamatergic neurons and their projections to the LHb regulate anesthetic potency and EEG features.

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.

scholarly journals Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

2021 ◽  
Vol 22 (15) ◽  
pp. 7911
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Future Research ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Similarities And Differences ◽  
Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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