scholarly journals Altered multimodal magnetic resonance parameters of basal nucleus of Meynert in Alzheimer’s disease

2020 ◽  
Vol 7 (10) ◽  
pp. 1919-1929
Author(s):  
Weimin Zheng ◽  
Hui Li ◽  
Bin Cui ◽  
Peipeng Liang ◽  
Ye Wu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Basal Nucleus ◽  
Resonance Parameters ◽  
Magnetic Resonance Parameters ◽  
Basal Nucleus Of Meynert

Clinical and neuropathological correlates of depression in Alzheimer's disease

1992 ◽  
Vol 22 (4) ◽  
pp. 877-884 ◽  
Author(s):  
Hans Förstl ◽  
Alistair Burns ◽  
Philip Luthert ◽  
Nigel Cairns ◽  
Peter Lantos ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Age Of Onset ◽  
Neuronal Degeneration ◽  
Cholinergic Neurons ◽  
Organic Substrate ◽  
Basal Nucleus ◽  
Alzheimer Pathology ◽  
Basal Nucleus Of Meynert

SynopsisDepressive symptoms have been reported in patients with mild to moderate Alzheimer's disease (AD). Recent evidence suggests that a noradrenergic deficit originating from neuronal degeneration in brainstem nuclei may represent an organic correlate of these disturbances. We examined the neuropathological changes in the locus coeruleus (LC), substantia nigra (SN), basal nucleus of Meynert and cortex of 52 patients (12 male, 40 female, mean age 83·2 ± 6·4 years) with pathologically verified AD. Fourteen patients (1 male, 13 female) showed signs of depression. The majority of these patients suffered from severe physical disability or sensory impairment and developed persistent delusions, but had less cognitive impairment. Neuronal counts in the LC were significantly lower than in the 38 patients without depression (36·9 ± 14 ·0; 51·4 ± 28·0 neuromelaninpigmented cells per section per nucleus;F= 3·4, df = 1, 50,P= 0·04). Neuron counts were higher in the basal nucleus of Meynert in depressed AD patients and there were no differences of the neuron numbers in the SN. Depression (main effect;F= 4·5,P= 0·04) contributed significantly to the variance of neuronal counts in the LC, even when covarying for gender, age of onset, cognitive impairment and cortical Alzheimer pathology. The observed disproportionate loss of noradrenergic and cholinergic neurons in the LC and basal nucleus of Meynert may represent an important organic substrate of depression in AD.

Evolution of Alzheimer's disease-related cytoskeletal changes in the basal nucleus of Meynert

2000 ◽  
Vol 100 (3) ◽  
pp. 259-269 ◽  
Author(s):  
I. Sassin ◽  
C. Schultz ◽  
D. R. Thal ◽  
U. Rüb ◽  
K. Arai ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Basal Nucleus ◽  
Basal Nucleus Of Meynert

Correlative decrease of large neurons in the neostriatum and basal nucleus of Meynert in Alzheimer's disease

1989 ◽  
Vol 504 (2) ◽  
pp. 354-357 ◽  
Author(s):  
Kiyomitsu Oyanagi ◽  
Hitoshi Takahashi ◽  
Koichi Wakabayashi ◽  
Fusahiro Ikuta
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Basal Nucleus ◽  
Basal Nucleus Of Meynert ◽  
Large Neurons

Prevalence of Sinusitis Detected by Magnetic Resonance Imaging in Subjects with Dementia or Alzheimer’s Disease

2015 ◽  
Vol 12 (10) ◽  
pp. 1006-1011 ◽  
Author(s):  
Minori Yasue ◽  
Saiko Sugiura ◽  
Yasue Uchida ◽  
Hironao Otake ◽  
Masaaki Teranishi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Resonance Imaging

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

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.

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