Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network

Xu, Hongbo; Liu, Yan; Zeng, Xiangzhu; Xu, Ying-Ying; Wang, Zeng

doi:10.1016/j.nicl.2023.103370

Cited by 7 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A number of study activities were utilised including diagnosis, (PD vs Healthy Controls (HC), PDD vs HC, PD-NC vs PD-MCI, PD-MCI vs PDD), differential diagnosis (PD-CI/PD-MCI/PDD vs AD vs DLB), identification of biomarkers for PD detection, and the prediction of future CI states. Most studies focused on diagnostic activities (n = 48) [ 21 – 26 , 28 , 30 , 31 , 84 , 113 , 114 , 116 – 119 , 121 , 122 , 124 – 126 , 128 , 130 , 133 – 136 , 138 , 142 , 144 – 146 , 148 – 151 , 153 – 155 , 157 – 161 , 164 , 166 , 171 , 172 ], followed by prediction (n = 12) [ 29 , 129 , 131 , 132 , 137 , 140 , 141 , 152 , 156 , 162 , 163 , 170 ], biomarker identification (n = 6) [ 27 , 120 , 123 , 143 , 147 , 169 ], and differential diagnosis (n = 4) [...…”

Section: Observations and Findingsmentioning

confidence: 99%

“…The most commonly used data modalities were imaging (n = 33) [ 24 , 27 – 31 , 113 , 114 , 117 , 119 , 122 , 129 , 130 , 133 , 137 , 139 – 143 , 147 – 152 , 155 , 156 , 159 – 161 , 163 , 164 ], clinical characteristics (n = 17) [ 84 , 118 , 131 , 132 , 137 , 140 , 152 , 153 , 155 , 156 , 159 , 160 , 162 – 164 , 169 , 170 ], EEG (n = 11) [ 25 , 26 , 120 , 125 – 128 , 135 , 136 , 146 , 151 ], and neuropsychological profile (n = 10) [ 115 , 118 , 132 , 134 , 142 , 157 , 158 …”

Section: Observations and Findingsmentioning

confidence: 99%

“…On a similar theme, analysis of DL models can be used to determine the categories within data that are the most influential for a decision, and can lend themselves well to identify the most crucial criteria in data. Xu et al [ 147 ] used such techniques with a CNN to analyse Regions of Interest in Hippocampal Mapping Images (HMIs) of T1 MRI gathered from 245 PD patients (195 PD, 25 PD-NC, 25 PD-MCI) alongside HC, MCI and AD patients. Subfields in these HMIs were used to classify these patient groups.…”

Section: Observations and Findingsmentioning

confidence: 99%

See 2 more Smart Citations

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

View full text Add to dashboard Cite

Background Parkinson’s Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. Methods To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson’s Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. Results Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson’s Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson’s Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. Conclusions Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.

show abstract

Section: Observations and Findingsmentioning

confidence: 99%

Section: Observations and Findingsmentioning

confidence: 99%

Section: Observations and Findingsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Altham,

Zhang,

Pereira

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Early drug-naive PD-MCI showed atrophy of the right entorhinal cortex [ 220 ]. Morphometric studies in early PD-MCI patients showed reduced limbic subcortical structures [ 221 ]; the hippocampal subfields subiculum, presubiculum, CA1 and fimbria were strongly correlated with CI, subfields GC-DG and fimbria being sensitive in detecting early stage of CI, while the left parasubiculum and presubiculum predicted conversion from PD-NC to PD-MCI [ 171 ]. A smaller CA1 region and reduced hippocampal–amygdaloid transition area volume were observed [ 169 ].…”

Section: Neuroimaging Findings In CI In Pd (See Supplement ...mentioning

confidence: 99%

“…The following supporting information can be downloaded at . References [ 42 , 55 , 80 , 140 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 2...…”

unclassified

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Jellinger

2023

IJMS

View full text Add to dashboard Cite

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

show abstract

11.7T Diffusion Magnetic Resonance Imaging and Tractography to Probe Human Brain Organoid Microstructure

Versace,

Hitchens,

Wallace

et al. 2024

Biological Psychiatry Global Open Science

View full text Add to dashboard Cite

Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network

Cited by 7 publications

References 54 publications

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

11.7T Diffusion Magnetic Resonance Imaging and Tractography to Probe Human Brain Organoid Microstructure

Contact Info

Product

Resources

About