32 Therefore, functional meta-analytic connectivity models (fMACM) 33 should be further constructed based on locally convergent brain regions. 26 Brain network abnormalities detected in patients with DLB are predominantly described in the default mode network (DMN), 27 frontal-parietal network (FPN), 28, 29 basal ganglia network, 30, 31 and visual network (VIS). 24, 25 Local brain regions that are selectively damaged act as “nodes” in functional networks, representing the basis of the network degradation hypothesis. Growing evidence suggests that neurodegenerative diseases are caused by brain network dysfunction rather than the dysregulation of an isolated brain region. 21 Additionally, a reduced metabolic activity in the frontal and occipital lobes is observed in both DLB and AD, although more reduced in the former 22, 23 Therefore, it is necessary to focus on these different findings to better understand the relatively uniform damage of brain regions. 20 Another article revealed that the temporal cerebral blood flow in DLB patients remained unchanged. 13, 14 A functional imaging report showed a hypoperfusion in the frontal, insular, and temporal cortexes of DLB patients, as well as the hypoperfusion in the parietal and temporoparietal cortexes of AD patients. 11, 19 This aspect means that they are more likely to develop subcortical atrophy than AD patients. 15- 17 DLB patients with a similar level of dementia have relatively better preservation of the hippocampus, temporal lobe, 12, 14, 18 and amygdala. However, other studies found a relatively concentrated pattern of atrophy in the subcortical brain, including midbrain, hypothalamus/thalamus, basal ganglia, 13, 14 and substantia innominate. 9, 10 Some reports showed the cortical atrophy of the frontal lobe, 11 temporal lobe, 11, 12 parietal lobe, and occipital lobe 12 in DLB. ![]() ![]() Structural imaging can reflect changes in brain volume at voxelwise level. Therefore, stable and consistent indicators that provide a theoretical basis for the diagnosis and differential diagnosis of DLB are still lacking. 8 However, broader structural and functional studies provided conflicting results. 7 A neuropathologically confirmed study showed that DAT imaging can distinguish between DLB and AD more accurately than the consensus clinical criteria. For example, the role of DAT imaging in distinguishing DLB from AD is well established, with a sensitivity of 78% and specificity of 90%. Multimodal neuroimaging is widely used in clinical practice. The widely spread pathologies related to Lewy bodies and coexisting AD-type pathologies 4- 6 make the clinical manifestations complex and highly variable, increasing the difficulty of the differential diagnosis between DLB and AD, especially in the early stages. 2, 3 Although DLB is the second most common neurodegenerative disorder after AD, the sensitivity of its diagnosis in clinical practice is suboptimal. This might provide a pathway for the neural regulation in DLB patients, and it might contribute to the development of specific interventions for DLB and AD.ĭementia with Lewy bodies (DLB) is characterized by fluctuating cognition, recurrent visual hallucinations, rapid eye movement sleep behavior disorder, and spontaneous parkinsonism, 1 accounting for 15%–20% of the total dementia cases at autopsy. The convergence of local brain regions and co-activation neural networks might be potential specific imaging markers in the diagnosis of DLB. The frontal-parietal, salience, and visual networks were all abnormally co-activated in DLB, but the default mode network remained normally co-activated compared with AD. DLB patients showed a relative preservation of the medial temporal lobe and a tendency of lower metabolism in the right lingual gyrus compared with AD. DLB patients showed a dysfunction in the bilateral inferior parietal lobule and right lingual gyrus compared with HC patients. ![]() ResultsĮleven structural studies and fourteen functional studies were included in this quantitative meta-analysis. The coordinate-based meta-analysis and functional meta-analytic connectivity modeling were performed using the activation likelihood estimation algorithm. ![]() PubMed, Web of Science, OVID, Science Direct, and Cochrane Library databases were used to identify neuroimaging studies that included DLB versus healthy controls (HCs) or DLB versus AD. The neural networks involved in the identified abnormal brain regions were further described. The aim of this study was to identify brain regions with local, structural, and functional abnormalities in dementia with Lewy bodies (DLB) and uncover the differences between DLB and Alzheimer's disease (AD).
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