Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer’s disease

Huang

Fan

et al. 2020

Preprint

ObjectivePSD is one of the most common neuropsychiatric symptoms with high prevalence, however, the mechanism of brain network in PSD and the relationship between the structural network and functional network remain unclear. This study incorporated multiple structural and functional connectivity metrics into a graph analysis framework in patients with PSD and PSND. MethodForty-five patients with acute ischemic stroke were divided into PSD group and non-PSD group respectively and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including FA and MD. Moreover, we investigated the relationship between functional and structural connectivity networks. The whole brain network was characterized with graph theoretical metrics and decomposed into modules. Subsequently, the graph metrics and the connectivity within modules were related to depression. ResultsStructural network disturbances in PSD were related to decreased clustering and increased path length compared to PSND, while being less modular.ConclusionSC-FC coupling may serve as a biomarker for post-stroke depression. Some regional characteristics were altered in structural connectivity network, involving attention, sensorimotor, subcortical and default-mode networks. These regions with altered nodal characteristics likely reflect the pathophysiology of PSD. The similarity of structural-functional are associated with cognitive dysfunction, retardation and desperation. Our findings highlighted the distinction in brain structural-functional networks in PSD.

Section: Introductionsupporting

confidence: 52%

Analysis of correlation between white matter changes and functional responses in post-stroke depression

Huang

Fan

et al. 2020

Preprint

“…Participants. We studied 124 AD, 103 aMCI, and 49 HC from an ongoing project, recruited from memory clinics in the National University Hospital, Saint Luke's Hospital and nearby communities 52,53 . Diagnoses were made by psychologists, neurologists, and research personnel at weekly consensus meetings based on clinical observation, lab tests (e.g., blood test), neuroimaging scans and neuropsychological assessments.…”

Section: Methodsmentioning

confidence: 99%

“…Data pre-processing. fMRI data were pre-processed with a standard pipeline using the FMRIB Software Library (FSL) 56 and Analysis of Functional NeuroImages software (AFNI) 57 as described previously 36,53 . Briefly, pre-processing of the structural images included reducing nonlinear image noise (SUSAN), extracting brain tissue (skull stripping, BET), normalizing to the Montreal Neurological Institute (MNI) 152 standard space (FLIRT/FNIRT) and segmenting into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF).…”

Section: Clinical and Neuropsychological Assessments The Montreal Comentioning

confidence: 99%

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

Zuo

et al. 2020

Sci Rep

Self Cite

However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.Alzheimer's disease (AD) is the major cause of dementia, and increasing attention has been focused on early disease detection/prevention. Therefore, studying brain changes along the AD disease continuum is important, i.e., from normal aging to the prodromal stage (amnestic mild cognitive impairment, aMCI) and finally to dementia stage. Using resting-state functional connectivity methods 1-5 that quantifies the temporal synchrony between brain regions, both AD and aMCI have been found to target large-scale networks, including reduced After ICA-based denoising, there was a main effect of group mainly in the posterior DMN including the precuneus/posterior cingulate cortex and VN (cuneus), and the SN (insula) ( Supplementary Table 1). Group comparisons replicated the GSR findings that aMCI had higher variability in the posterior DMN compared with AD (precuneus; Fig. 1B, bottom panel) and HC (angular gyrus ; Fig. 1A, bottom panel), as well as lower SN variability compared with HC (insula ; Fig. 1C, bottom panel), although the latter did not survive the cluster-level Scientific RepoRtS | (2020) 10:6457 | https://doi.Furthermore, we examined whether the variability related to global cognition decline over time, defined as the difference between baseline and year 2 (year 2 minus baseline).For all correlation analyses, we focused on the brain clusters showing group differences between aMCI and HC or between AD and HC (including clusters surviving the voxel-level threshold consistently using both GSR and ICA-based denoising) (n = 3 for slow4, and n = 6 for slow5 for both data denoising methods). Correlati...

“…This study recruited participants from two independent datasets: (a) 75 NCI and 104 MCI who were new patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI2) study as defined on July 10, 2018 and (b) 20 NCI and 93 MCI from Memory Aging and Cognition Centre (MACC) dataset recruited from memory clinics/community in Singapore (Chong et al, ). ADNI data were obtained from the ADNI database (http://adni.loni.usc.edu/).…”

Section: Methodsmentioning

confidence: 99%

Longitudinal trajectory of Amyloid‐related hippocampal subfield atrophy in nondemented elderly

Mak

Reilhac

et al. 2020

Human Brain Mapping

Self Cite

Hippocampal atrophy and abnormal β-Amyloid (Aβ) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aβ-associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aβ correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET-Aβ in AD-vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto-segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aβ-related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aβ correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal-to-widespread trajectory of Aβ-associated hippocampal subfield atrophy over disease progression in nondemented elderly.Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: