Predicting Fazekas scores from automatic segmentations of white matter signal abnormalities

Diaz-Galvan

Díaz‐Flores

et al. 2020

Preprint

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AIMS: To investigate the interplay between gray matter (GM) and white matter (WM) neurodegeneration in subjective cognitive decline (SCD), including thickness across the whole cortical mantle, hippocampal volume, and integrity across the whole WM. METHODS: We included 225 cognitively unimpaired individuals from a community-based cohort, of whom 123 endorsed one or more subjective cognitive complaints. GM neurodegeneration was assessed through measures of cortical thickness across the whole mantle and hippocampal volume. WM neurodegeneration was assessed through measures of mean diffusivity (MD) across the whole WM skeleton. Mediation analysis and multiple linear regression were conducted to investigate the interplay between the measures of GM and WM neurodegeneration.RESULTS: A higher number of complaints was associated with reduced hippocampal volume, cortical thinning in several frontal and temporal areas and the insula, and higher MD across the WM skeleton, with a tendency to spare the occipital lobe. SCD-related cortical thinning and increased MD were associated with each other and jointly contributed to the complaints, but the contribution of cortical thinning to SCD was stronger.CONCLUSIONS: Neurodegeneration processes affecting the GM and WM seem to be associated with each other in SCD and include brain areas other than those typically targeted by Alzheimer’s disease (AD). Our findings suggest that SCD may be a sensitive behavioral marker of heterogeneous brain pathologies in individuals recruited from the community.

Section: Limitationsmentioning

confidence: 99%

The Interplay Between Gray Matter and White Matter Neurodegeneration in Subjective Cognitive Decline

Diaz-Galvan

Díaz‐Flores

et al. 2020

Preprint

Self Cite

“…Hence, in the KIDS cohort, WMSA were assessed through the Fazekas scale on FLAIR images. Importantly, hypointense WMSA from FreeSurfer and the Fazekas scale are strongly associated with each other [27]. We followed a previous study that demonstrated that hypointense WMSA from FreeSurfer and Fazekas scores can be combined together by converting hypointense WMSA into low and high CVD burden [27].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 98%

“…Importantly, hypointense WMSA from FreeSurfer and the Fazekas scale are strongly associated with each other [27]. We followed a previous study that demonstrated that hypointense WMSA from FreeSurfer and Fazekas scores can be combined together by converting hypointense WMSA into low and high CVD burden [27]. Hence, we computed a unique WMSA variable by applying a cut-off of 0.00321 on hypointense WMSA from FreeSurfer after total intracranial volume (TIV) adjustment.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

“…Hence, we computed a unique WMSA variable by applying a cut-off of 0.00321 on hypointense WMSA from FreeSurfer after total intracranial volume (TIV) adjustment. This creates two categories of low and high CVD burden that are analogous to Fazekas scores of 0 or 1 de ned as low WMSA and Fazekas scores of 2 or 3 de ned as high WMSA [27].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

“…In the ADNI cohort we used an automatic segmentation based on white matter hypointensities while in the KIDS cohort we used visual ratings based on white matter hyperintensities. Although using different methods for WMSA could induce some noise in our analysis, we recently showed that both methods are strongly associated with each other [27]. Further, by classifying the output from both methods into high and low WMSA burden, we used a rougher measure that is less in uenced by differences between the two methods and has greater clinical applicability [42].…”

Section: Limitationsmentioning

confidence: 99%

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Atrophy subtypes and the ATN classification scheme in Alzheimer’s disease

Ekman

Poulakis

et al. 2020

Preprint

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BACKGROUND We investigated the association between atrophy subtypes of Alzheimer’s disease (AD), the ATN classification scheme, and key demographic and clinical factors, in two cohorts with different source characteristics (a highly selective research-oriented cohort, ADNI; and a naturalistic heterogeneous clinically-oriented cohort, Karolinska Imaging Dementia Study (KIDS). METHODS A total of 382 AD patients were included. Factorial analysis of mixed data was used to investigate associations between AD subtype based on brain atrophy patterns, ATN profiles based on cerebrospinal fluid biomarkers, and age, sex, Mini Mental State Examination (MMSE), cerebrovascular disease (CVD) (burden of white matter signal abnormalities, WMSA), and APOE genotype. RESULTS Older patients with high WMSA burden, belonging to the typical AD subtype, and showing A + T + N + or A + T + N- profiles clustered together and were mainly from ADNI. Younger patients with low WMSA burden, limbic-predominant or minimal atrophy AD subtypes, and A + T-N- or A + T-N + profiles, clustered together and were mainly from KIDS. APOE ε4 carriers more frequently showed the A + T-N- and A + T + N- profiles. CONCLUSIONS Our findings align with the recent framework for biological subtypes of AD: the combination of risk factors, protective factors, and brain pathologies determines belonging of AD patients to distinct subtypes.

Cerebrovascular disease and depressive symptomatology in individuals with subjective cognitive decline: A community‐based study

Diaz-Galvan

Figueroa

et al. 2021

Alzheimer's & Dementia

Background Subjective cognitive decline (SCD) may be the first sign of Alzheimer’s disease (AD), but it can also reflect other pathologies such as cerebrovascular disease or conditions like depressive symptomatology. The role of depressive symptomatology in SCD is controversial. In this study, we investigated the association between depressive symptomatology, cerebrovascular disease, and SCD. We hypothesized three different scenarios, where depressive symptomatology would co‐exist, contribute to, or be a consequence of SCD (Figure). Method We recruited 225 cognitively unimpaired individuals from a prospective community‐based study (mean age (SD) = 54.64 (10.18); age range 35 – 77 years; 55% women; 102 individuals without any subjective cognitive complaint, 123 individuals with one or more complaints). SCD was assessed with a scale of 9 memory and non‐memory subjective complaints. Depressive symptomatology was assessed with established questionnaires. We used magnetic resonance imaging markers of white matter signal abnormalities (WMSA) and mean diffusivity (MD) to assess cerebrovascular disease. We combined correlation, multiple regression, and mediation analyses to investigate the association between depressive symptomatology, cerebrovascular disease, and SCD. Results We found that SCD was associated with higher cerebrovascular disease (WMSA: r=0.210, p<0.002; MD: r=0.216, p=0.001), older age (r=0.373, p<0.001), and increased depressive symptomatology (r=0.340, p<0.001). In turn, depressive symptomatology was not associated with cerebrovascular disease (WMSA: r=0.003, p<0.961; MD: r=0.076, p<0.321) or age (r=0.069, p<0.030). We also studied whether abnormalities in white matter MD in SCD may be due to cerebrovascular disease. Results showed that variability in MD was mediated by WMSA burden (p=0.026), presumably reflecting cerebrovascular disease. Conclusion In our community‐based cohort, depressive symptomatology co‐exists with SCD and reflects emotional factors, but does not reflect cerebrovascular disease. In addition, depressive symptomatology did not influence the capacity of SCD to predict underlying cerebrovascular disease. We suggest that therapeutic interventions for depressive symptomatology could alleviate the psychological burden of negative emotions in people with SCD, and intervening on vascular risk factors to reduce cerebrovascular disease should be tested as an opportunity to minimize neurodegeneration in SCD.