Magnetic Resonance Volumetry: Prediction of Subjective Memory Complaints and Mild Cognitive Impairment, and Associations with Genetic and Cardiovascular Risk Factors

Abstract

Background/Aims: Subjective memory complaints (SMC) are strong predictors of mild cognitive impairment (MCI) and subsequent Alzheimer’s disease. Our aims were to see if fully automated cerebral MR volume measurements could distinguish subjects with SMC and MCI from controls, and if probable parental late-onset Alzheimer’s disease (LOAD), apolipoprotein E ε4 genotype, total plasma homocysteine, and cardiovascular risk factors were associated with MR volumetric findings. Methods: 198 stroke-free subjects comprised the control (n = 58), the SMC (n = 25) and the MCI (n = 115) groups. Analysis of covariance and receiver operating characteristic curve was used to see if MR volumetry distinguished subjects with SMC and MCI from controls. Results: Subjects with SMC and MCI had significantly larger lateral ventricles and smaller hippocampal volumes than controls. The area under the curve in subjects with SMC and MCI compared to that of controls was less than 0.68 for all volumes of intracranial structures. There was an interaction between sex and probable parental LOAD for hippocampal volume, with a significant association between probable parental LOAD and hippocampal volume in women. Conclusions: Fully automated MR volumetry can distinguish subjects with SMC and MCI from controls in a general population, but insufficiently to assume a clear clinical role. Research on sporadic LOAD might benefit from a sex-specific search for genetic risk factors.

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

“… Lee et al (2016) [ 117 ] Memory clinic consultation T1 MRI DTI Cross-sectional ApoE ɛ4+: n = 13 (66.4 ± 6.3) ApoE ɛ4-: n = 13 (66.2 ± 7.8) ApoE ɛ4+ SCD showed gray matter atrophy and lower FA compared with ApoE ɛ4- SCD. Rogne et al (2016) [ 69 ] 1 binary question T1 MRI Cross-sectional NC: n = 58 (70.6 ± 6.7) SCD: n = 25 (70.0 ± 9.1) MCI: n = 115 (74.5 ± 7.5) SCD had larger lateral ventricles and smaller hippocampal volumes than NC. Sun et al (2016) [ 122 ] Memory clinic consultation T1 MRI rs-fMRI Cross-sectional NC: n = 61 (64.1 ± 8.6) SCD: n = 25 (65.5 ± 6.1) SCD showed higher ALFF but no differences in gray matter volume Verfaillie et al (2016) [ 93 ] Memory clinic consultation T1 MRI Cross-sectional SCD stable: n = 253 (61 ± 9) SCD progression: n = 49 (69 ± 6) Hippocampal volumes, thinner cortex of the AD-signature and various AD-signature subcomponents were associated with increased risk of clinical progression Lauriola et al (2017) [ 123 ] Subjective cognitive decline Questionnaire T1 MRI Cross-sectional NC: n = 38 (64.0 ± 5.1) SCD: n = 32 (64.8 ± 6.3) SCD showed increased nighttime wakefulness and reduced sleep efficiency.…”

Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer’s Disease From Normal Aging

“…These results outperformed the existing studies that used predefined region-based methods to classify aMCI versus NC (ACC < 90%, AUC < 0.93) 40,41 and the studies that achieved the best performance in aMCI versus NC classification by using the density map-based approach (ACC = 90%) and cortical surface-based approach (AUC = 0.95). 11,40 More importantly, we also achieved much better performance than a recent study that also used a predefined region-based approach to distinguish SCD from NC (ACC < 68%), 7 which was observed either in our model with the volume ratios of multiple brain regions as the predictors or in the several other models that use the volume ratio of a single region as the predictor (Figure 4). In fact, for the first time, we demonstrated that automated MRI volumetry could differentiate SCD from NC with a very similar performance to that of differentiating aMCI from NC.…”

“…8 To further investigate the degree of brain volume loss among different early stages of AD, many studies applied machine learning approaches to differentiate patients with SCD or MCI from normal control (NC) participants. In these studies, differentiating aMCI from NCs [11][12][13] (classification accuracy >90%) was much easier than differentiating SCD from NCs 7 (classification accuracy <68%) using brain atrophy measures.…”

“…With regard to structural magnetic resonance imaging (MRI) research, researchers have reported an association between brain structural deficits (eg, in the gray matter and hippocampal regions) and the onset of SCD. 7,9,10 The brain regions with atrophy in patients with SCD largely follow the AD-like atrophy pattern (notably in the medial temporal lobe), which are also present in patients with aMCI. 8 To further investigate the degree of brain volume loss among different early stages of AD, many studies applied machine learning approaches to differentiate patients with SCD or MCI from normal control (NC) participants.…”

“…1 Subjective cognitive decline (SCD), that is, self-perceived cognitive decline while maintaining normal performance in objective cognitive assessment, 2 has been suggested as an even earlier symptomatic disease stage of AD than aMCI. 3 Therefore, there has been increasing research interest in characterizing the functional 4,5 and structural [6][7][8] changes in the brains of patients with SCD to determine a better population definition for future dementia prevention trials.…”