The effects of apolipoprotein ε 4 on aging brain in cognitively normal Chinese elderly: a surface-based morphometry study

Decoding MRI-informed brain age using mutual information

OBJECTIVE

We aimed to develop a standardized method to investigate the relationship between estimated brain age and regional morphometric features, meeting the criteria for simplicity, generalization, and intuitive interpretability.

METHODS

We utilized T1-weighted magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience project (N = 609) and employed a support vector regression method to train a brain age model. The pre-trained brain age model was applied to the dataset of the brain development project (N = 547). Kraskov (KSG) estimator was used to compute the mutual information (MI) value between brain age and regional morphometric features, including gray matter volume (GMV), white matter volume (WMV), cerebrospinal fluid (CSF) volume, and cortical thickness (CT).

RESULTS

Among four types of brain features, GMV had the highest MI value (8.71), peaking in the pre-central gyrus (0.69). CSF volume was ranked second (7.76), with the highest MI value in the cingulate (0.87). CT was ranked third (6.22), with the highest MI value in superior temporal gyrus (0.53). WMV had the lowest MI value (4.59), with the insula showing the highest MI value (0.53). For brain parenchyma, the volume of the superior frontal gyrus exhibited the highest MI value (0.80).

CONCLUSION

This is the first demonstration that MI value between estimated brain age and morphometric features may serve as a benchmark for assessing the regional contributions to estimated brain age. Our findings highlighted that both GMV and CSF are the key features that determined the estimated brain age, which may add value to existing computational models of brain age.

CRITICAL RELEVANCE STATEMENT

Mutual information (MI) analysis reveals gray matter volume (GMV) and cerebrospinal fluid (CSF) volume as pivotal in computing individuals' brain age.

KEY POINTS

Mutual information (MI) interprets estimated brain age with morphometric features. Gray matter volume in the pre-central gyrus has the highest MI value for estimated brain age. Cerebrospinal fluid volume in the cingulate has the highest MI value. Regarding brain parenchymal volume, the superior frontal gyrus has the highest MI value. The value of mutual information underscores the key brain regions related to brain age.

MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters

BACKGROUND

Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases.

PURPOSE

This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion.

METHODS

Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm.

RESULTS

With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline.

CONCLUSIONS

This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.

Ethnic differences in the effects of apolipoprotein E ɛ4 and vascular risk factors on accelerated brain aging

The frequency of the apolipoprotein E ɛ4 allele and vascular risk factors differs among ethnic groups. We aimed to assess the combined effects of apolipoprotein E ɛ4 and vascular risk factors on brain age in Korean and UK cognitively unimpaired populations. We also aimed to determine the differences in the combined effects between the two populations. We enrolled 2314 cognitively unimpaired individuals aged ≥45 years from Korea and 6942 cognitively unimpaired individuals from the UK, who were matched using propensity scores. Brain age was defined using the brain age index. The apolipoprotein E genotype (ɛ4 carriers, ɛ2 carriers and ɛ3/ɛ3 homozygotes) and vascular risk factors (age, hypertension and diabetes) were considered predictors. Apolipoprotein E ɛ4 carriers in the Korean (β = 0.511, P = 0.012) and UK (β = 0.302, P = 0.006) groups had higher brain age index values. The adverse effects of the apolipoprotein E genotype on brain age index values increased with age in the Korean group alone (ɛ2 carriers × age, β = 0.085, P = 0.009; ɛ4 carriers × age, β = 0.100, P < 0.001). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ2 carriers × age × ethnicity, β = 0.091, P = 0.022; ɛ4 carriers × age × ethnicity, β = 0.093, P = 0.003). The effects of apolipoprotein E on the brain age index values were more pronounced in individuals with hypertension in the Korean group alone (ɛ4 carriers × hypertension, β = 0.777, P = 0.038). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ4 carriers × hypertension × ethnicity, β=1.091, P = 0.014). We highlight the ethnic differences in the combined effects of the apolipoprotein E ɛ4 genotype and vascular risk factors on accelerated brain age. These findings emphasize the need for ethnicity-specific strategies to mitigate apolipoprotein E ɛ4-related brain aging in cognitively unimpaired individuals.

Using gamma-band transcranial alternating current stimulation (tACS) to improve sleep quality and cognition in patients with mild neurocognitive disorders due to Alzheimer's disease: A study protocol for a randomized controlled trial

BACKGROUND

Sleep disturbances are highly prevalent in patients with age-related neurodegenerative diseases, which severely affect cognition and even lead to accumulated β-amyloid. Encouraging results from recent studies on transcranial direct current stimulation (tDCS) showed moderate positive effects on sleep quality in preclinical Alzheimer's disease (AD). Compared to tDCS, transcranial alternating current stimulation (tACS) enables the entrainment of neuronal activity with optimized focality through injecting electric current with a specific frequency and has significant enhancement effects on slow wave activities.

METHODS AND DESIGN

This is a randomized, double-blind, sham-controlled clinical trial comparing 40 Hz tACS with tDCS in mild neurocognitive disorders due to AD with sleep disturbances. Magnetic resonance imaging (MRI) data is used to construct personalized realistic head model. Treatment outcomes, including sleep quality, cognitive performance and saliva Aβ levels will be conducted at baseline, 4th week, 8th week, 12th week and 24th week.

CONCLUSIONS

It is expected that the repeated gamma-band tACS will show significant improvements in sleep quality and cognitive functions compared to tDCS and sham tDCS. The findings will provide high-level evidence and guide further advanced studies in the field of neurodegenerative diseases and sleep medicine.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05544201.

Pinpointing the precise stimulation targets for brain rehabilitation in early-stage Parkinson's disease

BACKGROUND

Transcranial magnetic stimulation (TMS) is increasingly used as a promising non-pharmacological treatment for Parkinson's disease (PD). Scalp-to-cortex distance (SCD), as a key technical parameter of TMS, plays a critical role in determining the locations of treatment targets and corresponding dosage. Due to the discrepancies in TMS protocols, the optimal targets and head models have yet to be established in PD patients.

OBJECTIVE

To investigate the SCDs of the most popular used targets in left dorsolateral prefrontal cortex (DLPFC) and quantify its impact on the TMS-induced electric fields (E-fields) in early-stage PD patients.

METHODS

Structural magnetic resonance imaging scans from PD patients (n = 47) and normal controls (n = 36) were drawn from the NEUROCON and Tao Wu datasets. SCD of left DLPFC was measured by Euclidean Distance in TMS Navigation system. The intensity and focality of SCD-dependent E-fields were examined and quantified using Finite Element Method.

RESULTS

Early-stage PD patients showed an increased SCDs, higher variances in the SCDs and SCD-dependent E-fields across the seven targets of left DLPFC than normal controls. The stimulation targets located on gyral crown had more focal and homogeneous E-fields. The SCD of left DLPFC had a better performance in differentiating early-stage PD patients than global cognition and other brain measures.

CONCLUSION

SCD and SCD-dependent E-fields could determine the optimal TMS treatment targets and may also be used as a novel marker to differentiate early-stage PD patients. Our findings have important implications for developing optimal TMS protocols and personalized dosimetry in real-world clinical practice.

Diversity in verbal fluency performance and its associations with MRI-informed brain age matrices in normal ageing and neurocognitive disorders

INTRODUCTION

Category verbal fluency test (CVFT) has been widely used to assess and monitor the cognitive capacities in epidemiological studies and clinical trials. Pronounced discrepancy in CVFT performance has been found in individuals with different cognitive statuses. This study aimed to combine the psychometric and morphometric approaches to decode the complex verbal fluency performance in senior adults with normal ageing and neurocognitive disorders.

METHODS

This study adopted a two-stage cross-sectional design involving quantitative analyses of neuropsychological and neuroimaging data. In study I, capacity- and speed-based measures of CVFT were developed to evaluate the verbal fluency performance in normal ageing seniors (n = 261), those with mild cognitive impairment (n = 204), and those with dementia (n = 23) whose age range is from 65 to 85 years. In study II, structural magnetic resonance imaging-informed gray matter volume (GMV) and brain age matrices were calculated in a subsample (n = 52) from Study I through surface-based morphometry analysis. With age and gender as covariates, Pearson's correlation analysis was used to examine the associations of CVFT measures, GMV, and brain age matrices.

RESULTS

Speed-based measures showed extensive and stronger associations with other cognitive functions than capacity-based measures. The component-specific CVFT measures showed shared and unique neural underpinnings with lateralized morphometric features. Moreover, the increased CVFT capacity was significantly correlated with younger brain age in mild neurocognitive disorder (NCD) patients.

CONCLUSION

We found that the diversity of verbal fluency performance in normal ageing and NCD patients could be explained by a combination of memory, language, and executive abilities. The component-specific measures and related lateralized morphometric correlates also highlight the underlying theoretical meaning of verbal fluency performance and its clinical utility in detecting and tracing the cognitive trajectory in individuals with accelerated ageing.

Quantifying Age-Associated Cortical Complexity of Left Dorsolateral Prefrontal Cortex with Multiscale Measurements

BACKGROUND

Cortical complexity plays a central role in the diagnosis and prognosis of age-related diseases. However, little is known about the regional cortical complexity in the context of brain atrophy.

OBJECTIVE

We aimed to systematically examine the age-related changes of the cortical complexity of left dorsolateral prefrontal cortex (DLPFC) and its subregions.

METHODS

Two hundred and fourteen cognitively normal adults drawn from the Open Access Series of Imaging Studies (OASIS) were divided into four age groups: young, middle-aged, young-old, and old-old. Based on structural magnetic resonance imaging (sMRI) scans, the multiscale measures of cortical complexity included cortical thickness (mm), surface area (mm2), grey matter volume (mm3), density, gyrification index (GI), and fractal dimension (FD).

RESULTS

Advancing age was associated with reduced grey matter volume, pial surface area, density, and FD of left DLPFC, but correlated with increased cortical thickness and GI. Volumetric measures, cerebrospinal fluid volume in particular, showed better performance to discriminate young-old adults from old-old adults, while FD was more sensitive than the volumetric measures to discriminate young adults and middle-aged adults.

CONCLUSION

This is the first demonstration that chronological age has a pronounced and differential effect on the cortical complexity of left DLPFC. Our findings suggest that surface-based measures of cortical region, thickness, and gyrification in particular, could be considered as valuable imaging markers for the studies of aging brain and neurodegenerative diseases.

Dynamic changes of region-specific cortical features and scalp-to-cortex distance: implications for transcranial current stimulation modeling

BACKGROUND

Transcranial current stimulation in rehabilitation is a fast-growing field featured with computational and biophysical modeling. Cortical features and scalp-to-cortex distance (SCD) are key variables for determining the strength and distribution of the electric field, yet longitudinal studies able to capture these dynamic changes are missing. We sought to investigate and quantify the ageing effect on the morphometry and SCD of left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in normal ageing adults and mild cognitive impairment (MCI) converters.

METHODS

Baseline, 1-year and 3-year follow-up structural magnetic resonance imaging scans from normal ageing adults (n = 32), and MCI converters (n = 22) were drawn from the Open Access Series of Imaging Studies. We quantified the changes of the cortical features and SCDs of left M1 and DLPFC, including grey matter volume, white matter volume, cortical thickness, and folding. Head model was developed to simulate the impact of SCD on the electric field induced by transcranial current stimulation.

RESULTS

Pronounced ageing effect was found on the SCD of left DLPFC in MCI converters. The SCD change of left DLPFC from baseline to 3-year follow-up demonstrated better performance to discriminate MCI converters from normal ageing adults than the other morphometric measures. The strength of electric field was consequently decreased with SCD in MCI converters.

CONCLUSION

Ageing has a prominent, but differential effect on the region-specific SCD and cortical features in older adults with cognitive impairments. Our findings suggest that SCD, cortical thickness, and folding of the targeted regions could be used as valuable imaging markers when conducting transcranial brain stimulation in individuals with brain atrophy.

Hippocampal Volume Loss, Brain Amyloid Accumulation, and APOE Status in Cognitively Intact Elderly Subjects

BACKGROUND

Hippocampal volume loss (HVL), PET-documented brain amyloid accumulation, and APOE-ε4 status are predictive biomarkers of the transition from mild cognitive impairment to Alzheimer disease (AD). In asymptomatic cases, the role of these biomarkers remains ambiguous. In contrast to the idea that HVL occurs in late phases of neurodegeneration, recent contributions indicate that it might occur before abnormal amyloid PET occurrence in elderly subjects and that its severity could be only marginally related to APOE variants. Using a longitudinal design, we examined the determinants of HVL in our sample, i.e., brain amyloid burden and the presence of APOE-ε4, and made a longitudinal assessment of cognitive functions.

METHODS

We performed a 4.5-year longitudinal study on 81 elderly community dwellers (all right-handed;, 48 (59.3%) women; mean age 73.7 ± 3.7 years) including MRI at baseline and follow-up, PET amyloid during follow-up, neuropsychological assessment at 18 and 54 months, and APOE genotyping. All cases were assessed using a continuous cognitive score (CCS) that took into account the global evolution of neuropsychological performance. Linear regression models were used to identify predictors of HVL.

RESULTS

There was a negative association between the CCS and HVL bilaterally. In multivariate models adjusting for demographic variables, the presence of APOE-ε4 was related to increased HVL bilaterally. A trend of significance was observed with respect to the impact of amyloid positivity on HVL in the left hemisphere. No significant interaction was found between amyloid positivity and the APOE-ε4 allele.

CONCLUSION

The progressive decrement of neuropsychological performance is associated with HVL long before the emergence of clinically overt symptoms. In this cohort of healthy individuals, the presence of the APOE-ε4 allele was shown to be an independent predictor of worst hippocampal integrity in asymptomatic cases independently of amyloid positivity.

The Radiogenomics of Late-onset Alzheimer Disease

Radiogenomics, defined as the integrated analysis of radiologic imaging and genetic data, is a well-established tool shown to augment neuroimaging in the clinical diagnosis, prognostication, and scientific study of late-onset Alzheimer disease (LOAD). Early work using candidate single nucleotide polymorphisms (SNPs) identified genetic variation in APOE, BIN1, CLU, and CR1 as key modifiers of brain structure and function using magnetic resonance imaging (MRI). More recently, polygenic risk scores used in conjunction with MRI and positron emission tomography have shown great promise as a risk-stratification tool for clinical trials and care-management decisions. In addition, recent work using multimodal MRI and positron emission tomography as proxies of LOAD progression has identified novel risk variants that are enhancing our understanding of LOAD pathophysiology and progression. Herein, we highlight key studies and trends in the radiogenomics of LOAD over the past two decades and their implications for clinical practice and scientific research.

Beyond a Differential Diagnosis: Cognitive and Morphometric Decoding of Information Processing Speed in Senior Adults with DSM-5 Mild Neurocognitive Disorders

BACKGROUND

Processing speed has been highlighted as a diagnostic item for neurocognitive disorders (NCD) in DSM-5. The utility of information processing speed (IPS) enclosed with multiscale constructs in the diagnosis of NCD warrants exploration.

OBJECTIVE

We aimed to investigate the IPS with two types of measurements in the patients with NCD due to vascular disease (NCD-vascular) and NCD due to Alzheimer's disease (NCD-AD), and examine the associations between IPS measures and morphometric features.

METHODS

The IPS was evaluated using trail making test (TMT) and flanker test (n = 204). Direct scores, derived scores, and reaction time (RT) were used as IPS measures. Further, surface-based morphometry cortical volume was calculated in a subsample (n = 44) with structural MRI data.

RESULTS

All IPS measures showed a significant value to differentiate NCD patients from healthy subjects. Only mean RT could distinguish NCD-AD from NCD-vascular groups. TMT-B score and difference score were correlated with gray matter volume (GMV) of inferior frontal gyrus, precuneus and superior temporal cortex. Mean RT was associated with the GMV of post-central gyrus (r = -0.327, p = 0.035), and executive speed was associated with inferior frontal cortex (r = -0.475, p = 0.001), cingulate gyrus (r = -0.497, p = 0.001), and superior temporal gyrus (r = -0.36, p = 0.019).

CONCLUSION

The cognitive and morphometric correlates of IPS measures indicate that complex IPS might be decomposed into the domain-specific components with corresponding neural underpinnings. Our findings may also provide essential insights into the diagnostic item of NCD.

Aberrant interhemispheric functional connectivity within default mode network and its relationships with neurocognitive features in cognitively normal APOE ε 4 elderly carriers

BACKGROUND

Default mode network (DMN) is vulnerable to the effects of APOE genotype. Given the reduced brain volumes and APOE ε 4-related brain changes in elderly carriers, it is less known that whether these changes would influence the functional connectivity and to what extent. This study aimed to examine the functional connectivity within DMN, and its diagnostic value with age-related morphometric alterations considered.

METHODS

Whole brain and seed-based resting-state functional connectivity (RSFC) analysis were conducted in cognitively normal APOE ε 4 carriers and matched non-carriers (N=38). The absolute values of mean correlation coefficients (z-values) were used as a measure of functional connectivity strength (FCS) between DMN subregions, which were also used to estimate their diagnostic value by receiver-operating characteristic (ROC) curves.

RESULTS

APOE ε 4 carriers demonstrated decreased interhemispheric FCS, particularly between right hippocampal formation (R.HF) and left inferior parietal lobular (L.IPL) (t=3.487, p<0.001). ROC analysis showed that the FCS of R.HF and L.IPL could differentiate APOE ε 4 carriers from healthy counterparts (AUC value=0.734, p=0.025). Moreover, after adjusting the impact of morphometry, the differentiated value of FCS of R.HF and L.IPL was markedly improved (AUC value=0.828, p=0.002).

CONCLUSIONS

Our findings suggest that APOE ε 4 allele affects the functional connectivity within posterior DMN, particularly the atrophy-corrected interhemispheric FCS before the clinical expression of neurodegenerative disease.