Brain lateralization is considered evolutionarily adaptive. Impaired functional specialization is thought to cause abnormal lateralization in neurological disorders. However, the dynamic changes in brain laterality in Alzheimer's disease (AD) remain unclear. In this study, resting-state fMRI data and neuropsychological assessments from 109 participants (49 AD patients and 60 healthy controls [HC]) were used. Dynamic laterality time series were constructed by extracting the dynamic laterality index (DLI) within each sliding window. We assessed two key features: laterality reversal (LR), reflecting intra-hemispheric processing efficiency, and laterality fluctuation (LF), indicating inter-hemispheric communication. Group differences in dynamic laterality characteristics were analyzed using statistically rigorous methods, regressing out gender, age, years of education, and head movements. Spearman correlation analyses examined the relationship between laterality characteristics and cognitive performance. Our results showed that AD patients exhibited a more pronounced loss of left lateralization as well as stronger right lateralization, especially in the somatomotor network (SMN) and default mode network (DMN). Additionally, we observed decreased LR as well as increased LF with global trends in AD. These divergent changes disrupted the dynamical balance between intra- and inter-hemispheric information interaction. Notably, this imbalance depended on the degree of lateralization, and the higher order cognitive networks with high-level lateralization were more vulnerable. Importantly, the observed abnormal lateralization metrics were associated with worse cognitive impairment. Our study highlights a disruption of dynamic lateralization balance in higher order cortical networks in AD patients and reveals its potential role in the disease's pathophysiology.
Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) and more recently developed fixel-based derivatives to examine the relationship between brain diffusion microstructure and chronological age. These metrics are able to quantify signal fraction measurements at the voxel-wise level from six different tissue microenvironments found in the brain: extracellular free water, intracellular isotropic, intracellular anisotropic, fiber density and cross-section, the g-ratio, and aggregate conduction velocity. This study analyzes the Nathanial Kline Institute's Rockland cohort, a large-scale community sample of brain MRI data across the lifespan (409 subjects, ages 5–85 years). Microstructural measurements were taken in a number of structures throughout the white matter, subcortical gray matter, and lobar cortical regions while additionally evaluating lateral differences in microstructural measurements. The general trajectory of signal fraction measurements was a positive relationship with age and extracellular signal fraction and g-ratio, a negative relationship between age and intracellular isotropic signal fraction, fiber density and cross-section, and conduction velocity, and an inverted U-shaped trajectory for the intracellular anisotropic signal fraction. In individual sub-areas, these trends tended to still be present, with some notable exceptions. However, there were large differences in microstructure measurements between individual structures, including significant lateral differences between hemispheres for each of the subcortical gray matter structures and for each of the cortical regions. These results demonstrate that 3T-CSD and fixel-based metrics are able to describe age-related change across the brain and lifespan. By using a healthy population cohort this study can be used as a point of comparison for analysis of microstructure changes in the presence of pathology or with behavior. Finally, the detailed analysis of lateralized ROI results can inform diffusion microstructure studies examining cortical and subcortical regions.
Preeclampsia is a severe pregnancy complication that can cause brain injury, yet early detection of related cognitive deficits remains challenging. Therefore, in order to investigate alterations in choroid plexus volume (CPV) and susceptibility values of the choroid plexus (ChP) obtained from quantitative susceptibility mapping (QSM) in preeclampsia patients, we enrolled 281 participants, comprising 98 nonpregnant healthy controls (NPHC), 85 pregnant healthy controls (PHC), and 98 patients with preeclampsia. All participants were scanned on a 1.5 T MR scanner. The results of clinical characteristics and cognitive tests were collected from all the participants. One-way ANOVA tests were used to analyze the differences in CPV and susceptibility values of ChP among the three groups. Multiple linear regression analysis was used to find the factors that influenced CPV and its susceptibility values, as well as cognitive decline. Additionally, receiver operating characteristic (ROC) analysis was employed to evaluate the diagnostic performance of the two imaging measures. Preeclampsia patients exhibited smaller CPV and higher susceptibility values compared to the other groups (p < 0.001; p < 0.001). Significant negative correlations were observed between body mass index (BMI), mean arterial pressure and CPV/eTIV (β = −0.100, 95% CI = −0.158 ~ −0.042, p = 0.001; β = −0.022, 95% CI = −0.033 ~ −0.011, p < 0.001). Additionally, significant positive correlations were observed between BMI (β = 0.455, 95% CI = 0.125 ~ 0.786, p = 0.007), mean arterial pressure (β = 0.170, 95% CI = 0.107 ~ 0.232, p < 0.001), hemoglobin (β = 0.152, 95% CI = 0.051 ~ 0.254, p = 0.003) and susceptibility values of ChP. Furthermore, CPV/eTIV and susceptibility values of ChP could be independent contributing factors of scores of TMT. The combination of CPV, susceptibility values of ChP, BMI and gestational week could distinguish preeclampsia from pregnant groups (AUC = 0.787, 95% CI = 0.722–0.853, p < 0.001) as well as distinguish individuals with cognitive decline from preeclampsia patients (AUC = 0.737, 95% CI = 0.621–0.844, p < 0.001). These findings indicate that smaller CPV and higher susceptibility values characterize preeclampsia and may serve as auxiliary indices for its diagnosis and related cognitive decline.
Frahm, L., E. C. Cieslik, F. Hoffstaedter, et al. 2022. “Evaluation of Thresholding Methods for Activation Likelihood Estimation Meta-Analysis via Large-Scale Simulations.” Human Brain Mapping 43, no. 13: 3987–3997. https://doi.org/10.1002/hbm.25898.
There is a mistake in the author contributions of the paper. The text reads “Lennart F Edna C. Cieslik, and Robert Langner came up with the first drafts of the paper.” This should have read: “Lennart Frahm came up with the first draft of the paper. Edna C. Cieslik and Robert Langner gave feedback.”
We apologize for this error.
Fixel-based analysis (FBA) is an advanced diffusion MRI analysis technique that facilitates the evaluation of white matter microstructure within ‘fixels’ (specific fibre populations within a voxel). In recent years, FBA has gained prominence for its ability to better characterise fibre tract-specific changes than the more conventional diffusion MRI approaches and has shown promise in elucidating the pathophysiology of psychiatric and neurological diseases. However, FBA has been predominantly limited to single-centre studies, minimising the generalisability of the technique. In this study, the popular ComBat harmonisation technique was adapted for whole-brain FBA of diffusion MRI data. The study evaluates the effectiveness of ComBat in harmonising FBA metrics of fibre density, fibre cross-section and the combined metric of fibre density and cross-section in a large travelling subject dataset (n = 49, scan = 162). Participants were scanned across multiple centres, using different scanner models and imaging protocols, and FBA metrics were compared under these varying conditions before and after harmonisation. In addition, the impact of ComBat harmonisation on disease-related findings was evaluated in an independent multi-centre Alzheimer's disease (AD) dataset, by comparing the same fixel-based measures in patients with AD (n = 27) to those in cognitively normal control participants (n = 29) before and after ComBat harmonisation. We demonstrated that ComBat harmonisation effectively mitigated variability across scanner sites, scanner models, and protocols, in the travelling subject dataset, thus enhancing the comparability of FBA metrics. Notably, ComBat harmonisation improved the detection of AD-related changes in the fornix, a critical white matter tract associated with cognitive function, and strengthened the correlations between FBA metrics and cognitive scores. These results underscore the potential of ComBat harmonisation in enhancing the reliability of multi-centre neuroimaging studies, supporting the use of harmonisation techniques for accurate detection of disease-specific changes in neurodegenerative conditions. The ability to perform ComBat harmonisation within the whole-brain FBA pipeline may help further this fibre-specific technique into large-scale multi-centre studies.
The Amyloid/Tau/Neurodegeneration (A/T/N) biomarker framework is used for in vivo pathological profiling of Alzheimer's disease. A binary (+/−) label is assigned for each pathology (A, T, N) based on its presence or absence as determined by imaging or fluid biomarkers. Using imaging, neurodegeneration is confirmed by either structural MRI (indicating atrophy) or 18F-FDG PET (indicating hypometabolism), implicitly assuming that both modalities identify equivalent pathology. Preliminary evidence suggests that atrophy and hypometabolism do not spatially co-localize and are likely caused by distinct underlying pathologies. Further, while MRI is widely available, many sites lack access to PET. Recent evidence indicates that hemodynamic indices, identified via functional MRI or SPECT, may be spatially concordant with hypometabolism identified by 18F-FDG-PET and able to serve as reliable proxies. Coordinate-based meta-analyses of voxel-based morphometry and voxel-based physiology reports in Alzheimer's disease, collectively analyzing 139 articles (4218 individuals) were performed to test the following hypotheses: (1) that atrophy and hypometabolism in AD are spatially dissociated; (2) that hemodynamic indices exhibit the same spatial distributions as hypometabolism identified by PET; (3) that regions of atrophy and hypometabolism involve different functional systems and cognitive operations. Results confirmed all three hypotheses. Separation of atrophy and hypometabolism into two distinct subcategories of neurodegeneration as well as the development of regionally specific biomarkers for each are in order.
The amygdala is a key element of the limbic system. It is involved in emotional processing controlling responses to social, affective, or motivational stimuli, facilitating interaction with the surrounding environment. Given its importance in typical development, understanding early normative growth patterns in the amygdala can provide insights into the origins of emotional and social behaviors. This study examined age-related differences in amygdala volume from the fetal to early childhood period across six cohorts from in utero to the first 4 years of life, and explored the association with social, cognitive and communication outcomes. Amygdala volumes were analyzed in 471 participants ranging from 27 to 195 weeks postmenstrual age (PMA). The results of the mixed model analyses indicated that volume significantly increased in size with age (t(534) = 7.92, p < 0.001), with the left amygdala demonstrating stronger age-related changes (p < 0.001) and with males showing steeper age-related changes compared to females (p = 0.012). In a subset of participants with developmental outcome data (n = 30), significant associations were observed for the left amygdala, with larger volumes predicting receptive communication (B = −0.02, p = 0.017), including an interaction with sex (B = 0.03, p = 0.028). Additional effects were found for social–emotional (B = −0.03, p < 0.001) and cognitive outcomes (B = −0.02, p < 0.001), with interactions by sex and age (all p < 0.05), whereas no significant findings were detected for the right amygdala. These results characterize age-related changes in amygdala volumes early in development and highlight the role of sex and laterality, while underscoring its importance in social communication during early development.
Emotion regulation relies on the flexible coordination of neural networks involved in strategy selection and implementation. While previous studies have focused on task-related brain activity, the role of intrinsic, resting-state connectivity in shaping regulatory tendency in strategy selection and capacity in strategy implementation remains less well understood. Using spectral Dynamic Causal Modeling (spDCM) of resting-state fMRI data, we examined how effective connectivity within four emotion-related brain networks predicts individual differences in the capacity to implement and the tendency to select reappraisal versus distraction for high-intensity emotional stimuli. Forty healthy adults completed two emotion regulation tasks and a 10-min resting-state fMRI scan. We found that distinct and partially overlapping network dynamics predicted both strategy-specific regulation capacity and reappraisal tendency. Notably, the fronto-parietal and parieto-limbic networks were central to both capacity and tendency. In addition, fronto-lateral and limbic networks significantly contributed to the prediction of strategy-specific measures: Reappraisal capacity was associated with broader and more inhibitory connectivity, whereas distraction capacity was related to more localized and mixed excitatory/inhibitory connectivity patterns. Crucially, the connections most predictive of distraction and reappraisal capacity were distinct rather than shared, underscoring the importance of strategy-specific neural adaptations. These findings suggest that intrinsic brain network configurations influence the individual capacity to implement specific strategies and the tendency to select one strategy over the other.
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