Examining the identification of age-related atrophy between T1 and T1 + T2-FLAIR cortical thickness measurements

Differences in Cortical Morphology in People With and Without Migraine: A Registry for Migraine (REFORM) MRI Study

BACKGROUND AND OBJECTIVES

Structural imaging can offer insights into the cortical morphometry of migraine, which might reflect adaptations to recurring nociceptive messaging. This study compares cortical morphometry between a large sample of people with migraine and healthy controls, as well as across migraine subtypes.

METHODS

Adult participants with migraine and age-matched and sex-matched healthy controls attended a single MRI session with magnetization-prepared rapid acquisition gradient echo and fluid-attenuated inversion recovery sequences at 3T. Cortical surface area, thickness, and volume were compared between participants with migraine (including subgroups) and healthy controls across the whole cortex within FreeSurfer and reported according to the Desikan-Killiany atlas. The analysis used cluster-determining thresholds of p < 0.0001 and cluster-wise thresholds of p < 0.05, adjusted for age, sex, and total intracranial volume.

RESULTS

A total of 296 participants with migraine (mean age 41.6 years ± 12.4 SD, 261 women) and 155 healthy controls (mean age 41.1 years ± 11.7 SD, 133 women) were included. Among the participants with migraine, 180 (63.5%) had chronic migraine, 103 (34.8%) had migraine with aura, and 88 (29.7%) experienced a migraine headache during the scan. The total cohort of participants with migraine had reduced cortical surface area in the left insula, compared with controls (p < 0.0001). Furthermore, participants with chronic migraine (n = 180) exhibited reduced surface area in the left insula (p < 0.0001) and increased surface area in the right caudal anterior cingulate cortex (p < 0.0001), compared with controls. We found no differences specific to participants with aura or ongoing migraine headache. Post hoc tests revealed a positive correlation between monthly headache days and surface area within the identified anterior cingulate cluster (p = 0.014).

DISCUSSION

The identified cortical changes in migraine were limited to specific pain processing regions, including the insula and caudal anterior cingulate gyrus, and were most notable in participants with chronic migraine. These findings suggest persistent cortical changes associated with migraine.

TRIAL REGISTRATION INFORMATION

The REFORM study (clinicaltrials.gov identifier: NCT04674020).

Defining Overlooked Structures Reveals New Associations between the Cortex and Cognition in Aging and Alzheimer's Disease

Recent work suggests that indentations of the cerebral cortex, or sulci, may be uniquely vulnerable to atrophy in aging and Alzheimer's disease (AD) and that the posteromedial cortex (PMC) is particularly vulnerable to atrophy and pathology accumulation. However, these studies did not consider small, shallow, and variable tertiary sulci that are located in association cortices and are often associated with human-specific aspects of cognition. Here, we manually defined 4,362 PMC sulci in 432 hemispheres in 216 human participants (50.5% female) and found that these smaller putative tertiary sulci showed more age- and AD-related thinning than larger, more consistent sulci, with the strongest effects for two newly uncovered sulci. A model-based approach relating sulcal morphology to cognition identified that a subset of these sulci was most associated with memory and executive function scores in older adults. These findings lend support to the retrogenesis hypothesis linking brain development and aging and provide new neuroanatomical targets for future studies of aging and AD.

Sex, gender diversity, and brain structure in early adolescence

There remains little consensus about the relationship between sex and brain structure, particularly in early adolescence. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest-many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years old (N = 7195). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. Additional sensitivity analyses found that male versus female differences in gyrification and white matter were largely accounted for by total brain volume, rather than sex per se. The model with sex, but not gender diversity, was the best-fitting model in 60.1% of gray matter regions and 61.9% of white matter regions after adjusting for brain volume. The proportion of variance accounted for by sex was negligible to small in all cases. While models including felt-gender explained a greater amount of variance in a few regions, the felt-gender score alone was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Links between Neuroanatomy and Neurophysiology with Turning Performance in People with Multiple Sclerosis

Multiple sclerosis is accompanied by decreased mobility and various adaptations affecting neural structure and function. Therefore, the purpose of this project was to understand how motor cortex thickness and corticospinal excitation and inhibition contribute to turning performance in healthy controls and people with multiple sclerosis. In total, 49 participants (23 controls, 26 multiple sclerosis) were included in the final analysis of this study. All participants were instructed to complete a series of turns while wearing wireless inertial sensors. Motor cortex gray matter thickness was measured via magnetic resonance imaging. Corticospinal excitation and inhibition were assessed via transcranial magnetic stimulation and electromyography place on the tibialis anterior muscles bilaterally. People with multiple sclerosis demonstrated reduced turning performance for a variety of turning variables. Further, we observed significant cortical thinning of the motor cortex in the multiple sclerosis group. People with multiple sclerosis demonstrated no significant reductions in excitatory neurotransmission, whereas a reduction in inhibitory activity was observed. Significant correlations were primarily observed in the multiple sclerosis group, demonstrating lateralization to the left hemisphere. The results showed that both cortical thickness and inhibitory activity were associated with turning performance in people with multiple sclerosis and may indicate that people with multiple sclerosis rely on different neural resources to perform dynamic movements typically associated with fall risk.

Sex, gender diversity, and brain structure in children ages 9 to 11 years old

There remains little consensus about the relationship between sex and brain structure, particularly in childhood. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest - many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years-old (N=7693). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. The model with sex, but not gender diversity, was the best-fitting model in 75% of gray matter regions and 79% of white matter regions examined. The addition of gender to the sex model explained significantly more variance than sex alone with regard to bilateral cerebellum volume, left precentral cortical thickness, as well as gyrification in the right superior frontal gyrus, right parahippocampal gyrus, and several regions in the left parietal lobe. For mean diffusivity in the left uncinate fasciculus, the model with sex, gender, and their interaction captured the most variance. Nonetheless, the magnitude of variance accounted for by sex was small in all cases and felt-gender score was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years-old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity.

Tau pathology as determinant of changes in atrophy and cerebral blood flow: a multi-modal longitudinal imaging study

PURPOSE

Tau pathology is associated with concurrent atrophy and decreased cerebral blood flow (CBF) in Alzheimer's disease (AD), but less is known about their temporal relationships. Our aim was therefore to investigate the association of concurrent and longitudinal tau PET with longitudinal changes in atrophy and relative CBF.

METHODS

We included 61 individuals from the Amsterdam Dementia Cohort (mean age 65.1 ± 7.5 years, 44% female, 57% amyloid-β positive [Aβ +], 26 cognitively impaired [CI]) who underwent dynamic [18F]flortaucipir PET and structural MRI at baseline and 25 ± 5 months follow-up. In addition, we included 86 individuals (68 CI) who only underwent baseline dynamic [18F]flortaucipir PET and MRI scans to increase power in our statistical models. We obtained [18F]flortaucipir PET binding potential (BPND) and R1 values reflecting tau load and relative CBF, respectively, and computed cortical thickness from the structural MRI scans using FreeSurfer. We assessed the regional associations between i) baseline and ii) annual change in tau PET BPND in Braak I, III/IV, and V/VI regions and cortical thickness or R1 in cortical gray matter regions (spanning the whole brain) over time using linear mixed models with random intercepts adjusted for age, sex, time between baseline and follow-up assessments, and baseline BPND in case of analyses with annual change as determinant. All analyses were performed in Aβ-  cognitively normal (CN) individuals and Aβ+  (CN and CI) individuals separately.

RESULTS

In Aβ+ individuals, greater baseline Braak III/IV and V/VI tau PET binding was associated with faster cortical thinning in primarily frontotemporal regions. Annual changes in tau PET were not associated with cortical thinning over time in either Aβ+ or Aβ-  individuals. Baseline tau PET was not associated with longitudinal changes in relative CBF, but increases in Braak III/IV tau PET over time were associated with increases in parietal relative CBF over time in Aβ + individuals.

CONCLUSION

We showed that higher tau load was related to accelerated cortical thinning, but not to decreases in relative CBF. Moreover, tau PET load at baseline was a stronger predictor of cortical thinning than change of tau PET signal.

Different MRI structural processing methods do not impact functional connectivity computation

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular technique. This technique can assess several features of brain connectivity, such as inter-regional temporal correlation (functional connectivity), from which graph measures of network organization can be derived. However, these measures are prone to a certain degree of variability depending on the analytical steps during preprocessing. Many studies have investigated the effect of different preprocessing steps on functional connectivity measures; however, no study investigated whether different structural reconstructions lead to different functional connectivity metrics. Here, we evaluated the impact of different structural segmentation strategies on functional connectivity outcomes. To this aim, we compared different metrics computed after two different registration strategies. The first strategy used structural information from the 3D T1-weighted image (unimodal), while the second strategy implemented a multimodal approach, where an additional registration step used the information from the T2-weighted image. The impact of these different approaches was evaluated on a sample of 58 healthy adults. As expected, different approaches led to significant differences in structural measures (i.e., cortical thickness, volume, and gyrification index), with the maximum impact on the insula cortex. However, these differences were only slightly translated to functional metrics. We reported no differences in graph measures and seed-based functional connectivity maps, but slight differences in the insula when we compared the mean functional strength for each parcel. Overall, these results suggested that functional metrics are only slightly different when using a unimodal compared to a multimodal approach, while the structural output can be significantly affected.

A quantified comparison of cortical atlases on the basis of trait morphometricity

BACKGROUND

Many different brain atlases exist that subdivide the human cortex into dozens or hundreds of regions-of-interest (ROIs). Inconsistency across studies using one or another cortical atlas may contribute to the replication crisis across the neurosciences.

METHODS

Here, we provide a quantitative comparison between seven popular cortical atlases (Yeo, Desikan-Killiany, Destrieux, Jülich-Brain, Gordon, Glasser, Schaefer) and vertex-wise measures (thickness, surface area, and volume), to determine which parcellation retains the most information in the analysis of behavioural traits (incl. age, sex, body mass index, and cognitive ability) in the UK Biobank sample (N∼40,000). We use linear mixed models to compare whole-brain morphometricity; the proportion of trait variance accounted for when using a given atlas.

RESULTS

Commonly-used atlases resulted in a considerable loss of information compared to vertex-wise representations of cortical structure. Morphometricity increased linearly as a function of the log-number of ROIs included in an atlas, indicating atlas-based analyses miss many true associations and yield limited prediction accuracy. Likelihood ratio tests revealed that low-dimensional atlases accounted for unique trait variance rather than variance common between atlases, suggesting that previous studies likely returned atlas-specific findings. Finally, we found that the commonly-used atlases yielded brain-behaviour associations on par with those obtained with random parcellations, where specific region boundaries were randomly generated.

DISCUSSION

Our findings motivate future structural neuroimaging studies to favour vertex-wise cortical representations over coarser atlases, or to consider repeating analyses across multiple atlases, should the use of low-dimensional atlases be necessary. The insights uncovered here imply that cortical atlas choices likely contribute to the lack of reproducibility in ROI-based studies.

Parsimonious model for mass-univariate vertexwise analysis

Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results: We showed that when performed on a large sample ( N = 8662 , UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate > 0.6 ). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes. Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.

Relationships between preoperative cortical thickness, postoperative electroencephalogram slowing, and postoperative delirium

BACKGROUND

It is unclear how preoperative neurodegeneration and postoperative changes in EEG delta power relate to postoperative delirium severity. We sought to understand the relative relationships between neurodegeneration and delta power as predictors of delirium severity.

METHODS

We undertook a prospective cohort study of high-risk surgical patients (>65 yr old) to identify predictors of peak delirium severity (Delirium Rating Scale-98) with twice-daily delirium assessments (NCT03124303). Participants (n=86) underwent preoperative MRI; 54 had both an MRI and a postoperative EEG. Cortical thickness was calculated from the MRI and delta power from the EEG.

RESULTS

In a linear regression model, the interaction between delirium status and preoperative mean cortical thickness (suggesting neurodegeneration) across the entire cortex was a significant predictor of delirium severity (P<0.001) when adjusting for age, sex, and performance on preoperative Trail Making Test B. Next, we included postoperative delta power and repeated the analysis (n=54). Again, the interaction between mean cortical thickness and delirium was associated with delirium severity (P=0.028), as was postoperative delta power (P<0.001). When analysed across the Desikan-Killiany-Tourville atlas, thickness in multiple individual cortical regions was also associated with delirium severity.

CONCLUSIONS

Preoperative cortical thickness and postoperative EEG delta power are both associated with postoperative delirium severity. These findings might reflect different underlying processes or mechanisms.

CLINICAL TRIAL REGISTRATION

NCT03124303.

Association between plasma tau and postoperative delirium incidence and severity: a prospective observational study

BACKGROUND

Postoperative delirium is associated with increases in the neuronal injury biomarker, neurofilament light (NfL). Here we tested whether two other biomarkers, glial fibrillary acidic protein (GFAP) and tau, are associated with postoperative delirium.

METHODS

A total of 114 surgical patients were recruited into two prospective biomarker cohort studies with assessment of delirium severity and incidence. Plasma samples were sent for biomarker analysis including tau, NfL, and GFAP, and a panel of 10 cytokines. We determined a priori to adjust for interleukin-8 (IL-8), a marker of inflammation, when assessing associations between biomarkers and delirium incidence and severity.

RESULTS

GFAP concentrations showed no relationship to delirium. The change in tau from preoperative concentrations to postoperative Day 1 was greater in patients with postoperative delirium (P<0.001) and correlated with delirium severity (ρ=0.39, P<0.001). The change in tau correlated with increases in IL-8 (P<0.001) and IL-10 (P=0.0029). Linear regression showed that the relevant clinical predictors of tau changes were age (P=0.037), prior stroke/transient ischaemic attack (P=0.001), and surgical blood loss (P<0.001). After adjusting for age, sex, preoperative cognition, and change in IL-8, tau remained significantly associated with delirium severity (P=0.026). Using linear mixed effect models, only tau (not NfL or IL-8) predicted recovery from delirium (P<0.001).

CONCLUSIONS

The change in plasma tau was associated with delirium incidence and severity, and resolved over time in parallel with delirium features. The impact of this putative perioperative neuronal injury biomarker on long-term cognition merits further investigation.

CLINICAL TRIAL REGISTRATION

NCT02926417 and NCT03124303.

A unified framework for association and prediction from vertex-wise grey-matter structure

The recent availability of large‐scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex‐wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey‐matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case–control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex‐wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex‐wise complexity; (b) comparison of the information retained by different MRI processings.

Optimizing the accuracy of cortical volumetric analysis in traumatic brain injury

Cortical volumetric analysis is widely used to study the anatomic basis of neurological deficits in patients with traumatic brain injury (TBI). However, patients with TBI-related lesions are often excluded from MRI analyses because cortical lesions may compromise the accuracy of reconstructed surfaces upon which volumetric measurements are based. We developed a FreeSurfer-based lesion correction method and tested its impact on cortical volume measures in 87 patients with chronic moderate-to-severe TBI. We reconstructed cortical surfaces from T1-weighted MRI scans, then manually labeled and removed vertices on the cortical surfaces where lesions caused inaccuracies. Next, we measured the surface area of lesion overlap with seven canonical brain networks and the percent volume of each network affected by lesions.•

The lesion correction method revealed that cortical lesions in patients with TBI are preferentially located in the limbic and default mode networks (95.7% each), with the limbic network also having the largest average surface area (4.4+/−3.7%) and percent volume affected by lesions (12.7+/−9.7%).

•

The method has the potential to improve the accuracy of cortical volumetric measurements and permit inclusion of patients with lesioned brains in MRI analyses.

•

The method also provides new opportunities to elucidate network-based mechanisms of neurological deficits in patients with TBI.