Automated sulcal depth measurement on cortical surface reflecting geometrical properties of sulci

Alterations in structural and functional magnetic resonance imaging associated with cognitive function in patients with treatment-naïve first-episode major depressive disorder

BACKGROUND

Cognitive impairment is a prominent feature in the clinical presentation of major depressive disorder (MDD). Patients with MDD have brain structural and functional abnormalities. However, the association between such abnormalities and cognitive function remains unclear.

METHODS

For this research, 105 patients with treatment-naïve first-episode MDD and 53 healthy controls (HCs) underwent magnetic resonance imaging (MRI) and neuropsychological assessment. The MRI main indicators included sulcus depth (SD), local gyration index (LGI) and amplitude of low frequency fluctuations (ALFF). The MATRICS Consensus Cognitive Battery (MCCB) was used for neuropsychological assessment. The support vector machine (SVM) was used to assess the accuracy of the classification.

RESULTS

Compared with the HCs, the patients with MDD had significant decreases in five dimensions of the MCCB, as well as in SD in the left superior temporal sulcus and inferior parietal cortex, but had an increases in LGI in the left precuneus cortex and pericalcarine cortex and ALFF of the left calcarine fissure and surrounding cortex. In addition, the visual learning score (one MCCB dimension) was negatively associated with the SD of the left superior temporal sulcus and positively associated with the ALFF of left calcarine fissure and surrounding cortex. The SVM has a relatively good ability to distinguish patients with MDD and HCs.

CONCLUSION

Cognitive impairment in patients with MDD was associated with abnormal an SD and ALFF. These findings help to further understand cognitive impairment in patients with MDD.

Foetal cortical expansion is associated with neurodevelopmental outcome at 2-years in congenital heart disease: a longitudinal follow-up study

BACKGROUND

In adolescents and adults with complex congenital heart disease (CHD), abnormal cortical folding is a putative predictor of poor neurodevelopmental outcome. However, it is unknown when this relationship first emerges. We test the hypothesis that it begins in utero, when the brain starts to gyrify and folding patterns first become established.

METHODS

We carried out a prospective, longitudinal case-control study, acquiring foetal MRIs at two timepoints in utero, (Scan 1 = 20-30 Gestational Weeks (GW) and Scan 2 = 30-39 GW), then followed up participants at two years of age to assess neurodevelopmental outcomes. We used normative modelling to chart growth trajectories of surface features across 60 cortical regions in a control population (n = 157), then quantified the deviance of each foetus with CHD (n = 135) and explored the association with neurodevelopmental outcomes at two years of age.

FINDINGS

Differences in cortical development between CHD and Control foetuses only emerged after 30 GW, and lower regional cortical surface area growth was correlated with poorer neurodevelopmental outcomes at two years of age in the CHD group.

INTERPRETATION

This work highlights the third trimester specifically as a critical period in brain development for foetuses with CHD, where the reduced surface area expansion in specific cortical regions becomes consequential in later life, and predictive of neurodevelopmental outcome in toddlerhood.

FUNDING

This research was supported by the NINDS (R01NS114087, K23NS101120) and NIBIB (R01EB031170) of the NIH, PHN Scholar Award, AAN Clinical Research Training Fellowship, BBRF Young Investigator Awards, and the Farb Family Fund.

Role of data-driven regional growth model in shaping brain folding patterns

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. Recent findings indicate significant regional variations in brain tissue growth, while the role of these variations in cortical development remains unclear. In this study, we explored how regional cortical growth affects brain folding patterns using computational simulation. We first developed growth models for typical cortical regions using machine learning (ML)-assisted symbolic regression, based on longitudinal real surface expansion and cortical thickness data from prenatal and infant brains derived from over 1000 MRI scans of 735 pediatric subjects with ages ranging from 29 postmenstrual weeks to 2 years of age. These models were subsequently integrated into computational software to simulate cortical development with anatomically realistic geometric models. We comprehensively quantified the resulting folding patterns using multiple metrics such as mean curvature, sulcal depth, and gyrification index. Our results demonstrate that regional growth models generate complex brain folding patterns that more closely match actual brains structures, both quantitatively and qualitatively, compared to conventional uniform growth models. Growth magnitude plays a dominant role in shaping folding patterns, while growth trajectory has a minor influence. Moreover, multi-region models better capture the intricacies of brain folding than single-region models. Our results underscore the necessity and importance of incorporating regional growth heterogeneity into brain folding simulations, which could enhance early diagnosis and treatment of cortical malformations and neurodevelopmental disorders such as cerebral palsy and autism.

Longitudinal Assessment of Abnormal Cortical Folding in Fetuses and Neonates With Isolated Non-Severe Ventriculomegaly

PURPOSE

The impact of ventriculomegaly (VM) on cortical development and brain functionality has been extensively explored in existing literature. VM has been associated with higher risks of attention-deficit and hyperactivity disorders, as well as cognitive, language, and behavior deficits. Some studies have also shown a relationship between VM and cortical overgrowth, along with reduced cortical folding, both in fetuses and neonates. However, there is a lack of longitudinal studies that study this relationship from fetuses to neonates.

METHOD

We used a longitudinal dataset of 30 subjects (15 healthy controls and 15 subjects diagnosed with isolated non-severe VM (INSVM)) with structural MRI acquired in and ex utero for each subject. We focused on the impact of fetal INSVM on cortical development from a longitudinal perspective, from the fetal to the neonatal stage. Particularly, we examined the relationship between ventricular enlargement and both volumetric features and a multifaceted set of cortical folding measures, including local gyrification, sulcal depth, curvature, and cortical thickness.

FINDINGS

Our results show significant effects of isolated non-severe VM (INSVM) compared to healthy controls, with reduced cortical thickness in specific brain regions such as the occipital, parietal, and frontal lobes.

CONCLUSION

These findings align with existing literature, confirming the presence of alterations in cortical growth and folding in subjects with isolated non-severe VM (INSVM) from the fetal to neonatal stage compared to controls.

TOMM40 may mediate GFAP, neurofilament light Protein, pTau181, and brain morphometry in aging

A growing amount of data has implicated the TOMM40 gene in the risk for Alzheimer's disease (AD), neurodegeneration, and accelerated aging. No studies have investigated the relationship of TOMM40 rs2075650 ('650) on the structural complexity of the brain or plasma markers of neurodegeneration. We used a comprehensive approach to quantify the impact of TOMM40 '650 on brain morphology and multiple cortical attributes in cognitively unimpaired (CU) individuals. We also tested whether the presence of the risk allele, G, of TOMM40 '650 was associated with plasma markers of amyloid, tau, and neurodegeneration and if there were interactions with age and sex, controlling for the effects of APOE ε4. We found that the TOMM40 '650 G-allele was associated with decreased sulcal depth, increased gyrification index, and decreased gray matter volume. NfL, GFAP, and pTau181 had independent and age-associated increases in individuals with a G-allele. Our data suggest that TOMM40 '650 is associated with aging-related plasma biomarkers and brain structure variation in temporal-limbic circuits.

Cortical morphological alterations in cognitively normal Parkinson's disease with severe hyposmia

Olfactory dysfunction is a common non-motor symptom of Parkinson's disease(PD) and may hold valuable insights into the disease's underlying pathophysiology. This study aimed to investigate cortical morphometry alterations in PD patients with severe hyposmia(PD-SH) and mild hyposmia(PD-MH) using surface-based morphometry(SBM) methods. Participants included 36 PD-SH patients, 38 PD-MH patients, and 40 healthy controls(HCs). SBM analysis revealed distinct patterns of cortical alterations in PD-SH and PD-MH patients. PD-MH patients exhibited reduced cortical thickness in the right supramarginal gyrus, while PD-SH patients showed widespread cortical thinning in regions including the bilateral pericalcarine cortex, bilateral lingual gyrus, left inferior parietal cortex, left lateral occipital cortex, right pars triangularis, right cuneus, and right superior parietal cortex. Moreover, PD-SH patients displayed reduced cortical thickness in the right precuneus compared to PD-MH patients. Fractal dimension analysis indicated increased cortical complexity in PD-MH patients' right superior temporal cortex and right supramarginal gyrus, as well as decreased complexity in the bilateral postcentral cortex, left superior parietal cortex, and right precentral cortex. Similarly, cortical gyrification index and cortical sulcal depth exhibited heterogeneous patterns of changes in PD-SH and PD-MH patients compared to HCs. These findings underscore the multifaceted nature of olfactory impairment in PD, with distinct patterns of cortical morphometry alterations associated with different degrees of hyposmia. The observed discrepancies in brain regions showing alterations reflect the complexity of PD's pathophysiology. These insights contribute to a deeper understanding of olfactory dysfunction in PD and provide potential avenues for early diagnosis and targeted interventions.

Surface-Based Morphometry Analysis of the Cerebral Cortex in Patients With Probable Idiopathic Rapid Eye Movement Sleep Behavior Disorder

INTRODUCTION

Strong indications support the notion that idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) acts as a precursor to multiple α-synucleinopathies, including Parkinson's disease and dementia with Lewy bodies. Despite numerous investigations into the alterations in cortical thickness and the volume of subcortical areas associated with this condition, comprehensive studies on the cortical surface morphology, focusing on gyrification and sulcal depth changes, are scarce. The purpose of this research was to explore the cortical surface morphology in individuals with probable iRBD (piRBD), to pinpoint early-phase diagnostic markers.

METHODS

This study included 30 piRBD patients confirmed using the RBD Screening Questionnaire (RBDSQ) and 33 control individuals selected from the Parkinson's Progression Markers Initiative (PPMI) database. They underwent neurophysiological tests and MRI scans. The FreeSurfer software was utilized to estimate cortical thickness (CTH), cortical and subcortical volumetry, local gyrification index (LGI), and sulcus depth (SD). Subsequently, these parameters were compared between the two groups. Additionally, linear correlation analysis was employed to estimate the relationship between brain morphological parameters and clinical parameters.

RESULTS

Compared to the healthy control (HC), piRBD patients exhibited a significant reduction in CTH, LGI, and cortical volume in the bilateral superior parietal, lateral occipital, orbitofrontal, temporo-occipital, bilateral rostral middle frontal, inferior parietal, and precentral brain regions. Moreover, a significant and notable correlation was observed between CTH and Geriatric Depression Scale (GDS), letter-number sequencing (LTNS), the Benton Judgment of Line Orientation (BJLO) test, and the symbol digit modalities test (SDMT) in several brain regions encompassing the motor cortex.

CONCLUSION

Patients with piRBD displayed widespread atrophy in various brain regions, predominantly covering the motor and sensory cortex. Furthermore, LGI could serve as a prognostic biomarker of disease's progression in piRBD.

The Contribution of Explainable Machine Learning Algorithms Using ROI-based Brain Surface Morphology Parameters in Distinguishing Early-onset Schizophrenia From Bipolar Disorder

RATIONALE AND OBJECTIVES

To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms.

METHOD

High-resolution T1-weighted images were obtained to measure cortical thickness (CT), gyrification, gyrification index (GI), sulcal depth (SD), fractal dimension (FD), and brain volumes. After the feature selection step, ML classifiers were applied for each feature set and the combination of them. The SHapley Additive exPlanations (SHAP) technique was implemented to interpret the contribution of each feature.

FINDINGS

144 adolescents (16.2 ± 1.4 years, female=39%) with EOS (n = 81) and EBD (n = 63) were included. The Adaptive Boosting (AdaBoost) algorithm had the highest accuracy (82.75%) in the whole dataset that includes all variables from Destrieux atlas. The best-performing algorithms were K-nearest neighbors (KNN) for FD subset, support vector machine (SVM) for SD subset, and AdaBoost for GI subset. The KNN algorithm had the highest accuracy (accuracy=79.31%) in the whole dataset from the Desikan-Killiany-Tourville atlas.

CONCLUSION

This study demonstrates the use of ML in the differential diagnosis of EOS and EBD using surface-based morphometry measurements. Future studies could focus on multicenter data for the validation of these results.

Neuroimaging Insights: Structural Changes and Classification in Ménière's Disease

OBJECTIVES

This study aimed to comprehensively investigate the neuroanatomical alterations associated with idiopathic Ménière's disease (MD) using voxel-based morphometry and surface-based morphometry techniques. The primary objective was to explore nuanced changes in gray matter volume, cortical thickness, fractal dimension, gyrification index, and sulcal depth in MD patients compared with healthy controls (HC). Additionally, we sought to develop a machine learning classification model utilizing these neuroimaging features to effectively discriminate between MD patients and HC.

DESIGN

A total of 55 patients diagnosed with unilateral MD and 70 HC were enrolled in this study. Voxel-based morphometry and surface-based morphometry were employed to analyze neuroimaging data and identify structural differences between the two groups. The selected neuroimaging features were used to build a machine learning classification model for distinguishing MD patients from HC.

RESULTS

Our analysis revealed significant reductions in gray matter volume in MD patients, particularly in frontal and cingulate gyri. Distinctive patterns of alterations in cortical thickness were observed in brain regions associated with emotional processing and sensory integration. Notably, the machine learning classification model achieved an impressive accuracy of 84% in distinguishing MD patients from HC. The model's precision and recall for MD and HC demonstrated robust performance, resulting in balanced F1-scores. Receiver operating characteristic curve analysis further confirmed the discriminative power of the model, supported by an area under the curve value of 0.92.

CONCLUSIONS

This comprehensive investigation sheds light on the intricate neuroanatomical alterations in MD. The observed gray matter volume reductions and distinct cortical thickness patterns emphasize the disease's impact on neural structure. The high accuracy of our machine learning classification model underscores its diagnostic potential, providing a promising avenue for identifying MD patients. These findings contribute to our understanding of MD's neural underpinnings and offer insights for further research exploring the functional implications of structural changes.

Dynamic changes in subplate and cortical plate microstructure at the onset of cortical folding in vivo

Cortical gyrification takes place predominantly during the second to third trimester, alongside other fundamental developmental processes, such as the development of white matter connections, lamination of the cortex and formation of neural circuits. The mechanistic biology that drives the formation cortical folding patterns remains an open question in neuroscience. In our previous work, we modelled the in utero diffusion signal to quantify the maturation of microstructure in transient fetal compartments, identifying patterns of change in diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester. In this work, we apply the same modelling approach to explore whether microstructural maturation of these compartments is correlated with the process of gyrification. We quantify the relationship between sulcal depth and tissue anisotropy within the cortical plate (CP) and underlying subplate (SP), key transient fetal compartments often implicated in mechanistic hypotheses about the onset of gyrification. Using in utero high angular resolution multi-shell diffusion-weighted imaging (HARDI) from the Developing Human Connectome Project (dHCP), our analysis reveals that the anisotropic, tissue component of the diffusion signal in the SP and CP decreases immediately prior to the formation of sulcal pits in the fetal brain. By back-projecting a map of folded brain regions onto the unfolded brain, we find evidence for cytoarchitectural differences between gyral and sulcal areas in the late second trimester, suggesting that regional variation in the microstructure of transient fetal compartments precedes, and thus may have a mechanistic function, in the onset of cortical folding in the developing human brain.

Automatic cortical surface parcellation in the fetal brain using attention-gated spherical U-net

Cortical surface parcellation for fetal brains is essential for the understanding of neurodevelopmental trajectories during gestations with regional analyses of brain structures and functions. This study proposes the attention-gated spherical U-net, a novel deep-learning model designed for automatic cortical surface parcellation of the fetal brain. We trained and validated the model using MRIs from 55 typically developing fetuses [gestational weeks: 32.9 ± 3.3 (mean ± SD), 27.4-38.7]. The proposed model was compared with the surface registration-based method, SPHARM-net, and the original spherical U-net. Our model demonstrated significantly higher accuracy in parcellation performance compared to previous methods, achieving an overall Dice coefficient of 0.899 ± 0.020. It also showed the lowest error in terms of the median boundary distance, 2.47 ± 1.322 (mm), and mean absolute percent error in surface area measurement, 10.40 ± 2.64 (%). In this study, we showed the efficacy of the attention gates in capturing the subtle but important information in fetal cortical surface parcellation. Our precise automatic parcellation model could increase sensitivity in detecting regional cortical anomalies and lead to the potential for early detection of neurodevelopmental disorders in fetuses.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

Background

Deep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.

Methods

We examined the association between the predicted brain age difference (PAD: predicted brain age-chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.

Results

Our results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.

Conclusion

These results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques

BACKGROUND AND PURPOSE

While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques.

MATERIALS AND METHODS

This is a multi-institutional IRB-approved study in which pregnant women with and without opioid use during the current pregnancy were prospectively recruited to undergo fetal MR imaging. A total of 14 opioid-exposed (31.4 ± 2.3 weeks of gestation) and 15 unexposed (31.4 ± 2.4 weeks) fetuses were included. Whole brain volume, cortical plate volume, surface area, sulcal depth, mean curvature, and gyrification index were computed as quantitative features by using our fetal brain MR imaging processing pipeline.

RESULTS

After correcting for gestational age, fetal sex, maternal education, polysubstance use, high blood pressure, and MR imaging acquisition site, all of the global morphologic features were significantly lower in the opioid-exposed fetuses compared with the unexposed fetuses, including brain volume, cortical volume, cortical surface area, sulcal depth, cortical mean curvature, and gyrification index. In regional analysis, the opioid-exposed fetuses showed significantly decreased surface area and sulcal depth in the bilateral Sylvian fissures, central sulci, parieto-occipital fissures, temporal cortices, and frontal cortices.

CONCLUSIONS

In this small cohort, prenatal opioid exposure was associated with altered fetal brain development in the third trimester. This adds to the growing body of literature demonstrating that prenatal opioid exposure affects the developing brain.

Structural brain morphometry differences and similarities between young patients with Crohn's disease in remission and healthy young and old controls

Introduction

Crohn's disease (CD), one of the main phenotypes of inflammatory bowel disease (IBD), can affect any part of the gastrointestinal tract. It can impact the function of gastrointestinal secretions, as well as increasing the intestinal permeability leading to an aberrant immunological response and subsequent intestinal inflammation. Studies have reported anatomical and functional brain changes in Crohn's Disease patients (CDs), possibly due to increased inflammatory markers and microglial cells that play key roles in communicating between the brain, gut, and systemic immune system. To date, no studies have demonstrated similarities between morphological brain changes seen in IBD and brain morphometry observed in older healthy controls..

Methods

For the present study, twelve young CDs in remission (M = 26.08 years, SD = 4.9 years, 7 male) were recruited from an IBD Clinic. Data from 12 young age-matched healthy controls (HCs) (24.5 years, SD = 3.6 years, 8 male) and 12 older HCs (59 years, SD = 8 years, 8 male), previously collected for a different study under a similar MR protocol, were analyzed as controls. T1 weighted images and structural image processing techniques were used to extract surface-based brain measures, to test our hypothesis that young CDs have different brain surface morphometry than their age-matched young HCs and furthermore, appear more similar to older HCs. The phonemic verbal fluency (VF) task (the Controlled Oral Word Association Test, COWAT) (Benton, 1976) was administered to test verbal cognitive ability and executive control.

Results/Discussion

On the whole, CDs had more brain regions with differences in brain morphometry measures when compared to the young HCs as compared to the old HCs, suggesting that CD has an effect on the brain that makes it appear more similar to old HCs. Additionally, our study demonstrates this atypical brain morphometry is associated with function on a cognitive task. These results suggest that even younger CDs may be showing some evidence of structural brain changes that demonstrate increased resemblance to older HC brains rather than their similarly aged healthy counterparts.

Cortical surface analysis for focal cortical dysplasia diagnosis by using PET images

Focal cortical dysplasia (FCD) is a neurological disorder distinguished by faulty brain cell structure and development. Repetitive and uncontrollable seizures may be linked to FCD's aberrant cortical thickness, gyrification, and sulcal depth. Quantitative cortical surface analysis is a crucial alternative to ineffective visual inspection. This study recruited 42 subjects including 22 FCD patients who underwent surgery and 20 healthy controls (HC). For the FCD patients, T1-weighted and PET images were obtained by a PET-MRI scanner, and the confirmed epileptogenic zone (EZ) was collected from postsurgical follow-up. For the HCs, CT and PET images were obtained by a PET-CT scanner. Cortical thickness, gyrification index, and sulcal depth were calculated using a computational anatomical toolbox (CAT12). A cluster-based analysis is carried out to determine each FCD patient's aberrant cortical surface. After parcellating the cerebral cortex into 68 regions by the Desikan-Killiany atlas, a region of interest (ROI) analysis was conducted to know whether the feature in the FCD group is significantly different from that in the HC group. Finally, the features of all ROIs were utilised to train a support vector machine classifier (SVM). The classification performance is evaluated by the leave-one-out cross-validation. The cluster-based analysis can localize the EZ cluster with the highest accuracy of 54.5 % (12/22) for cortical thickness, 40.9 % (9/22) and 13.6 % (3/22) for sulcal depth and gyrification, respectively. Moderate concordance (Kappa, 0.6) is observed between the confirmed EZs and identified clusters by using the cortical thickness. Fair concordance (Kappa, 0.3) and no concordance (Kappa, 0.1) is found by using sulcal depth and gyrification. Significant differences are found in 46 of 68 regions (67.7 %) for the three measures. The trained SVM classifier achieved a prediction accuracy of 95.5 % for the cortical thickness, while the sulcal depth and the gyrification obtained 86.0 % and 81.5 %. Cortical thickness, as determined by quantitative cortical surface analysis of PET data, has a greater ability than sulcal depth and gyrification to locate aberrant EZ clusters in FCD. Surface measures might be different in many regions for FCD and HC. By integrating machine learning and cortical morphologies features, individual prediction of FCD seems to be feasible.

Loss of control eating in children is associated with altered cortical and subcortical brain structure

Introduction

Loss of control (LOC) eating is the perceived inability to control how much is eaten, regardless of actual amount consumed. Childhood LOC-eating is a risk factor for the development of binge-eating disorder (BED), but its neurobiological basis is poorly understood. Studies in children with BED have shown both increased gray matter volume in regions related to top-down cognitive control (e.g., dorsolateral prefrontal cortex) and reward-related decision making (e.g., orbital frontal cortex) relative to healthy controls. However, no studies have examined brain structure in children with LOC-eating. To identify potential neurobiological precursors of BED, we conducted secondary analysis of five studies that conducted T1 MPRAGE scans.

Methods

A total of 143, 7-12-year-old children (M = 8.9 years, 70 boys) were included in the study, 26% of which (n = 37) reported LOC-eating (semi-structured interview). Age, sex, and obesity status did not differ by LOC-eating. Differences between children with and without LOC were examined for gray matter volume, cortical thickness, gyrification, sulci depth, and cortical complexity after adjusting for age, sex, total intercranial volume, weight status, and study.

Results

Children with LOC, relative to those without, had greater gray matter volume in right orbital frontal cortex but lower gray matter volume in right parahippocampal gyrus, left CA4/dentate gyrus, and left cerebellar lobule VI. While there were no differences in cortical thickness or gyrification, children with LOC-eating had great sulci depth in left anterior cingulate cortex and cuneus and greater cortical complexity in right insular cortex.

Discussion

Together, this indicates that children with LOC-eating have structural differences in regions related to cognitive control, reward-related decision-making, and regulation of eating behaviors.

Correlation Analysis of ApoB, ApoA1, and ApoB/ApoA1 with Cortical Morphology in Patients with Memory Complaints

Background

Apolipoproteins and cortical morphology are closely associated with memory complaints, and both may contribute to the development of Alzheimer's disease.

Objective

To examine whether apolipoprotein B (ApoB), apolipoprotein A-1 (ApoA1), and their ratio (ApoB/ApoA1) are associated with cortical morphology in patients with memory complaints.

Methods

Ninety-seven patients underwent neuropsychological testing, measurements of ApoB, ApoA1, ApoB/ApoA1, plasma Alzheimer's biomarker, apolipoprotein E (ApoE) genotyping, and 3T structural magnetic resonance imaging (sMRI) scans. Based on sMRI scanning locations, patients were categorized into the University of Electronic Science and Technology (UESTC) and the Fourth People's Hospital of Chengdu (FPHC). The Computational Anatomy Toolbox within Statistical Parametric Mapping was used to calculate each patient's cortical morphology index based on sMRI data. The cortical morphology index and apolipoproteins were also analyzed.

Results

Significant positive correlations were found between ApoB and sulcal depth in the lateral occipital cortex among the UESTC, the FPHC, and the total sample groups, and negative correlations were observed between sulcal depth in the lateral occipital cortex and the scores of the Shape Trails Test Part A and B. In the FPHC group, the scores of the Montreal Cognitive Assessment Basic, delayed recall of the Auditory Verbal Learning Test, Animal Fluency Test and Boston Naming Test were positively correlated with the sulcal depth.

Conclusions

ApoB is associated with the sulcal depth in the lateral occipital cortex, potentially relating to speed/executive function in individuals with memory complaints.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods

BACKGROUND

The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods.

METHODS

Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set.

RESULTS

SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set.

CONCLUSION

SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.

On presentation of the human cerebral sulci from inside of the cerebrum

The human cerebral cortex is highly convoluted forming patterns of gyri separated by sulci. The cerebral sulci and gyri are fundamental in cortical anatomy as well as neuroimage processing and analysis. Narrow and deep cerebral sulci are not fully discernible either on the cortical or white matter surface. To cope with this limitation, I propose a new sulci presentation method that employs the inner cortical surface for sulci examination from the inside of the cerebrum. The method has four steps, construct the cortical surface, segment and label the sulci, dissect (open) the cortical surface, and explore the fully exposed sulci from the inside. The inside sulcal maps are created for the left and right lateral, left and right medial, and basal hemispheric surfaces with the sulci parcellated by color and labeled. These three-dimensional sulcal maps presented here are probably the first of this kind created. The proposed method demonstrates the full course and depths of sulci, including narrow, deep, and/or convoluted sulci, which has an educational value and facilitates their quantification. In particular, it provides a straightforward identification of sulcal pits which are valuable markers in studying neurologic disorders. It enhances the visibility of sulci variations by exposing branches, segments, and inter-sulcal continuity. The inside view also clearly demonstrates the sulcal wall skewness along with its variability and enables its assessment. Lastly, this method exposes the sulcal 3-hinges introduced here.

Systematic cortical thickness and curvature patterns in primates

Humans are known to have significant and consistent differences in thickness throughout the cortex, with thick outer gyral folds and thin inner sulcal folds. Our previous work has suggested a mechanical basis for this thickness pattern, with the forces generated during cortical folding leading to thick gyri and thin sulci, and shown that cortical thickness varies along a gyral-sulcal spectrum in humans. While other primate species are expected to exhibit similar patterns of cortical thickness, it is currently unknown how these patterns scale across different sizes, forms, and foldedness. Among primates, brains vary enormously from roughly the size of a grape to the size of a grapefruit, and from nearly smooth to dramatically folded; of these, human brains are the largest and most folded. These variations in size and form make comparative neuroanatomy a rich resource for investigating common trends that transcend differences between species. In this study, we examine 12 primate species in order to cover a wide range of sizes and forms, and investigate the scaling of their cortical thickness relative to the surface geometry. The 12 species were selected due to the public availability of either reconstructed surfaces and/or population templates. After obtaining or reconstructing 3D surfaces from publicly available neuroimaging data, we used our surface-based computational pipeline (https://github.com/mholla/curveball) to analyze patterns of cortical thickness and folding with respect to size (total surface area), geometry (i.e. curvature, shape, and sulcal depth), and foldedness (gyrification). In all 12 species, we found consistent cortical thickness variations along a gyral-sulcal spectrum, with convex shapes thicker than concave shapes and saddle shapes in between. Furthermore, we saw an increasing thickness difference between gyri and sulci as brain size increases. Our results suggest a systematic folding mechanism relating local cortical thickness to geometry. Finally, all of our reconstructed surfaces and morphometry data are available for future research in comparative neuroanatomy.

The complex interplay of hypoxia and sleep disturbance in gray matter structure alterations in obstructive sleep apnea patients

Background

Obstructive Sleep Apnea (OSA) characteristically leads to nocturnal hypoxia and sleep disturbance. Despite clear evidence of OSA-induced cognitive impairments, the literature offers no consensus on the relationship between these pathophysiological processes and brain structure alterations in patients.

Objective

This study leverages the robust technique of structural equation modeling to investigate how hypoxia and sleep disturbance exert differential effects on gray matter structures.

Methods

Seventy-four Male participants were recruited to undergo overnight polysomnography and T1-weighted Magnetic Resonance Imaging. Four structural outcome parameters were extracted, namely, gray matter volume, cortical thickness, sulcal depth, and fractal dimension. Structural equation models were constructed with two latent variables (hypoxia, and sleep disturbance) and three covariates (age, body mass index, and education) to examine the association between gray matter structural changes in OSA and the two latent variables, hypoxia and sleep disturbance.

Results

The structural equation models revealed hypoxia-associated changes in diverse regions, most significantly in increased gray matter volume, cortical thickness and sulcal depth. In contrast, sleep disturbance. Was shown to be largely associated with reduce gray matter volume and sulcal depth.

Conclusion

This study provides new evidence showing significant effects of OSA-induced hypoxia and sleep disturbance on gray matter volume and morphology in male patients with obstructive sleep apnea. It also demonstrates the utility of robust structural equation models in examining obstructive sleep apnea pathophysiology.

Sensory system-specific associations between brain structure and balance

Nearly 75% of older adults in the US report balance problems. Although it is known that aging results in widespread brain atrophy, less is known about how brain structure relates to balance in aging. We collected T1- and diffusion-weighted MRI scans and measured postural sway of 36 young (18-34 years) and 22 older (66-84 years) adults during eyes open, eyes closed, eyes open-foam, and eyes closed-foam conditions. We calculated summary measures indicating visual, proprioceptive, and vestibular contributions to balance. Across both age groups, thinner cortex in multisensory integration regions was associated with greater reliance on visual inputs for balance. Greater gyrification within sensorimotor and parietal cortices was associated with greater reliance on proprioceptive inputs. Poorer vestibular function was correlated with thinner vestibular cortex, greater gyrification within sensorimotor, parietal, and frontal cortices, and lower free water-corrected axial diffusivity across the corona radiata and corpus callosum. These results expand scientific understanding of how individual differences in brain structure relate to balance and have implications for developing brain stimulation interventions to improve balance.

Gyral peaks: Novel gyral landmarks in developing macaque brains

Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.

Cortical abnormalities in patients with fibromyalgia: a pilot study of surface-based morphometry analysis

BACKGROUND

Although neuroanatomical studies correlated to fibromyalgia (FM) are gaining increasing interest, the cortical morphology of patients are largely unknown, and data on cortical gyrification are scarce. The objective of the present study is to assess the cortical morphology in female patients with FM compared with healthy controls (HC) using surface-based morphometry (SBM) analysis of magnetic resonance imaging (MRI).

METHODS

T1-MRIs and clinical data of 20 FM patients and 20 HC subjects were obtained from a public databset via OpenNeuro. For each subject, surface parameters including cortical thickness, local gyrification index (LGI), sulcal depth, and fractal dimensionality were estimated using SBM analysis. These data were compared between two groups controlled by age. The correlations between regional SBM parameters showing group differences and clinical profiles were analyzed.

RESULTS

Compared with HC subjects, FM patients showed reduced cortical thickness in right primary motor cortex, lower LGI in right rostral anterior cingulate and higher sulcal depth in right precuneus (p < 0.05 cluster level family- wise error corrected). In FM patients, correlation analysis showed that the cortical thickness in right primary motor cortex were inversely correlated with scores of pain catastrophizing scale (r = -0.498, p = 0.030) and pain self-perception scale (r = -0.527, p = 0.020), and disease duration (r = -0.488, p = 0.034), respectively.

CONCLUSIONS

Our findings provide evidence of neuroanatomical aberrations in FM patients, which may provide insight into the neuropathology of FM.

In Utero MRI Identifies Impaired Second Trimester Subplate Growth in Fetuses with Congenital Heart Disease

The subplate is a transient brain structure which plays a key role in the maturation of the cerebral cortex. Altered brain growth and cortical development have been suggested in fetuses with complex congenital heart disease (CHD) in the third trimester. However, at an earlier gestation, the putative role of the subplate in altered brain development in CHD fetuses is poorly understood. This study aims to examine subplate growth (i.e., volume and thickness) and its relationship to cortical sulcal development in CHD fetuses compared with healthy fetuses by using 3D reconstructed fetal magnetic resonance imaging. We studied 260 fetuses, including 100 CHD fetuses (22.3-32 gestational weeks) and 160 healthy fetuses (19.6-31.9 gestational weeks). Compared with healthy fetuses, CHD fetuses had 1) decreased global and regional subplate volumes and 2) decreased subplate thickness in the right hemisphere overall, in frontal and temporal lobes, and insula. Compared with fetuses with two-ventricle CHD, those with single-ventricle CHD had reduced subplate volume and thickness in right occipital and temporal lobes. Finally, impaired subplate growth was associated with disturbances in cortical sulcal development in CHD fetuses. These findings suggested a potential mechanistic pathway and early biomarker for the third-trimester failure of brain development in fetuses with complex CHD.

SIGNIFICANCE STATEMENT

Our findings provide an early biomarker for brain maturational failure in fetuses with congenital heart disease, which may guide the development of future prenatal interventions aimed at reducing neurological compromise of prenatal origin in this high-risk population.

Early alterations in cortical morphology after neoadjuvant chemotherapy in breast cancer patients: A longitudinal magnetic resonance imaging study

There is growing evidence that chemotherapy may have a significant impact on the brains of breast cancer patients, causing changes in cortical morphology. However, early morphological alterations induced by chemotherapy in breast cancer patients are unclear. To investigate the patterns of those alterations, we compared female breast cancer patients (n = 45) longitudinally before (time point 0, TP0) and after (time point 1, TP1) the first cycle of neoadjuvant chemotherapy, using voxel‐based morphometry (VBM) and surface‐based morphometry (SBM). VBM and SBM alteration data underwent correlation analysis. We also compared cognition‐related neuropsychological tests in the breast cancer patients between TP0 and TP1. Reductions in gray matter volume, cortical thickness, sulcal depth, and gyrification index were found in most brain areas, while increments were found to be mainly concentrated in and around the hippocampus. Reductions of fractal dimension mainly occurred in the limbic and occipital lobes, while increments mainly occurred in the anterior and posterior central gyrus. Significant correlations were found between altered VBM and altered SBM mainly in the bilateral superior frontal gyrus. We found no significant differences in the cognition‐related neuropsychological tests before and after chemotherapy. The altered brain regions are in line with those associated with impaired cognitive domains in previous studies. We conclude that breast cancer patients showed widespread morphological alterations soon after neoadjuvant chemotherapy, despite an absence of cognitive impairments. The affected brain regions may indicate major targets of early brain damage after chemotherapy.

Cortical thickness systematically varies with curvature and depth in healthy human brains

Cortical thickness varies throughout the cortex in a systematic way. However, it is challenging to investigate the patterns of cortical thickness due to the intricate geometry of the cortex. The cortex has a folded nature both in radial and tangential directions which forms not only gyri and sulci but also tangential folds and intersections. In this article, cortical curvature and depth are used to characterize the spatial distribution of the cortical thickness with much higher resolution than conventional regional atlases. To do this, a computational pipeline was developed that is capable of calculating a variety of quantitative measures such as surface area, cortical thickness, curvature (mean curvature, Gaussian curvature, shape index, intrinsic curvature index, and folding index), and sulcal depth. By analyzing 501 neurotypical adult human subjects from the ABIDE-I dataset, we show that cortex has a very organized structure and cortical thickness is strongly correlated with local shape. Our results indicate that cortical thickness consistently increases along the gyral-sulcal spectrum from concave to convex shape, encompassing the saddle shape along the way. Additionally, tangential folds influence cortical thickness in a similar way as gyral and sulcal folds; outer folds are consistently thicker than inner.

Machine Learning Models for Diagnosis of Parkinson's Disease Using Multiple Structural Magnetic Resonance Imaging Features

Purpose

This study aimed to develop machine learning models for the diagnosis of Parkinson's disease (PD) using multiple structural magnetic resonance imaging (MRI) features and validate their performance.

Methods

Brain structural MRI scans of 60 patients with PD and 56 normal controls (NCs) were enrolled as development dataset and 69 patients with PD and 71 NCs from Parkinson's Progression Markers Initiative (PPMI) dataset as independent test dataset. First, multiple structural MRI features were extracted from cerebellar, subcortical, and cortical regions of the brain. Then, the Pearson's correlation test and least absolute shrinkage and selection operator (LASSO) regression were used to select the most discriminating features. Finally, using logistic regression (LR) classifier with the 5-fold cross-validation scheme in the development dataset, the cerebellar, subcortical, cortical, and a combined model based on all features were constructed separately. The diagnostic performance and clinical net benefit of each model were evaluated with the receiver operating characteristic (ROC) analysis and the decision curve analysis (DCA) in both datasets.

Results

After feature selection, 5 cerebellar (absolute value of left lobule crus II cortical thickness (CT) and right lobule IV volume, relative value of right lobule VIIIA CT and lobule VI/VIIIA gray matter volume), 3 subcortical (asymmetry index of caudate volume, relative value of left caudate volume, and absolute value of right lateral ventricle), and 4 cortical features (local gyrification index of right anterior circular insular sulcus and anterior agranular insula complex, local fractal dimension of right middle insular area, and CT of left supplementary and cingulate eye field) were selected as the most distinguishing features. The area under the curve (AUC) values of the cerebellar, subcortical, cortical, and combined models were 0.679, 0.555, 0.767, and 0.781, respectively, for the development dataset and 0.646, 0.632, 0.690, and 0.756, respectively, for the independent test dataset, respectively. The combined model showed higher performance than the other models (Delong's test, all p-values < 0.05). All models showed good calibration, and the DCA demonstrated that the combined model has a higher net benefit than other models.

Conclusion

The combined model showed favorable diagnostic performance and clinical net benefit and had the potential to be used as a non-invasive method for the diagnosis of PD.

Association of Elevated Maternal Psychological Distress, Altered Fetal Brain, and Offspring Cognitive and Social-Emotional Outcomes at 18 Months

Importance

Prenatal maternal psychological distress is associated with disturbances in fetal brain development. However, the association between altered fetal brain development, prenatal maternal psychological distress, and long-term neurodevelopmental outcomes is unknown.

Objective

To determine the association of fetal brain development using 3-dimensional magnetic resonance imaging (MRI) volumes, cortical folding, and metabolites in the setting of maternal psychological distress with infant 18-month neurodevelopment.

Design, Setting, and Participants

Healthy mother-infant dyads were prospectively recruited into a longitudinal observational cohort study from January 2016 to October 2020 at Children's National Hospital in Washington, DC. Data analysis was performed from January 2016 to July 2021.

Exposures

Prenatal maternal stress, anxiety, and depression.

Main Outcomes and Measures

Prenatal maternal stress, anxiety, and depression were measured using validated self-report questionnaires. Fetal brain volumes and cortical folding were measured from 3-dimensional, reconstructed T2-weighted MRI scans. Fetal brain creatine and choline were quantified using proton magnetic resonance spectroscopy. Infant neurodevelopment at 18 months was measured using Bayley Scales of Infant and Toddler Development III and Infant-Toddler Social and Emotional Assessment. The parenting stress in the parent-child dyad was measured using the Parenting Stress Index-Short Form at 18-month testing.

Results

The cohort consisted of 97 mother-infant dyads (mean [SD] maternal age, 34.79 [5.64] years) who underwent 184 fetal MRI visits (87 participants with 2 fetal studies each) with maternal psychological distress measures between 24 and 40 gestational weeks and completed follow-up infant neurodevelopmental testing. Prenatal maternal stress was negatively associated with infant cognitive performance (β = -0.51; 95% CI, -0.92 to -0.09; P = .01), and this association was mediated by fetal left hippocampal volume. In addition, prenatal maternal anxiety, stress, and depression were positively associated with all parenting stress measures at 18-month testing. Finally, fetal cortical local gyrification index and sulcal depth were negatively associated with infant social-emotional performance (local gyrification index: β = -54.62; 95% CI, -85.05 to -24.19; P < .001; sulcal depth: β = -14.22; 95% CI, -23.59 to -4.85; P = .002) and competence scores (local gyrification index: β = -24.01; 95% CI, -40.34 to -7.69; P = .003; sulcal depth: β = -7.53; 95% CI, -11.73 to -3.32; P < .001).

Conclusions and Relevance

In this cohort study of 97 mother-infant dyads, fetal cortical local gyrification index and sulcal depth were associated with infant 18-month social-emotional and competence outcomes, and fetal left hippocampal volume mediated the association between prenatal maternal stress and infant cognitive outcome. These findings suggest that altered prenatal brain development in the setting of elevated maternal distress has adverse infant sociocognitive outcomes, and identifying early biomarkers associated with long-term neurodevelopment may assist in early targeted interventions.

Differential Relationships Between Brain Structure and Dual Task Walking in Young and Older Adults

Almost 25% of all older adults experience difficulty walking. Mobility difficulties for older adults are more pronounced when they perform a simultaneous cognitive task while walking (i.e., dual task walking). Although it is known that aging results in widespread brain atrophy, few studies have integrated across more than one neuroimaging modality to comprehensively examine the structural neural correlates that may underlie dual task walking in older age. We collected spatiotemporal gait data during single and dual task walking for 37 young (18-34 years) and 23 older adults (66-86 years). We also collected T 1-weighted and diffusion-weighted MRI scans to determine how brain structure differs in older age and relates to dual task walking. We addressed two aims: (1) to characterize age differences in brain structure across a range of metrics including volumetric, surface, and white matter microstructure; and (2) to test for age group differences in the relationship between brain structure and the dual task cost (DTcost) of gait speed and variability. Key findings included widespread brain atrophy for the older adults, with the most pronounced age differences in brain regions related to sensorimotor processing. We also found multiple associations between regional brain atrophy and greater DTcost of gait speed and variability for the older adults. The older adults showed a relationship of both thinner temporal cortex and shallower sulcal depth in the frontal, sensorimotor, and parietal cortices with greater DTcost of gait. Additionally, the older adults showed a relationship of ventricular volume and superior longitudinal fasciculus free-water corrected axial and radial diffusivity with greater DTcost of gait. These relationships were not present for the young adults. Stepwise multiple regression found sulcal depth in the left precentral gyrus, axial diffusivity in the superior longitudinal fasciculus, and sex to best predict DTcost of gait speed, and cortical thickness in the superior temporal gyrus to best predict DTcost of gait variability for older adults. These results contribute to scientific understanding of how individual variations in brain structure are associated with mobility function in aging. This has implications for uncovering mechanisms of brain aging and for identifying target regions for mobility interventions for aging populations.

Brain Surface Area Alterations Correlate With Gait Impairments in Parkinson’s Disease

Parkinson’s disease (PD) is a common neurodegenerative disease with progressive gait, cognition, and overall functional decline. Surface area changes are frequently seen with aging. In neurodegenerative diseases, the changes can be evident with disease progression. The current study aimed to study the regional microstructural alterations using surface-based morphometry to correlate with gait measures of the pace and rhythm domains in PD patients. We hypothesize that specific regional surface changes can be associated with PD gait impairments. Surface analysis might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait in PD, and potentially could serve as an imaging marker in conjunction with other imaging markers. Twenty-seven PD patients and 37 healthy controls were included. The clinical assessment included Mini-Mental State Exanimation, PD motor assessment, clinical gait testing, and objective/quantitative gait assessment. For patients with PD, all motor and gait testing were performed during both OFF and ON medication states. Three Tesla MRI and high-resolution 3D structural images were acquired with an MP-RAGE pulse sequence. Structural image data preprocessing was performed using the DPABISurf toolbox. Clinical characteristics between PD and control group were compared, and correlation between the surface area and behavioral data were analyzed. At the left lateral temporal cortex (LTC) and right inferior parietal cortex (IPC), PD patients have significantly larger surface areas when compared to controls (P &lt; 0.05) using surface-based morphometry. The surface area changes of the left LTC and right IPC were associated with the worse performance of gait assessed by Berg Balance Scale and Timed Up and Go during OFF (P &lt; 0.01). The left LTC area changes significantly correlated with the number of steps, velocity, and the stride length of the pace domain in the ON state. Our findings suggest that PD is associated with a characteristic regional pattern of larger surface area in the left LTC and right IPC. These regional changes were associated with the pace domain of the gait in the ON state. Overall, surface-based analyses might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait in PD, and potentially could serve as an imaging marker.

Cortical development coupling between surface area and sulcal depth on macaque brains

Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.

Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain

We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.

Patterns of Sulcal depth and cortical thickness in Parkinson's disease

Previous voxel-based morphometry (VBM) and cortical thickness (CT) studies on Parkinson's disease (PD) have mainly reported the gray matter size reduction, whereas the shape of cortical surface can also change in PD patients. For the first time, we analyzed sulcal depth (SD) patterns in PD patients by using whole brain region of interest (ROI)-based approach. In a cross-sectional study, high-resolution brain structural MRI images were collected from 60 PD patients without dementia and 56 age-and sex-matched healthy controls (HC). SD and CT were estimated using the Computational Anatomy Toolbox (CAT12) and statistically compared between groups on whole brain ROI-based level using statistical parametric mapping 12 (SPM12). Additionally, correlations between regional brain changes and clinical variables were also examined. Compared to HC, PD patients showed lower SD in widespread regions, including temporal (the bilateral transverse temporal, the left inferior temporal, the right middle temporal and the right superior temporal), insular (the left insula), frontal (the left pars triangularis, the left pars opercularis and the left precentral), parietal (the bilateral superior parietal) and occipital (the right cuneus) regions. For CT, only the left pars opercularis showed lower CT in PD patients compared to HC. No regions showed higher SD or CT in PD patients compared to HC. In PD patients, a significant positive correlation was found between SD of the left pars opercularis and MMSE scores, such that lower MMSE scores were related to lower SD of the left pars opercularis. Our results of widespread lower SD, but relatively localized lower CT, indicate that SD seems to be more sensitive to brain changes than CT and may be mainly affected by white matter damage. Hence, SD may be a more promising indicator to investigate the surface shape changes in PD patients. The significant positive correlation between SD of the left pars opercularis and MMSE scores suggests that SD may be prognostic of future cognitive decline.

The influence of biophysical parameters in a biomechanical model of cortical folding patterns

Abnormal cortical folding patterns, such as lissencephaly, pachygyria and polymicrogyria malformations, may be related to neurodevelopmental disorders. In this context, computational modeling is a powerful tool to provide a better understanding of the early brain folding process. Recent studies based on biomechanical modeling have shown that mechanical forces play a crucial role in the formation of cortical convolutions. However, the effect of biophysical parameters in these models remain unclear. In this paper, we investigate the effect of the cortical growth, the initial geometry and the initial cortical thickness on folding patterns. In addition, we not only use several descriptors of the folds such as the dimensionless mean curvature, the surface-based three-dimensional gyrification index and the sulcal depth, but also propose a new metric to quantify the folds orientation. The results demonstrate that the cortical growth mode does almost not affect the complexity degree of surface morphology; the variation in the initial geometry changes the folds orientation and depth, and in particular, the slenderer the shape is, the more folds along its longest axis could be seen and the deeper the sulci become. Moreover, the thinner the initial cortical thickness is, the higher the spatial frequency of the folds is, but the shallower the sulci become, which is in agreement with the previously reported effects of cortical thickness.

Altered cortical morphology in major depression disorder patients with suicidality

Background

Major depressive disorder (MDD) is associated with high risk of suicide, but the biological underpinnings of suicidality in MDD patients are far from conclusive. Previous neuroimaging studies using voxel-based morphometry (VBM) demonstrated that depressed individuals with suicidal thoughts or behaviors exhibit specific cortical structure alterations. To complement VBM findings, surface-based morphometry (SBM) can provide more details into gray matter structure, including the cortical complexity, cortical thickness and sulcal depth for brain images.

Objective

This study aims to use SBM to investigate cortical morphology alterations to obtain evidence for neuroanatomical alterations in depressed patients with suicidality.

Methods

Here, 3D T1-weighted MR images of brain from 39 healthy controls, 40 depressed patients without suicidality (patient controls), and 39 with suicidality (suicidal groups) were analyzed based on SBM to estimate the fractal dimension, gyrification index, sulcal depth, and cortical thickness using the Computational Anatomy Toolbox. Correlation analyses were performed between clinical data and cortical surface measurements from patients.

Results

Surface-based morphometry showed decreased sulcal depth in the parietal, frontal, limbic, occipital and temporal regions and decreased fractal dimension in the frontal regions in depressed patients with suicidality compared to both healthy and patient controls. Additionally, in patients with depression, the sulcal depth of the left caudal anterior cingulate cortex was negatively correlated with Hamilton Depression Rating Scale scores.

Conclusions

Depressed patients with suicidality had abnormal cortical morphology in some brain regions within the default mode network, frontolimbic circuitry and temporal regions. These structural deficits may be associated with the dysfunction of emotional processing and impulsivity control. This study provides insights into the underlying neurobiology of the suicidal brain.

Abnormal Topological Organization of Sulcal Depth-Based Structural Covariance Networks in Parkinson's Disease

Recent research on Parkinson's disease (PD) has demonstrated the topological abnormalities of structural covariance networks (SCNs) using various morphometric features from structural magnetic resonance images (sMRI). However, the sulcal depth (SD)-based SCNs have not been investigated. In this study, we used SD to investigate the topological alterations of SCNs in 60 PD patients and 56 age- and gender-matched healthy controls (HC). SCNs were constructed by thresholding SD correlation matrices of 68 regions and analyzed using graph theoretical approaches. Compared with HC, PD patients showed increased normalized clustering coefficient and normalized path length, as well as a reorganization of degree-based and betweenness-based hubs (i.e., less frontal hubs). Moreover, the degree distribution analysis showed more high-degree nodes in PD patients. In addition, we also found the increased assortativity and reduced robustness under a random attack in PD patients compared to HC. Taken together, these findings indicated an abnormal topological organization of SD-based SCNs in PD patients, which may contribute in understanding the pathophysiology of PD at the network level.

Regional Alterations in Cortical Sulcal Depth in Living Fetuses with Down Syndrome

Down syndrome (DS) is the most common genetic cause of developmental disabilities. Advanced analysis of brain magnetic resonance imaging (MRI) has been used to find brain abnormalities and their relationship to neurocognitive impairments in children and adolescents with DS. Because genetic factors affect brain development in early fetal life, there is a growing interest in analyzing brains from living fetuses with DS. In this study, we investigated regional sulcal folding depth as well as global cortical gyrification from fetal brain MRIs. Nine fetuses with DS (29.1 ± 4.24 gestational weeks [mean ± standard deviation]) were compared with 17 typically developing [TD] fetuses (28.4 ± 3.44). Fetuses with DS showed lower whole-brain average sulcal depths and gyrification index than TD fetuses. Significant decreases in sulcal depth were found in bilateral Sylvian fissures and right central and parieto-occipital sulci. On the other hand, significantly increased sulcal depth was shown in the left superior temporal sulcus, which is related to atypical hemispheric asymmetry of cortical folding. Moreover, these group differences increased as gestation progressed. This study demonstrates that regional sulcal depth is a sensitive marker for detecting alterations of cortical development in DS during fetal life, which may be associated with later neurocognitive impairment.

Cortical Morphological Changes in Congenital Amusia: Surface-Based Analyses

Background: Congenital amusia (CA) is a rare disorder characterized by deficits in pitch perception, and many structural and functional magnetic resonance imaging studies have been conducted to better understand its neural bases. However, a structural magnetic resonance imaging analysis using a surface-based morphology method to identify regions with cortical features abnormalities at the vertex-based level has not yet been performed. Methods: Fifteen participants with CA and 13 healthy controls underwent structural magnetic resonance imaging. A surface-based morphology method was used to identify anatomical abnormalities. Then, the surface parameters' mean value of the identified clusters with statistically significant between-group differences were extracted and compared. Finally, Pearson's correlation analysis was used to assess the correlation between the Montreal Battery of Evaluation of Amusia (MBEA) scores and surface parameters. Results: The CA group had significantly lower MBEA scores than the healthy controls (p = 0.000). The CA group exhibited a significant higher fractal dimension in the right caudal middle frontal gyrus and a lower sulcal depth in the right pars triangularis gyrus (p < 0.05; false discovery rate-corrected at the cluster level) compared to healthy controls. There were negative correlations between the mean fractal dimension values in the right caudal middle frontal gyrus and MBEA score, including the mean MBEA score (r = -0.5398, p = 0.0030), scale score (r = -0.5712, p = 0.0015), contour score (r = -0.4662, p = 0.0124), interval score (r = -0.4564, p = 0.0146), rhythmic score (r = -0.5133, p = 0.0052), meter score (r = -0.3937, p = 0.0382), and memory score (r = -0.3879, p = 0.0414). There was a significant positive correlation between the mean sulcal depth in the right pars triangularis gyrus and the MBEA score, including the mean score (r = 0.5130, p = 0.0052), scale score (r = 0.5328, p = 0.0035), interval score (r = 0.4059, p = 0.0321), rhythmic score (r = 0.5733, p = 0.0014), meter score (r = 0.5061, p = 0.0060), and memory score (r = 0.4001, p = 0.0349). Conclusion: Individuals with CA exhibit cortical morphological changes in the right hemisphere. These findings may indicate that the neural basis of speech perception and memory impairments in individuals with CA is associated with abnormalities in the right pars triangularis gyrus and middle frontal gyrus, and that these cortical abnormalities may be a neural marker of CA.

Temporal Patterns of Emergence and Spatial Distribution of Sulcal Pits During Fetal Life

Sulcal pits are thought to represent the first cortical folds of primary sulci during neurodevelopment. The uniform spatial distribution of sulcal pits across individuals is hypothesized to be predetermined by a human-specific protomap which is related to functional localization under genetic controls in early fetal life. Thus, it is important to characterize temporal and spatial patterns of sulcal pits in the fetal brain that would provide additional information of functional development of the human brain and crucial insights into abnormal cortical maturation. In this paper, we investigated temporal patterns of emergence and spatial distribution of sulcal pits using 48 typically developing fetal brains in the second half of gestation. We found that the position and spatial variance of sulcal pits in the fetal brain are similar to those in the adult brain, and they are also temporally uniform against dynamic brain growth during fetal life. Furthermore, timing of pit emergence shows a regionally diverse pattern that may be associated with the subdivisions of the protomap. Our findings suggest that sulcal pits in the fetal brain are useful anatomical landmarks containing detailed information of functional localization in early cortical development and maintaining their spatial distribution throughout the human lifetime.

Differential longitudinal changes in structural complexity and volumetric measures in community-dwelling older individuals

Fractal geometry provides a method of analyzing natural and especially biological morphologies. To investigate the relationship between the complexity measure, which is indexed as fractal dimensionality (FD), and the traditional Euclidean metrics, such as the volume and thickness, of the brain in older age, we analyzed 483 MRI scans of 161 community-dwelling, nondemented individuals aged 70-90 years at the baseline and their 2-year and 6-year follow-ups. We quantified changes in neuroimaging metrics in cortical lobes and subcortical structures and investigated the effects of age, sex, hemisphere, and education on FD. We also analyzed the mediating effects of these metrics for further investigation. FD showed significant age-related decline in all structures, and its trajectory was best modeled quadratically in the bilateral frontal, parietal, and occipital lobes, as well as in the bilateral caudate, putamen, hippocampus, amygdala, and accumbens. FD showed specific mediating effects on aging and cognitive decline in some regions. Our findings suggest that FD is reliable yet shows a different pattern of decline compared with volumetric measures.

Association of Prenatal Maternal Psychological Distress With Fetal Brain Growth, Metabolism, and Cortical Maturation

IMPORTANCE

Prenatal maternal stress is increasingly associated with adverse outcomes in pregnant women and their offspring. However, the association between maternal stress and human fetal brain growth and metabolism is unknown.

OBJECTIVE

To identify the association between prenatal maternal psychological distress and fetal brain growth, cortical maturation, and biochemical development using advanced 3-dimensional volumetric magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (1H-MRS).

DESIGN, SETTING, AND PARTICIPANTS

This cohort study prospectively recruited pregnant women from low-risk obstetric clinics in Washington, DC, from January 1, 2016, to April 17, 2019. Participants were healthy volunteers with a normal prenatal medical history, no chronic or pregnancy-induced physical or mental illnesses, and normal results on fetal ultrasonography and biometry studies. Fetal brain MRI studies were performed at 2 time points between 24 and 40 weeks' gestation.

EXPOSURES

Prenatal maternal stress, anxiety, and depression.

MAIN OUTCOMES AND MEASURES

Volumes of fetal total brain, cortical gray matter, white matter, deep gray matter, cerebellum, brainstem, and hippocampus were measured from 3-dimensional reconstructed T2-weighted MRI scans. Cortical folding measurements included local gyrification index, sulcal depth, and curvedness. Fetal brain N-acetylaspartate, creatine, and choline levels were quantified using 1H-MRS. Maternal stress, depression, and anxiety were measured with the Perceived Stress Scale (PSS), Edinburgh Postnatal Depression Scale (EPDS), Spielberger State Anxiety Inventory (SSAI), and Spielberger Trait Anxiety Inventory (STAI).

RESULTS

A total of 193 MRI studies were performed in 119 pregnant women (67 [56%] carrying male fetuses and 52 [44%], female fetuses; maternal mean [SD] age, 34.46 [5.95] years) between 24 and 40 gestational weeks. All women were high school graduates, 99 (83%) were college graduates, and 100 (84%) reported professional employment. Thirty-two women (27%) had positive scores for stress, 31 (26%) for anxiety, and 13 (11%) for depression. Maternal trait anxiety was associated with smaller fetal left hippocampal volume (STAI score: -0.002 cm3; 95% CI, -0.003 to -0.0008 cm3; P = .004). Maternal anxiety and stress were associated with increased fetal cortical gyrification in the frontal lobe (β for SSAI score: 0.004 [95% CI, 0.001-0.006; P = .002]; β for STAI score: 0.004 [95% CI, 0.002-0.006; P < .001]; β for PSS score: 0.005 [95% CI, 0.001-0.008; P = .005]) and temporal lobe (β for SSAI score: 0.004 [95% CI, 0.001-0.007; P = .004]; β for STAI score: 0.004 [95% CI, 0.0008-0.006; P = .01]). Elevated maternal depression was associated with decreased creatine (EPDS score: -0.04; 95% CI, -0.06 to -0.02; P = .005) and choline (EPDS score: -0.03; 95% CI, -0.05 to -0.01; P = .02) levels in the fetal brain.

CONCLUSIONS AND RELEVANCE

This study found that the prevalence of maternal psychological distress in healthy, well-educated, and employed pregnant women was high, underappreciated, and associated with impaired fetal brain biochemistry and hippocampal growth as well as accelerated cortical folding. These findings appear to support the need for routine mental health surveillance for all pregnant women and targeted interventions in women with elevated psychological distress.

Genetic Influence on the Sulcal Pits: On the Origin of the First Cortical Folds

The influence of genes on cortical structures has been assessed through various phenotypes. The sulcal pits, which are the putative first cortical folds, have for long been assumed to be under tight genetic control, but this was never quantified. We estimated the pit depth heritability in various brain regions using the high quality and large sample size of the Human Connectome Project pedigree cohort. Analysis of additive genetic variance indicated that their heritability ranges between 0.2 and 0.5 and displays a regional genetic control with an overall symmetric pattern between hemispheres. However, a noticeable asymmetry of heritability estimates is observed in the superior temporal sulcus and could thus be related to language lateralization. The heritability range estimated in this study reinforces the idea that cortical shape is determined primarily by nongenetic factors, which is consistent with the important increase of cortical folding from birth to adult life and thus predominantly constrained by environmental factors. Nevertheless, the genetic cues, implicated with various local levels of heritability in the formation of sulcal pits, play a fundamental role in the normal gyral pattern development. Quantifying their influence and identifying the underlying genetic variants would provide insight into neurodevelopmental disorders.

Robust estimation of sulcal morphology

While it is well established that cortical morphology differs in relation to a variety of inter-individual factors, it is often characterized using estimates of volume, thickness, surface area, or gyrification. Here we developed a computational approach for estimating sulcal width and depth that relies on cortical surface reconstructions output by FreeSurfer. While other approaches for estimating sulcal morphology exist, studies often require the use of multiple brain morphology programs that have been shown to differ in their approaches to localize sulcal landmarks, yielding morphological estimates based on inconsistent boundaries. To demonstrate the approach, sulcal morphology was estimated in three large sample of adults across the lifespan, in relation to aging. A fourth sample is additionally used to estimate test–retest reliability of the approach. This toolbox is now made freely available as supplemental to this paper: https://cmadan.github.io/calcSulc/.

Constructing Statistically Unbiased Cortical Surface Templates Using Feature-Space Covariance

The choice of surface template plays an important role in cross-sectional subject analyses involving cortical brain surfaces because there is a tendency toward registration bias given variations in inter-individual and inter-group sulcal and gyral patterns. In order to account for the bias and spatial smoothing, we propose a feature-based unbiased average template surface. In contrast to prior approaches, we factor in the sample population covariance and assign weights based on feature information to minimize the influence of covariance in the sampled population. The mean surface is computed by applying the weights obtained from an inverse covariance matrix, which guarantees that multiple representations from similar groups (e.g., involving imaging, demographic, diagnosis information) are down-weighted to yield an unbiased mean in feature space. Results are validated by applying this approach in two different applications. For evaluation, the proposed unbiased weighted surface mean is compared with un-weighted means both qualitatively and quantitatively (mean squared error and absolute relative distance of both the means with baseline). In first application, we validated the stability of the proposed optimal mean on a scan-rescan reproducibility dataset by incrementally adding duplicate subjects. In the second application, we used clinical research data to evaluate the difference between the weighted and unweighted mean when different number of subjects were included in control versus schizophrenia groups. In both cases, the proposed method achieved greater stability that indicated reduced impacts of sampling bias. The weighted mean is built based on covariance information in feature space as opposed to spatial location, thus making this a generic approach to be applicable to any feature of interest.

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer's disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, 'shape connections' between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus.

Sulcal Depth-based Cortical Shape Analysis in Normal Healthy Control and Schizophrenia Groups

Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region-focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface. However, taking the average sulcal depth over the full ROI blurs overall sulcal depth measurements which may result in reduced sensitivity to detect sulcal depth changes in neurological and psychiatric disorders. To overcome such a blurring effect, we focus on sulcal fundic regions in each ROI by filtering out other gyral regions. Consequently, the proposed method results in more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis to sulcal depth reduction in schizophrenia with a comparison to the normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia; sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p < 0.05).