Local cortical surface complexity maps from spherical harmonic reconstructions

Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging

BACKGROUND AND OBJECTIVE

Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD.

METHODS

This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions.

RESULTS

The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations.

CONCLUSION

Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.

Analyzing fractal dimension in electroconvulsive therapy: Unraveling complexity in structural and functional neuroimaging

BACKGROUND

Numerous studies show that electroconvulsive therapy (ECT) induces hippocampal neuroplasticity, but findings are inconsistent regarding its clinical relevance. This study aims to investigate ECT-induced plasticity of anterior and posterior hippocampi using mathematical complexity measures in neuroimaging, namely Higuchi's fractal dimension (HFD) for fMRI time series and the fractal dimension of cortical morphology (FD-CM). Furthermore, we explore the potential of these complexity measures to predict ECT treatment response.

METHODS

Twenty patients with a current depressive episode (16 with major depressive disorder and 4 with bipolar disorder) underwent MRI-scans before and after an ECT-series. Twenty healthy controls matched for age and sex were also scanned twice for comparison purposes. Resting-state fMRI data were processed, and HFD was computed for anterior and posterior hippocampi. Group-by-time effects for HFD in anterior and posterior hippocampi were calculated and correlations between HFD changes and improvement in depression severity were examined. For FD-CM analyses, we preprocessed structural MRI with CAT12's surface-based methods. We explored group-by-time effects for FD-CM and the predictive value of baseline HFD and FD-CM for treatment outcome.

RESULTS

Patients exhibited a significant increase in bilateral hippocampal HFD from baseline to follow-up scans. Right anterior hippocampal HFD increase was associated with reductions in depression severity. We found no group differences and group-by-time effects in FD-CM. After applying a whole-brain regression analysis, we found that baseline FD-CM in the left temporal pole predicted reduction of overall depression severity after ECT. Baseline hippocampal HFD did not predict treatment outcome.

CONCLUSION

This study suggests that HFD and FD-CM are promising imaging markers to investigate ECT-induced neuroplasticity associated with treatment response.

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.

Mapping morphological cortical networks with joint probability distributions from multiple morphological features

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

Androgen deprivation increases frontopolar cortical thickness in prostate cancer patients: an effect of early neurodegeneration?

Androgen deprivation therapy (ADT) has been associated with adverse effects on the brain. ADT leads to altered testosterone levels that may affect brain morphology as well as cognition. Considering the reliability of cortical thickness (CT) as a marker of cognitive and brain changes, e.g., in Alzheimer's disease, we assessed the impacts of ADT on CT and working memory. Thirty men with non-metastatic prostate cancer receiving ADT and 32 patients not receiving ADT (controls or CON), matched in age and years of education, participated in N-back task and quality-of-life (QoL) assessments as well as brain imaging at baseline and prospectively at 6 months. Imaging data were processed with published routines to estimate CT and the results of a group by time flexible factorial analysis were evaluated at a corrected threshold. ADT and CON did not differ in N-back performance or QoL across time points. Relative to CON, patients receiving ADT showed significantly higher frontopolar cortex (FPC) CT at 6-month follow-up vs. baseline. Follow-up vs. baseline FPC CT change correlated negatively with changes in 2-back correct response rate and in testosterone levels across all participants. In mediation analysis, FPC CT change mediated the association between testosterone level change and 2-back accuracy rate change. Increases in FPC CT following 6 months of ADT may reflect early neurodegenerative changes in response to androgen deprivation. While no significant impact on working memory or QoL was observed over 6 months, further research of longer duration of treatment is warranted to unravel the full spectrum of cognitive and neural consequences of ADT in prostate cancer patients.

Addressing the sources of inter-subject variability in E-field parameters in anodal tDCS stimulation over motor cortical network

Objetive: .Although transcranial direct current stimulation constitutes a non-invasive neuromodulation technique with promising results in a great variety of applications, its clinical implementation is compromised by the high inter-subject variability reported. This study aims to analyze the inter-subject variability in electric fields (E-fields) over regions of the cortical motor network under two electrode montages: the classical C3Fp2 and an alternative P3F3, which confines more the E-field over this region.Approach.Computational models of the head of 98 healthy subjects were developed to simulate the E-field under both montages. E-field parameters such as magnitude, focality and orientation were calculated over three regions of interest (ROI): M1S1, supplementary motor area (SMA) and preSMA. The role of anatomical characteristics as a source of inter-subject variability on E-field parameters and individualized stimulation intensity were addressed using linear mixed-effect models.Main results.P3F3 showed a more confined E-field distribution over M1S1 than C3Fp2; the latter elicited higher E-fields over supplementary motor areas. Both montages showed high inter-subject variability, especially for the normal component over C3Fp2. Skin, bone and CSF ROI volumes showed a negative association with E-field magnitude irrespective of montage. Grey matter volume and montage were the main sources of variability for focality. The curvature of gyri was found to be significantly associated with the variability of normal E-fields.Significance.Computational modeling proves useful in the assessment of E-field variability. Our simulations predict significant differences in E-field magnitude and focality for C3Fp2 and P3F3. However, anatomical characteristics were also found to be significant sources of E-field variability irrespective of electrode montage. The normal E-field component better captured the individual variability and low rate of responder subjects observed in experimental studies.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Cortical changes in the brain of patients with hemifacial spasm

OBJECTIVE

Hemifacial spasm (HFS) is a movement disorder characterized by involuntary muscle contractions on one side of the face. It is associated with disturbances in the brain's functional architecture. Despite this, the structural alterations in the brain related to HFS remain poorly understood. In this study, we investigated the cortical morphology changes in patients with HFS compared to healthy controls (HCs).

METHODS

We analyzed 3D T1-weighted MRI images from 33 patients with left-sided primary HFS and 33 age- and sex-matched HCs. Measurements of cortical thickness (CTh), sulcal depth, local gyrification index (lGI), and fractal dimension were taken using a computational anatomy toolbox. A general linear model, accounting for age, gender, and total brain volume, was applied for statistical analyses. Significant clusters were then assessed for correlations with clinical parameters.

RESULTS

The HFS patients displayed several cortical abnormalities when compared to HCs, including reduced CTh in the contralateral precentral gyrus and left orbitofrontal cortex, decreased sulcal depth in the left orbitofrontal cortex, and increased lGI in the right insula and superior temporal cortex. However, fractal dimension did not differ significantly between the groups. Additionally, in HFS patients, a notable negative correlation was found between the sulcal depth in the left orbitofrontal cortex and the Beck Depression Inventory-II scores.

CONCLUSIONS

Our findings reveal that HFS is associated with specific surface-based morphological changes in the brain. These alterations contribute to a deeper understanding of the neurophysiological mechanisms involved in HFS and may have implications for future research and treatment strategies.

Mapping the interplay of atrial fibrillation, brain structure, and cognitive dysfunction

INTRODUCTION

Atrial fibrillation (AF) is associated with an elevated risk of cognitive impairment and dementia. Understanding the cognitive sequelae and brain structural changes associated with AF is vital for addressing ensuing health care needs.

METHODS AND RESULTS

We examined 1335 stroke-free individuals with AF and 2683 matched controls using neuropsychological assessments and multimodal neuroimaging. The analysis revealed that individuals with AF exhibited deficits in executive function, processing speed, and reasoning, accompanied by reduced cortical thickness, elevated extracellular free-water content, and widespread white matter abnormalities, indicative of small vessel pathology. Notably, brain structural differences statistically mediated the relationship between AF and cognitive performance.

DISCUSSION

Integrating a comprehensive analysis approach with extensive clinical and magnetic resonance imaging data, our study highlights small vessel pathology as a possible unifying link among AF, cognitive decline, and abnormal brain structure. These insights can inform diagnostic approaches and motivate the ongoing implementation of effective therapeutic strategies. Highlights We investigated neuropsychological and multimodal neuroimaging data of 1335 individuals with atrial fibrillation (AF) and 2683 matched controls. Our analysis revealed AF-associated deficits in cognitive domains of attention, executive function, processing speed, and reasoning. Cognitive deficits in the AF group were accompanied by structural brain alterations including reduced cortical thickness and gray matter volume, alongside increased extracellular free-water content as well as widespread differences of white matter integrity. Structural brain changes statistically mediated the link between AF and cognitive performance, emphasizing the potential of structural imaging markers as a diagnostic tool in AF-related cognitive decline.

Cortical morphology variations during the menstrual cycle in individuals with and without premenstrual dysphoric disorder

BACKGROUND

Premenstrual dysphoric disorder (PMDD) is hypothesized to stem from maladaptive neural sensitivity to ovarian steroid hormone fluctuations. Recently, we found thinner cortices in individuals with PMDD, compared to healthy controls, during the symptomatic phase. Here, we aimed at investigating whether such differences illustrate state-like characteristics specific to the symptomatic phase, or trait-like features defining PMDD.

METHODS

Patients and controls were scanned using structural magnetic resonance imaging during the mid-follicular and late-luteal phase of the menstrual cycle. Group-by-phase interaction effects on cortical architecture metrics (cortical thickness, gyrification index, cortical complexity, and sulcal depth) were assessed using surface-based morphometry.

RESULTS

Independently of menstrual cycle phase, a main effect of diagnostic group on surface metrics was found, primarily illustrating thinner cortices (0.3 < Cohen's d > 1.1) and lower gyrification indices (0.4 < Cohen's d > 1.0) in patients compared to controls. Furthermore, menstrual cycle-specific effects were detected across all participants, depicting a decrease in cortical thickness (0.4 < Cohen's d > 1.7) and region-dependent changes in cortical folding metrics (0.4 < Cohen's d > 2.2) from the mid-follicular to the late luteal phase.

LIMITATIONS

Small effects (d = 0.3) require a larger sample size to be accurately characterized.

CONCLUSIONS

These findings provide initial evidence of trait-like cortical characteristics of the brain of individuals with premenstrual dysphoric disorder, together with indications of menstrual cycle-related variations in cortical architecture in patients and controls. Further investigations exploring whether these differences constitute stable vulnerability markers or develop over the years may help understand PMDD etiology.

Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment.

METHODS

Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD.

RESULTS

Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606.

CONCLUSIONS

This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

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.

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.

Neural deformation fields for template-based reconstruction of cortical surfaces from MRI

The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods separate the surface registration from the surface extraction, which is computationally inefficient and prone to distortions. We introduce Vox2Cortex-Flow (V2C-Flow), a deep mesh-deformation technique that learns a deformation field from a brain template to the cortical surfaces of an MRI scan. To this end, we present a geometric neural network that models the deformation-describing ordinary differential equation in a continuous manner. The network architecture comprises convolutional and graph-convolutional layers, which allows it to work with images and meshes at the same time. V2C-Flow is not only very fast, requiring less than two seconds to infer all four cortical surfaces, but also establishes vertex-wise correspondences to the template during reconstruction. In addition, V2C-Flow is the first approach for cortex reconstruction that models white matter and pial surfaces jointly, therefore avoiding intersections between them. Our comprehensive experiments on internal and external test data demonstrate that V2C-Flow results in cortical surfaces that are state-of-the-art in terms of accuracy. Moreover, we show that the established correspondences are more consistent than in FreeSurfer and that they can directly be utilized for cortex parcellation and group analyses of cortical thickness.

Sleep and circadian rhythm disruptions in behavioral variant frontotemporal dementia

INTRODUCTION

Sleep and rest-activity rhythm alterations are common in neurodegenerative diseases. However, their characterization in patients with behavioral variant frontotemporal dementia (bvFTD) has proven elusive. We investigated rest-activity rhythm alterations, sleep disturbances, and their neural correlates in bvFTD.

METHODS

Twenty-seven bvFTD patients and 25 healthy controls completed sleep questionnaires and underwent 7 days of actigraphy while concurrently maintaining a sleep diary. Cortical complexity and thickness were calculated from T1-weighted magnetic resonance (MR) images.

RESULTS

Compared to controls, bvFTD patients showed longer time in bed (95% confidence interval [CI]: 79.31, 321.83) and total sleep time (95% CI: 24.38, 321.88), lower sleep efficiency (95% CI: -12.58, -95.54), and rest-activity rhythm alterations in the morning and early afternoon. Increased sleep duration was associated with reduced cortical thickness in frontal regions.

DISCUSSION

Patients with bvFTD showed longer sleep duration, lower sleep quality, and rest-activity rhythm alterations. Actigraphy could serve as a cost-effective and accessible tool for ecologically monitoring changes in sleep duration in bvFTD patients.

HIGHLIGHTS

We assessed sleep and circadian rhythms in behavioral variant frontotemporal dementia (bvFTD) using actigraphy. Patients with bvFTD show increased sleep duration and reduced sleep quality. Patients with bvFTD show rest-activity alterations in the morning and early afternoon. Sleep duration is associated with reduced cortical thickness in frontal regions. These alterations may represent an early sign of neurodegeneration.

Lower fractional dimension in Alzheimer's disease correlates with reduced locus coeruleus signal intensity

This study aimed to determine the pattern of fractional dimension (FD) in Alzheimer's disease (AD) patients, and investigate the relationship between FD and the locus coeruleus (LC) signal intensity.A total of 27 patients with AD and 25 healthy controls (HC) were collected to estimate the pattern of fractional dimension (FD) and cortical thickness (CT) using the Computational Anatomy Toolbox (CAT12), and statistically analyze between groups on a vertex level using statistical parametric mapping 12. In addition, they were examined by neuromelanin sensitive MRI(NM-MRI) technique to calculate the locus coeruleus signal contrast ratios (LC-CRs). Additionally, correlations between the pattern of FD and LC-CRs were further examined.Compared to HC, AD patients showed widespread lower CT and FD Furthermore, significant positive correlation was found between local fractional dimension (LFD) of the left rostral middle frontal cortex and LC-CRs. Results suggest lower cortical LFD is associated with LCCRs that may reflect a reduction due to broader neurodegenerative processes. This finding may highlight the potential utility for advanced measures of cortical complexity in assessing brain health and early identification of neurodegenerative processes.

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.

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.

CAT: a computational anatomy toolbox for the analysis of structural MRI data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Neuroanatomical correlates of aggressiveness: a case-control voxel- and surface-based morphometric study

Aggression occurs across the population ranging on a symptom continuum. Most previous studies have used magnetic resonance imaging in clinical/forensic samples, which is associated with several confounding factors. The present study examined structural brain characteristics in two healthy samples differing only in their propensity for aggressive behavior. Voxel- and surface-based morphometry (SBM) analyses were performed on 29 male martial artists and 32 age-matched male controls. Martial artists had significantly increased mean gray matter volume in two frontal (left superior frontal gyrus and bilateral anterior cingulate cortex) and one parietal (bilateral posterior cingulate gyrus and precuneus) brain clusters compared to controls (whole brain: p < 0.001, cluster level: family-wise error (FWE)-corrected). SBM analyses revealed a trend for greater gyrification indices in martial artists compared to controls in the left lateral orbital frontal cortex and the left pars orbitalis (whole brain: p < 0.001, cluster level: FWE-corrected). The results indicate brain structural differences between martial artists and controls in frontal and parietal brain areas critical for emotion processing/inhibition of emotions as well as empathic processes. The present study highlights the importance of studying healthy subjects with a propensity for aggressive behavior in future structural MRI research on aggression.

Fractal Dimension Studies of the Brain Shape in Aging and Neurodegenerative Diseases

The fractal dimension is a morphometric measure that has been used to investigate the changes of brain shape complexity in aging and neurodegenerative diseases. This chapter reviews fractal dimension studies in aging and neurodegenerative disorders in the literature. Research has shown that the fractal dimension of the left cerebral hemisphere increases until adolescence and then decreases with aging, while the fractal dimension of the right hemisphere continues to increase until adulthood. Studies in neurodegenerative diseases demonstrated a decline in the fractal dimension of the gray matter and white matter in Alzheimer's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia. In multiple sclerosis, the white matter fractal dimension decreases, but conversely, the fractal dimension of the gray matter increases at specific stages of disease. There is also a decline in the gray matter fractal dimension in frontotemporal dementia and multiple system atrophy of the cerebellar type and in the white matter fractal dimension in epilepsy and stroke. Region-specific changes in fractal dimension have also been found in Huntington's disease and Parkinson's disease. Associations were found between the fractal dimension and clinical scores, showing the potential of the fractal dimension as a marker to monitor brain shape changes in normal or pathological processes and predict cognitive or motor function.

Fractal Dimension Analysis in Neurological Disorders: An Overview

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

Fractal Analysis in MATLAB: A Tutorial for Neuroscientists

MATLAB is one of the software platforms most widely used for scientific computation. MATLAB includes a large set of functions, packages, and toolboxes that make it simple and fast to obtain complex mathematical and statistical computations for many applications. In this chapter, we review some tools available in MATLAB for performing fractal analyses on typical neuroscientific data in a practical way. We provide detailed examples of how to calculate the fractal dimension of 1D, 2D, and 3D data in MATLAB. Furthermore, we review other software packages for fractal analysis.

The longitudinal impact of type 2 diabetes on brain gyrification

Type 2 diabetes has an effect on brain structure, including cortical gyrification. The significance of these changes is better understood if assessed over time. However, there is a lack of studies assessing longitudinally the effect of this disease with complex aethology in gyrification. While changes in this feature have been associated mainly with genetic legacy, our study allowed to shed light on the effect of the variation of glycaemic profile over time in gyrification in this metabolic disease. In this longitudinal study, we analysed brain anatomical magnetic resonance images of 15 participants with type 2 diabetes and 13 healthy control participants to investigate the impact of this metabolic disease on the gyrification index over a 7-year period. We observed a significant interaction between time and group in six regions, four of which (left precentral gyrus, left gyrus rectus, left subcentral gyrus and sulci and right inferior temporal gyrus) showed an increase in gyrification in type 2 diabetes and a decrease in the control group and the two others (left pericallosal sulcus and right inferior frontal sulcus) the opposite pattern. The variation of the gyrification was correlated with the variation of the glycaemic profile. Following the interaction, the simple main effect of time in each group separately has shown that in the group with diabetes, there were more regions susceptible to alterations of gyrification. In sum, our results raise credit for the possibility that glycaemic control also might influence gyrification in type 2 diabetes.

Changed cortical thickness and sulcal depth in pediatric acute lymphoblastic leukemia survivors treated with chemotherapy only

The purpose of this study is to observe the changes of cortical morphological characteristics and their potential contribution to cognitive function in ALL survivors by using surface-based morphometry (SBM). Using SBM analysis, we calculated and compared group differences in cortical thickness, sulcal depth, gyrification, and fractal dimension of the cerebral cortex between 18 pediatric ALL survivors treated on chemotherapy-only protocols and off treatment within 2 years, and 18 healthy controls (HCs) with two-sample t-tests [P < 0.05, family-wise error (FWE) corrected]. Relationships between abnormal cortical characteristic values and cognitive function parameters were investigated with partial correlation analysis, taking age as a covariate. We found decreased cortical thickness mainly located in the prefrontal and temporal region, and increased sulcal depth in left rostral middle frontal cortex and left pars orbitalis in the ALL survivors compared to HCs. There were no statistically significant differences in the gyrification and fractal dimension between the two groups. In ALL survivors, cortical thickness and sulcal depth of above areas values revealed no significant correlation with the cognitive function parameters. In conclusion, pediatric ALL survivors show decreased cortical thickness in prefrontal and temporal regions, and increased sulcal depth in prefrontal region. These results suggest that SBM-based approach can be used to assess changes of cortical morphological characteristics in pediatric ALL survivors.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

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.

Pathological Networking of Gray Matter Dendritic Density With Classic Brain Morphometries in OCD

Importance

The pathogenesis of obsessive-compulsive disorder (OCD) may involve altered dendritic morphology, but in vivo imaging of neurite morphology in OCD remains limited. Such changes must be interpreted functionally within the context of the multimodal neuroimaging approach to OCD.

Objective

To examine whether dendritic morphology is altered in patients with OCD compared with healthy controls (HCs) and whether such alterations are associated with other brain structural metrics in pathological networks.

Design, Setting, and Participants

This case-control study used cross-sectional data, including multimodal brain images and clinical symptom assessments, from 108 patients with OCD and 108 HCs from 2014 to 2017. Patients with OCD were recruited from Shanghai Mental Health Center, Shanghai, China, and HCs were recruited via advertisements. The OCD group comprised unmedicated adults with a Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) diagnosis of OCD, while the HCs were adults without any DSM-IV diagnosis, matched for age, sex, and education level. Data were analyzed from September 2019 to April 2023.

Exposure

DSM-IV diagnosis of OCD.

Main Outcomes and Measures

Multimodal brain imaging was used to compare neurite microstructure and classic morphometries between patients with OCD and HCs. The whole brain was searched to identify regions exhibiting altered morphology in patients with OCD and explore the interplay between the brain metrics representing these alterations. Brain-symptom correlations were analyzed, and the performance of different brain metric configurations were evaluated in distinguishing patients with OCD from HCs.

Results

Among 108 HCs (median [IQR] age, 26 [23-31] years; 50 [46%] female) and 108 patients with OCD (median [IQR] age, 26 [24-31] years; 46 [43%] female), patients with OCD exhibited deficient neurite density in the right lateral occipitoparietal regions (peak t = 3.821; P ≤ .04). Classic morphometries also revealed widely-distributed alterations in the brain (peak t = 4.852; maximum P = .04), including the prefrontal, medial parietal, cingulate, and fusiform cortices. These brain metrics were interconnected into a pathological brain network associated with OCD symptoms (global strength: HCs, 0.253; patients with OCD, 0.941; P = .046; structural difference, 0.572; P < .001). Additionally, the neurite density index exhibited high discriminatory power in distinguishing patients with OCD from HCs (accuracy, ≤76.85%), and the entire pathological brain network also exhibited excellent discriminative classification properties (accuracy, ≤82.87%).

Conclusions and Relevance

The findings of this case-control study underscore the utility of in vivo imaging of gray matter dendritic density in future OCD research and the development of neuroimaging-based biomarkers. They also endorse the concept of connectopathy, providing a potential framework for interpreting the associations among various OCD symptom-related morphological anomalies.

Single-subject cortical morphological brain networks across the adult lifespan

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Altered voxel-based and surface-based morphometry in inflammatory bowel disease

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is characterized by inflammation of the gastrointestinal tract and is a disorder of the brain-gut axis. Neuroimaging studies of brain function and structure have helped better understand the relationships between the brain, gut, and comorbidity in IBD. Studies of brain structure have primarily employed voxel-based morphometry to measure grey matter volume and surface-based morphometry to measure cortical thickness. Far fewer studies have employed other surface-based morphometry metrics such as gyrification, cortical complexity, and sulcal depth. In this study, brain structure differences between 72 adults with IBD and 90 healthy controls were assessed using all five metrics. Significant differences were found for cortical thickness with the IBD group showing extensive left-lateralized thinning, and for cortical complexity with the IBD group showing greater complexity in the left fusiform and right posterior cingulate. No significant differences were found in grey matter volume, gyrification, or sulcal depth. Within the IBD group, a post hoc analysis identified that disease duration is associated with cortical complexity of the right supramarginal gyrus, albeit with a more lenient threshold applied.

The subjective amplitude of the diurnal rhythm matters - Chronobiological insights for neuroimaging studies

Multiple aspects of human psychophysiology, including mood and cognition, are subjected to diurnal rhythms. While the previous magnetic resonance imaging (MRI) studies have focused solely on the morningness-eveningness (ME) preference dichotomy, i.e. the circadian phase, the second key dimension of the diurnal rhythms, i.e. the strength of these preferences (amplitude; AM), has been completely overlooked. Uncovering the neural correlates of AM is especially important considering its link with negative emotionality. Structural T1-weighted neuroimaging data from 79 early (EC) and 74 late (LC) chronotypes were analysed to compare grey matter (GM) volume and cortical thickness. The study aimed to elucidate whether the subjective AM and its interaction with ME was a significant predictor of individual brain structure. Both GM volume and cortical thickness of the left primary visual cortex was negatively correlated with AM scores across the entire sample. Furthermore, EC and LC differed in their association between AM scores and the GM volume in the right middle temporal gyrus, with the positive and negative correlations reported respectively in the two groups. The current study underlines the importance of the visual system in circadian rhythmicity and provides possible neural correlates for AM-related differences in negative affect processing. Furthermore, the presence of the opposite correlations between brain anatomy and AM in the two groups suggests that the behavioural and neuronal chronotype differences might become more pronounced in individuals with extreme diurnal differences in mood and cognition, highlighting the necessity to additionally account for AM in neuroimaging studies.

Diffuse glioma-induced structural reorganization in close association with preexisting syntax-related networks

Glioma in the left frontal cortex has been reported to cause agrammatic comprehension and induce global functional connectivity alterations within the syntax-related networks. However, it remains unclear to what extent the structural reorganization is affected by preexisting syntax-related networks. We examined 28 patients with a diffuse glioma in the left hemisphere and 23 healthy participants. Syntactic abilities were assessed by a picture-sentence matching task with various sentence types. The lesion responsible for agrammatic comprehension was identified by region-of-interest-based lesion-symptom mapping (RLSM). Cortical structural alterations were examined by surface-based morphometry (SBM), in which the cortical thickness and fractal dimension were measured with three-dimensional magnetic resonance imaging (MRI). Fiber tracking on the human population-averaged diffusion MRI template was performed to examine whether the cortical structural alterations were associated with the syntax-related networks. The RLSM revealed associations between agrammatic comprehension and a glioma in the posterior limb of the left internal capsule. The SBM demonstrated that decreased cortical thickness and/or increased complexity of the right posterior insula were associated not only with agrammatic comprehension of the patients but also with the syntactic abilities of healthy participants. The fiber tracking revealed that the route between these two regions was anatomically integrated into the preexisting syntax-related networks previously identified. These results suggest a potential association between agrammatic comprehension in patients with diffuse glioma and structural variations in specific tracts and cortical regions, which may be closely related to the syntax-related networks.

Higher emotional granularity relates to greater inferior frontal cortex cortical thickness in healthy, older adults

Individuals with high emotional granularity make fine-grained distinctions between their emotional experiences. To have greater emotional granularity, one must acquire rich conceptual knowledge of emotions and use this knowledge in a controlled and nuanced way. In the brain, the neural correlates of emotional granularity are not well understood. While the anterior temporal lobes, angular gyri, and connected systems represent conceptual knowledge of emotions, inhibitory networks with hubs in the inferior frontal cortex (i.e., posterior inferior frontal gyrus, lateral orbitofrontal cortex, and dorsal anterior insula) guide the selection of this knowledge during emotions. We investigated the structural neuroanatomical correlates of emotional granularity in 58 healthy, older adults (ages 62-84 years), who have had a lifetime to accrue and deploy their conceptual knowledge of emotions. Participants reported on their daily experience of 13 emotions for 8 weeks and underwent structural magnetic resonance imaging. We computed intraclass correlation coefficients across daily emotional experience surveys (45 surveys on average per participant) to quantify each participant's overall emotional granularity. Surface-based morphometry analyses revealed higher overall emotional granularity related to greater cortical thickness in inferior frontal cortex (pFWE < 0.05) in bilateral clusters in the lateral orbitofrontal cortex and extending into the left dorsal anterior insula. Overall emotional granularity was not associated with cortical thickness in the anterior temporal lobes or angular gyri. These findings suggest individual differences in emotional granularity relate to variability in the structural neuroanatomy of the inferior frontal cortex, an area that supports the controlled selection of conceptual knowledge during emotional experiences.

Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder

Background

Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.

Objective

To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.

Methods

Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls.

Results

Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy.

Conclusion

Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Less cortical complexity in ventromedial prefrontal cortex is associated with a greater preference for risky and immediate rewards

In our everyday lives, we are often faced with situations in which we have to make choices that involve risky or delayed rewards. However, the extent to which we are willing to accept larger risky (over smaller certain) or larger delayed (over smaller immediate) rewards vary across individuals. Here we investigated the relationship between cortical surface complexity in medial prefrontal cortex and individual differences in risky and intertemporal preferences. We found that lower cortical complexity in ventromedial prefrontal cortex (vmPFC) was associated with a greater preference for risky and immediate rewards. In addition to these common structural associations in mPFC, we also found associations between lower cortical complexity and a greater preference for immediate rewards that extended into left dorsomedial prefrontal cortex and right vmPFC. Taken together, the shared association suggests that lower cortical complexity in vmPFC may be a structural marker for individual differences in impulsive behavior.

Morphological alterations of contralesional hemisphere relate to functional outcomes after stroke

The present study aimed to investigate poststroke morphological alterations contralesionally and correlations with functional outcomes. Structural magnetic resonance images were obtained from 27 poststroke patients (24 males, 50.21 ± 10.97 years) and 20 healthy controls (13 males, 46.63 ± 12.18 years). Voxel-based and surface-based morphometry analysis were conducted to detect alterations of contralesional grey matter volume (GMV), cortical thickness (CT), gyrification index (GI), sulcus depth (SD), and fractal dimension (FD) in poststroke patients. Partial correlation analysis was used to explore the relationship between regions with significant structural differences and scores of clinical assessments, including Modified Barthel Index (MBI), Berg Balance Scale (BBS), Fugl-Meyer Assessment of Upper Extremity (FMA-UE), Mini-Mental State Examination (MMSE), and Montreal Cognitive Assessment (MoCA). Correction for multiplicity was conducted within each parameter and for all tests. GMV significantly decreased in the contralesional motor-related, occipital and temporal cortex, limbic system, and cerebellum lobe (P < 0.01, family-wise error [FWE] correction). Lower CT was found in the contralesional precentral and lingual gyrus (P < 0.01, FWE correction), while lower GI found in the contralesional superior temporal gyrus and insula (P < 0.01, FWE correction). There were significant correlations between GMV of contralesional lingual gyrus and MBI (P = 0.031, r = 0.441), and BBS (P = 0.047, r = 0.409) scores, and GMV of contralesional hippocampus and FMA-UE scores (P = 0.048, r = 0.408). In conclusion, stroke patients exhibited wide grey matter loss and cortical morphological changes in the contralesional hemisphere, which correlated with sensorimotor functions and the ability of daily living.

Unsupervised machine learning model to predict cognitive impairment in subcortical ischemic vascular disease

INTRODUCTION

It is challenging to predict which patients who meet criteria for subcortical ischemic vascular disease (SIVD) will ultimately progress to subcortical vascular cognitive impairment (SVCI).

METHODS

We collected clinical information, neuropsychological assessments, T1 imaging, diffusion tensor imaging, and resting-state functional magnetic resonance imaging from 83 patients with SVCI and 53 age-matched patients with SIVD without cognitive impairment. We built an unsupervised machine learning model to isolate patients with SVCI. The model was validated using multimodal data from an external cohort comprising 45 patients with SVCI and 32 patients with SIVD without cognitive impairment.

RESULTS

The accuracy, sensitivity, and specificity of the unsupervised machine learning model were 86.03%, 79.52%, and 96.23% and 80.52%, 71.11%, and 93.75% for internal and external cohort, respectively.

DISCUSSION

We developed an accurate and accessible clinical tool which requires only data from routine imaging to predict patients at risk of progressing from SIVD to SVCI.

HIGHLIGHTS

Our unsupervised machine learning model provides an accurate and accessible clinical tool to predict patients at risk of progressing from subcortical ischemic vascular disease (SIVD) to subcortical vascular cognitive impairment (SVCI) and requires only data from imaging routinely used during the diagnosis of suspected SVCI. The model yields good accuracy, sensitivity, and specificity and is portable to other cohorts and to clinical practice to distinguish patients with SIVD at risk for progressing to SVCI. The model combines assessment of diffusion tensor imaging and functional magnetic resonance imaging measures in patients with SVCI to analyze whether the "disconnection hypothesis" contributes to functional and structural changes and to the clinical presentation of SVCI.

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis.

Alterations in cortical morphometry of the contralesional hemisphere in children, adolescents, and young adults with perinatal stroke

Perinatal stroke causes most hemiparetic cerebral palsy and cognitive dysfunction may co-occur. Compensatory developmental changes in the intact contralesional hemisphere may mediate residual function and represent targets for neuromodulation. We used morphometry to explore cortical thickness, grey matter volume, gyrification, and sulcal depth of the contralesional hemisphere in children, adolescents, and young adults after perinatal stroke and explored associations with motor, attention, and executive function. Participants aged 6-20 years (N = 109, 63% male) with unilateral perinatal stroke underwent T1-weighted imaging. Participants had arterial ischemic stroke (AIS; n = 36), periventricular venous infarction (PVI; n = 37) or were controls (n = 36). Morphometry was performed using the Computational Anatomy Toolbox (CAT12). Group differences and associations with motor and executive function (in a smaller subsample) were assessed. Group comparisons revealed areas of lower cortical thickness in contralesional hemispheres in both AIS and PVI and greater gyrification in AIS compared to controls. Areas of greater grey matter volume and sulcal depth were also seen for AIS. The PVI group showed lower grey matter volume in cingulate cortex and less volume in precuneus relative to controls. No associations were found between morphometry metrics, motor, attention, and executive function. Cortical structure of the intact contralesional hemisphere is altered after perinatal stroke. Alterations in contralesional cortical morphometry shown in perinatal stroke may be associated with different mechanisms of damage or timing of early injury. Further investigations with larger samples are required to more thoroughly explore associations with motor and cognitive function.

Lifelong impairment in episodic re-experiencing: Neuropsychological and neuroimaging examination of a new case of Severely Deficient Autobiographical Memory

Autobiographical memory (AM) represents a complex and multimodal cognitive function, that allows an individual to collect and retrieve personal events and facts, enabling to develop and maintain the continuity of the self over time. Here we describe the case of DR (acronym of the fictional name Doriana Rossi), a 53-year-old woman, who complains of a specific and lifelong deficit in recalling autobiographical episodes. Along with an extensive neuropsychological assessment, DR underwent a structural and functional MRI examination to further define this impairment. The neuropsychological assessment revealed a deficit in episodic re-experiencing of her own personal life events. DR showed reduced cortical thickness in the Retrosplenial Complex in the left hemisphere, and in the Lateral Occipital Cortex, in the Prostriate Cortex and the Angular Gyrus in the right hemisphere. An altered pattern of activity in the calcarine cortex was detected during ordering of autobiographical events according to her own personal timeline. The present study provides further evidence about the existence of a severely deficient autobiographical memory condition in neurologically healthy people, with otherwise preserved cognitive functioning. Furthermore, the present data provide new important insights into neurocognitive mechanisms underpinning such a developmental condition.

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills, resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through pre-determined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically-fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporo-parietal junction regions, predominantly follows at least two partially non-overlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis.

Hyperacusis is associated with smaller gray matter volumes in the supplementary motor area

Hyperacusis is a disorder in loudness perception characterized by increased sensitivity to ordinary environmental sounds and associated with otologic conditions, including hearing loss and tinnitus (the phantom perception of sound) as well as neurologic and neuropsychiatric conditions. Hyperacusis is believed to arise centrally in the brain; however, the underlying causes are unknown. To gain insight into differences in brain morphology associated with hyperacusis, we undertook a retrospective case-control study comparing whole-brain gray matter morphology in participants with sensorineural hearing loss and tinnitus who either scored above or below the threshold for hyperacusis based on a standard questionnaire. We found that participants reporting hyperacusis had smaller gray matter volumes and cortical sheet thicknesses in the right supplementary motor area (SMA), independent of anxiety, depression, tinnitus burden, or sex. In fact, the right SMA volumes extracted from an independently defined volume of interest could accurately classify participants. Finally, in a subset of participants where functional data were also available, we found that individuals with hyperacusis showed increased sound-evoked responses in the right SMA compared to individuals without hyperacusis. Given the role of the SMA in initiating motion, these results suggest that in hyperacusis the SMA is involved in a motor response to sounds.

Voxel-Based and Surface-Based Morphometry Analysis in Patients with Pathologically Confirmed Argyrophilic Grain Disease and Alzheimer’s Disease

Background: Due to clinicoradiological similarities, including amnestic cognitive impairment and limbic atrophy, differentiation of argyrophilic grain disease (AGD) from Alzheimer’s disease (AD) is often challenging. Minimally invasive biomarkers, especially magnetic resonance imaging (MRI), are valuable in routine clinical practice. Although it is necessary to explore radiological clues, morphometry analyses using new automated analytical methods, including whole-brain voxel-based morphometry (VBM) and surface-based morphometry (SBM), have not been sufficiently investigated in patients with pathologically confirmed AGD and AD. Objective: This study aimed to determine the volumetric differences in VBM and SBM analyses between patients with pathologically confirmed AGD and AD. Methods: Eight patients with pathologically confirmed AGD with a lower Braak neurofibrillary tangle stage (<III), 11 patients with pathologically confirmed AD without comorbid AGD, and 10 healthy controls (HC) were investigated. Gray matter volumetric changes in VBM and cortical thickness changes in SBM were compared between the two patient groups (i.e., AGD and AD) and the HC group. Results: In contrast to widespread gray matter volume or cortical thickness loss in the bilateral limbic, temporoparietal, and frontal lobes of the AD group, these were limited, especially in the limbic lobes, in the AGD group, compared with that of the HC group. Although bilateral posterior dominant gray matter volume loss was identified in the AD group compared with the AGD group on VBM, there was no significant cluster between these patient groups on SBM. Conclusion: VBM and SBM analyses both showed a different distribution of atrophic changes between AGD and AD.

Longitudinal changes in surface based brain morphometry measures in amnestic mild cognitive impairment and Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is associated with marked brain atrophy. While commonly used structural MRI imaging methods do not account for the complexity of human brain morphology, little is known about the longitudinal changes of cortical geometry and their relationship with cognitive decline in subjects with AD.

METHODS

Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to perform two-sample t-tests to investigate longitudinal changes of cortical thickness (CTh) and three surface-based morphometry measures: fractal dimension (i.e. cortical complexity; FD), gyrification index (GI), and sulcal depth (SD) in subjects with AD, amnestic mild cognitive impairment (aMCI) in comparison to cognitively unimpaired controls (CU) in baseline and 2-year follow-up sMRI scans. In addition, correlations of the morphological measures with two-year cognitive decline as assessed by the modified AD Assessment Scale-Cognitive Subscale (ADAS-Cog 11) were calculated via regression analyses.

RESULTS

Compared to CU, both AD and aMCI showed marked decreases in CTh. In contrast, analyses of FD and GI yielded a more nuanced decline of the respective measures with some areas showing increases in FD and GI. Overall changes in FD and GI were more pronounced in AD as compared to aMCI. Analyses of SD yielded widespread decreases. Interestingly, cognitive decline corresponded well with CTh declines in aMCI but not AD, whereas changes in FD corresponded with AD only but not aMCI, whereas GI and SD were associated with cognitive decline in aMCI and AD.

CONCLUSION

Patterns of longitudinal changes in FD, GI and SD were only partially overlapping with CTh reductions. In AD, surface-based morphometry measures for brain-surface complexity showed better correspondence than CTh with cognitive decline over a two-year period of time. Being drawn from measures reflecting changes in more intricate aspects of human brain morphology, these data provide new insight into the complexity of AD-related brain atrophy and its relationship with cognitive decline.

Neural correlates of gait adaptation in younger and older adults

Mobility decline is a major concern for older adults. A key component of maintaining mobility with advancing age is the ability to learn and adapt to the environment. The split-belt treadmill paradigm is an experimental protocol that tests the ability to adapt to a dynamic environment. Here we examined the magnetic resonance imaging (MRI) derived structural neural correlates of individual differences in adaptation to split-belt walking for younger and older adults. We have previously shown that younger adults adopt an asymmetric walking pattern during split-belt walking, particularly in the medial-lateral (ML) direction, but older adults do not. We collected T[Formula: see text]-weighted and diffusion-weighted MRI scans to quantify brain morphological characteristics (i.e. in the gray matter and white matter) on these same participants. We investigated two distinct questions: (1) Are there structural brain metrics that are associated with the ability to adopt asymmetry during split-belt walking; and (2) Are there different brain-behavior relationships for younger and older adults? Given the growing evidence that indicates the brain has a critical role in the maintenance of gait and balance, we hypothesized that brain areas commonly associated with locomotion (i.e. basal ganglia, sensorimotor cortex, cerebellum) would be associated with ML asymmetry and that older adults would show more associations between split-belt walking and prefrontal brain areas. We identified multiple brain-behavior associations. More gray matter volume in the superior frontal gyrus and cerebellar lobules VIIB and VIII, more sulcal depth in the insula, more gyrification in the pre/post central gyri, and more fractional anisotropy in the corticospinal tract and inferior longitudinal fasciculus corresponded to more gait asymmetry. These associations did not differ between younger and older adults. This work progresses our understanding of how brain structure is associated with balance during walking, particularly during adaptation.

The complexity of cortical folding is reduced in chronic cocaine users

Cocaine use is a worldwide health problem with psychiatric, somatic and socioeconomic complications, being the second most widely used illicit drug in the world. Despite several structural neuroimaging studies, the alterations in cortical morphology associated with cocaine use and addiction are still poorly understood. In this study, we compared the complexity of cortical folding (CCF), a measure that aims to summarize the convoluted structure of the cortex between patients with cocaine addiction (n = 52) and controls (n = 36), and correlated it with characteristics of addiction and impulsivity. We found that patients with cocaine addiction had greater impulsivity and showed reduced CCF in a cluster that encompassed the left insula and the supramarginal gyrus (SMG) and in one in the left medial orbitofrontal cortex. Finally, the CCF in the left medial orbitofrontal cortex was correlated with the age of onset of cocaine addiction and with attentional impulsivity. Overall, our findings suggest that chronic cocaine use is associated with changes in the cortical surface in the fronto-parieto-limbic regions that underlie emotional regulation and these changes are associated with earlier cocaine use. Future longitudinal studies are warranted to unravel the association of these changes with the diathesis for the disorder and with the chronic use of this substance.

Alteration of the cortical morphology in classical trigeminal neuralgia: voxel-, deformation-, and surface-based analysis

OBJECTIVE

This study aimed to combine voxel-based morphometry, deformation-based morphometry, and surface-based morphometry to analyze gray matter volume and cortex shape in classical trigeminal neuralgia patients.

METHODS

This study included 79 classical trigeminal neuralgia patients and age- and sex-matched 81 healthy controls. The aforementioned three methods were used to analyze brain structure in classical trigeminal neuralgia patients. Spearman correlation analysis was used to analyze the correlation of brain structure with the trigeminal nerve and clinical parameters.

RESULTS

The bilateral trigeminal nerve was atrophied, and the ipsilateral trigeminal nerve volume was smaller than the contralateral volume in the classical trigeminal neuralgia. The gray matter volume of Temporal_Pole_Sup_R and Precentral_R was found to be decreased using voxel-based morphometry. The gray matter volume of Temporal_Pole_Sup_R had a positive correlation with disease duration and a negative correlation with the cross-section area of the compression point and the quality-of-life score in trigeminal neuralgia. The gray matter volume of Precentral_R was negatively correlated with the ipsilateral volume of the trigeminal nerve cisternal segment, cross-section area of compression point, and visual analogue scale. The gray matter volume of Temporal_Pole_Sup_L was found to be increased using deformation-based morphometry and had a negative correlation with the self-rating anxiety scale. The gyrification of the middle temporal gyrus_L increased and the Postcentral_L thickness decreased, as detected using surface-based morphometry.

CONCLUSIONS

The gray matter volume and cortical morphology of pain-related brain regions were correlated with clinical and trigeminal nerve parameters. voxel-based morphometry, deformation-based morphometry, and surface-based morphometry complemented each other in analyzing the brain structures of patients with classical trigeminal neuralgia and provided a basis for studying the pathophysiology of classical trigeminal neuralgia.

Altered cortical gyrification, sulcal depth, and fractal dimension in the autism spectrum disorder comorbid attention-deficit/hyperactivity disorder than the autism spectrum disorder

Autism spectrum disorder (ASD) frequently occurs accompanied by attention-deficit/hyperactivity disorder (ADHD), which catches increasing attention. The comorbid diagnosis of ASD with ADHD (ASD + ADHD) is permitted in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). However, compared to autism spectrum disorder without other symptoms (ASD-only), the special neural underpinnings in ASD+ADHD remain unclear. Therefore, this study aimed to uncover the differences in cortical complexity between ASD + ADHD and ASD-only patients. A total of 114 ASD participants (i.e. containing 40 ASD + ADHD and 74 ASD-only participants) with T1-weighted magnetic resonance images were collected from the Autism Brain Imaging Data Exchange II. Afterward, a surface-based morphometry method was carried out to compare the cortical complexity (i.e. gyrification index, fractal dimension, and sulcal depth) between the ASD + ADHD and ASD-only cohorts. Results showed the increased fractal dimension in the right fusiform gyrus of the ASD + ADHD cohort in comparison to the ASD-only cohort. Moreover, the ASD + ADHD cohort exhibited increased sulcal depth in the left middle temporal gyrus/inferior temporal gyrus and right middle temporal gyrus compared to the ASD-only cohort. Last but not least, the increased gyrification index in the insula/postcentral gyrus was observed in the ASD + ADHD cohort in comparison to the ASD-only cohort. Overall, the present study contributes to the delineation of particular structural abnormalities in ASD + ADHD than ASD-only, enriching the evidence of the combined phenotype of ASD + ADHD.

Structural Changes in Brain Magnetic Resonance Imaging Associated With Psychogenic Non-epileptic Seizures: An Analytical Cross-Sectional Study

Background Psychogenic non-epileptic seizures (PNES) are often seen as indicators of poor motor and sensory function caused by psychological responses to stressful experiences. A seizure might trigger these reactions. The aim of our study was to assess the structural changes in brain MRI associated with psychogenic non-epileptic seizures. Methodology A retrospective analytical cross-sectional study at the Department of Medicine and Neurology, Ayub Teaching Hospital, Abbottabad, was conducted from October 2020 to September 2021. The medical records of patients with confirmed PNES were collected and retrospectively evaluated. Results Medical records and MRI scans were accessible for 52 patients with PNES; 10 patients were excluded from the study. The average age of the patients (standard deviation) was 34 (±9) years, and the average age at onset was 31.6 (±5.8) years. Based on the video-EEG recordings, 57.1% of patients (n=24) were classified as having broadly generalized motor seizures, 40% of patients (n=17) were classified as having predominantly akinetic seizures defined primarily by blank spells, and only one patient was classified as having focal motor seizures. Only three patients (7%) had a positive epilepsy family history. Twenty-four (47.6%) patients with brain MRI scans reported abnormal findings, while 22 (52.4%) had normal MRI findings. The majority of patients with abnormal MRIs had nonspecific white matter changes (50%), mesial temporal sclerosis (15%), and cysts (15%). In a statistical analysis, age at the beginning of PNES (p = 0.04), duration of PNES (p=0.01), concomitant epilepsy (p = 0.05), generalized motor seizures (p= 0.03), and focal motor seizures (p= 0.02) were strongly associated with abnormal brain MRI findings. Conclusion Research reveals that persons with PNES have a higher-than-average prevalence of anatomical brain abnormalities. The main takeaway is that these findings lend credence to the growing body of data suggesting that PNES may not be a medical mystery but rather a disorder with physical foundations in the brain. Important implications for diagnosing and treating PNES patients are discussed, as are the outcomes of earlier neuroimaging investigations of PNES. Studying the involvement of structural brain anomalies in the etiology of psychogenic non-epileptic seizures requires further well-designed multicenter studies with larger sample sizes and a consistent imaging approach (PNES). It is crucial to consider any confounding variables, such as co-occurring mental diseases, while designing this study.

Surface-Based Cortical Measures in Multimodal Association Brain Regions Predict Chess Expertise

The complex structure of the brain supports high-order cognition, which is crucial for mastering chess. Surface-based measures, including the fractional dimension (FD) and gyrification index (GI), may be more sensitive in detecting cortical changes relative to volumetric indexes. For this reason, structural magnetic resonance imaging data from 29 chess experts and 29 novice participants were analyzed using the CAT12 toolbox. FD and GI for each brain region were compared between the groups. A multivariate model was used to identify surface-based brain measures that can predict chess expertise. In chess experts, FD is increased in the left frontal operculum (p < 0.01), and this change correlates with the starting age of chess practice (ρ = −0.54, p < 0.01). FD is decreased in the right superior parietal lobule (p < 0.01). Chess expertise is predicted by the FD in a network of fronto-parieto-temporal regions and is associated with GI changes in the middle cingulate gyrus (p < 0.01) and the superior temporal sulcus (p < 0.01). Our findings add to the evidence that chess expertise is based on the complex properties of the brain surface of a network of transmodal association areas important for flexible high-level cognitive functions. Interestingly, these changes are associated with long-lasting practice, suggesting that neuroplastic effects develop over time.