A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging

Cingulate cortex cortical thickness associated with non-suicidal self-injury and suicide risk in youth with mood disorders

BACKGROUND

Non-suicidal self-injury (NSSI) is associated with increased suicide risk and is prevalent among patients with mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD). Structural alterations in cortical regions involved in emotional processing are linked to NSSI as well as suicide risk in mood disorders. Few studies have investigated the neurobiological substrates of NSSI and suicidal thoughts and behaviors (STB), particularly comparing youth with BD to those with MDD. There is a critical need to examine NSSI and STB in the context of MDD and BD separately, as risks differ between these populations.

METHODS

This study investigated the relationship between anterior cingulate cortex (ACC) cortical thickness and volume and NSSI and STB in youth with mood disorders. One-hundred thirty-seven youth (86 with MDD and 51 with BD), ages 13 to 21, completed a diagnostic interview, clinical assessments, and 3 T magnetic resonance imaging. Morphometric analysis of brain images was performed to evaluate differences in cingulate regions of interest.

RESULTS

Seventy-five youth reported a NSSI. Youth with BD were more likely to report NSSI than youth with MDD. In addition, youth with BD and NSSI were more likely to have a suicide attempt and had significantly lower cortical thickness in the right caudal ACC (p = .009, η2 = 0.050) compared to youth with MDD and NSSI.

CONCLUSIONS

These structural alterations in the ACC, which impact emotional regulation and pain processing, may be linked to the increased NSSI and suicide risk observed in BD.

Changes in topological properties of brain structural covariance networks and alertness in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizures

This study investigated brain structural covariance network (SCN) topological changes and alertness in temporal lobe epilepsy (TLE) with and without focal to bilateral tonic-clonic seizures (FBTCS). Seventy-eight subjects, including 32 TLE patients with FBTCS (TLE-FBTCS), 46 TLE patients without FBTCS (TLE-FS), and 42 healthy controls (HCs), underwent the Attention Network Test to assess alertness and volumetric MRI scans. SCNs were constructed and analyzed using graph theory. Results showed that TLE-FS patients had lower total cerebral volume than HCs, and the lowest volume was observed in the TLE-FBTCS group. Compared to HCs and TLE-FBTCS patients, TLE-FS patients exhibited increased small-worldness, normalized clustering coefficient, global efficiency, and modularity, but decreased normalized characteristic shortest path length and assortativity. Specific brain regions, such as the hippocampus, thalamus, and superior temporal sulcus, showed changes in nodal clustering coefficients and efficiency in TLE-FS patients. Further analysis revealed decreased intrinsic/phasic alertness in TLE-FBTCS patients. Correlation analysis indicated that SCN topological properties were associated with alertness in TLE-FS patients but not in TLE-FBTCS patients. These findings suggest that TLE-FS and TLE-FBTCS patients show different changes in SCN integration and segregation, with TLE-FS alertness linked to SCN topological properties, providing insights into TLE's neuropathological mechanisms.

Sex-specific brain morphological and network differences in patients showing Parkinson's disease with and without possible rapid eye movement sleep behavior disorder

Background

Sex is a crucial determinant in the clinical manifestations of diseases. However, previous studies have not clarified whether altered brain morphology shows sex-specific patterns in patients with Parkinson's disease (PD) with or without possible rapid eye movement sleep behavior disorder (RBD). This study aimed to investigate sex-specific differences in the patterns of morphological changes among different subgroups of PD.

Methods

High-resolution T1-weighted magnetic resonance imaging and clinical scale data were collected from 278 participants in the Parkinson's disease Progression Marker Initiative database: 93 patients with PD-pRBD (60 males, 33 females), 114 patients showing PD without RBD (PDnon-pRBD group; 68 males, 46 females), and 71 healthy controls (HCs; 44 males, 17 females). The Computational Anatomy Toolbox (CAT) 12 was utilized to collect data on gray matter volume (GMV) and cortical morphological metrics. Subsequently, individual-level morphological similarity networks were constructed on the basis of these cortical metrics. Finally, the topological properties of the network were analyzed using graph theoretical methods.

Results

In the PD-pRBD group, the GMV in the frontal and temporal lobes of males was lower than that of females. In contrast, the gyrification index (GI) of the frontal lobe in males was lower than that in females within the PDnon-pRBD group. Network analyses based on graph theory revealed that male PD-pRBD patients showed lower network information integration than female patients, particularly in terms of the global properties of fractal dimension (FD) networks. Moreover, in the PD-pRBD group, male patients showed a strong correlation between morphological network metrics and cognitive performance, as measured by the Hopkins Verbal Learning Test-Revised (HVLT-R) memory scores.

Conclusion

The presence of more significant sex-related differences in brain morphological changes in the PD-pRBD group in comparison with the PDnon-pRBD group highlights the importance of considering sex-related differences in the diagnosis and management of patients with PD-pRBD.

Sex-Specific Imaging Biomarkers for Parkinson's Disease Diagnosis: A Machine Learning Analysis

This study aimed to identify sex-specific imaging biomarkers for Parkinson's disease (PD) based on multiple MRI morphological features by using machine learning methods. Participants were categorized into female and male subgroups, and various structural morphological features were extracted. An ensemble Lasso (EnLasso) method was employed to identify a stable optimal feature subset for each sex-based subgroup. Eight typical classifiers were adopted to construct classification models for PD and HC, respectively, to validate whether models specific to sex subgroups could bolster the precision of PD identification. Finally, statistical analysis and correlation tests were carried out on significant brain region features to identify potential sex-specific imaging biomarkers. The best model (MLP) based on the female subgroup and male subgroup achieved average classification accuracy of 92.83% and 92.11%, respectively, which were better than that of the model based on the overall samples (86.88%) and the overall model incorporating gender factor (87.52%). In addition, the most discriminative feature of PD among males was the lh 6r (FD), but among females, it was the lh PreS (GI). The findings indicate that the sex-specific PD diagnosis model yields a significantly higher classification performance compared to previous models that included all participants. Additionally, the male subgroup exhibited a greater number of brain region changes than the female subgroup, suggesting sex-specific differences in PD risk markers. This study underscore the importance of stratifying data by sex and offer insights into sex-specific variations in PD phenotypes, which could aid in the development of precise and personalized diagnostic approaches in the early stages of the disease.

Biophysical basis for brain folding and misfolding patterns in ferrets and humans

A mechanistic understanding of neurodevelopment requires us to follow the multiscale processes that connect molecular genetic processes to macroscopic cerebral cortical formations and thence to neurological function. Using magnetic resonance imaging of the brain of the ferret, a model organism for studying cortical morphogenesis, we create in vitro physical gel models and in silico numerical simulations of normal brain gyrification. Using observations of genetically manipulated animal models, we identify cerebral cortical thickness and cortical expansion rate as the primary drivers of dysmorphogenesis and demonstrate that in silico models allow us to examine the causes of aberrations in morphology and developmental processes at various stages of cortical ontogenesis. Finally, we explain analogous cortical malformations in human brains, with comparisons with human phenotypes induced by the same genetic defects, providing a unified perspective on brain morphogenesis that is driven proximally by genetic causes and affected mechanically via variations in the geometry of the brain and differential growth of the cortex.

Association of cortical macrostructural and microstructural changes with cognitive performance and gene expression in subcortical ischemic vascular disease patients with cognitive impairment

OBJECTIVE

Previous researches have demonstrated that patients with subcortical ischemic vascular disease (SIVD) exhibited brain structure abnormalities. However, the cortical macrostructural and microstructural characteristics and their relationship with cognitive scores and gene expression in SIVD patients remain largely unknown.

METHODS

This study collected 3D-T1 and diffusion tensor imaging data from 30 SIVD patients with cognitive impairment (SIVD-CI) and 32 normal controls. The between-group comparative analyses of cortical thickness, area, volume, local gyrification index (LGI), and mean diffusivity (MD) were conducted with a general linear model. Moreover, the associations between the significant neuroimaging values and the cognitive scores and gene expression values from Allen Human Brain Atlas database were evaluated using partial least squares regression and partial correlation analysis.

RESULTS

SIVD-CI patients showed significant decreases in cortical thicknesses across 18 regions, cortical volumes across three regions, and cortical LGI across five regions, as well as significant increases in cortical MD across five regions (P < 0.05). The significantly reduced cortical thicknesses of the right insula, left superior temporal gyrus, left central anterior gyrus, and left caudal anterior cingulate cortex, as well as the significantly reduced cortical LGI in left caudal anterior cingulate cortex, were significantly positively correlated with different cognitive scores (P < 0.05). Furthermore, the abnormal cortical structural indicators were found to be significantly related to nine risk genes (VCAN, APOE, EFEMP1, SALL1, BCAN, KCNK2, EPN2, DENND1B and XKR6) (P < 0.05).

CONCLUSIONS

The specific cortical structural damage may be related to specific cognitive decline and specific risk genes in SIVD-CI patients.

Mapping the aging brain: Insights into microstructural changes from free water-corrected fractional anisotropy

Aging has a significant impact on brain structure, demonstrated by numerous MRI studies using diffusion tensor imaging (DTI). While these studies reveal changes in fractional anisotropy (FA) across different brain regions, they tend to focus on white matter tracts and cognitive regions, often overlooking gray matter and motor areas. Additionally, traditional DTI metrics can be affected by partial volume effects. To address these limitations and gain a better understanding of microstructural changes across the whole brain, we utilized free water-corrected fractional anisotropy (FAt) to examine aging-related microstructural changes in a group of 20 young adults (YA) and 24 older adults (OA). A voxel-wise analysis revealed that YA had higher FAt values predominantly in white matter tracts associated with both motor and non-motor functions. In contrast, OA showed higher levels of FAt primarily in gray matter regions, including both subcortical and cortical motor areas, and occipital and temporal cortices. Complementing these cross-sectional results, correlation analyses within the OA group showed that many of these changes are further exacerbated with increasing age, underscoring the progressive nature of these microstructural alterations. In summary, the distinct patterns of FAt changes in gray versus white matter with aging suggest different underlying mechanisms. While white matter FAt values decrease, likely due to axonal degeneration, the increase in gray matter FAt could reflect either compensatory processes or pathological changes. Including behavioral data in future studies will be crucial for understanding the functional implications of these microstructural gray matter changes and their effects on cognitive and motor functions.

Atrophy of cerebellar lobule VI and primary motor cortex in cervical dystonia - a region of interest-based study

BACKGROUND

Recently, a network model of cervical dystonia (CD) has been adopted that implicates nodes and pathways involving cerebellar, basal-ganglia and cortico-cortical connections. Although functional changes in the cerebello-thalamo-cortical network in dystonia have been reported in several studies, structural information of this network remain sparse.

OBJECTIVE

To characterize the structural properties of the cerebellar motor network in isolated CD patients. This includes cerebellar lobules involved in motor processing, the dentate nucleus (DN), the thalamus, and the primary motor cortex (M1).

METHODS

Magnetic resonance imaging data of 18 CD patients and 18 healthy control subjects were acquired. In CD patients, the motor part of the Toronto Western Spasmodic Torticollis Rating Scale was assessed to evaluate motor symptom severity. The volume of cerebellar lobules I-VI and VIII, the DN and thalamus, and the cortical thickness (CT) of M1 were determined for a region of interest (ROI)-based quantitative analysis. Volumes/CT of these ROIs were compared between groups and associated with motor symptom severity in patients.

RESULTS

The volume of lobule VI and the CT of M1 were reduced in CD patients. The volumes of the other ROIs were not different between groups. No association was identified between the structural properties of lobule VI or M1 and the severity of CD motor symptoms.

CONCLUSION

Atrophy within the cerebellum and M1 contributes to CD's complex motor network pathology. Further investigations are needed to ascertain the mechanisms underlying the local volume loss.

Atrophy of cerebellum Crus I indicates poor outcome of cochlear implantation in the elderly

Cochlear implantation (CI) is a highly effective treatment for profound hearing loss in elderly individuals, including those with ARHL. However, factors influencing the success of CI in the elderly population are not fully understood. Hence, we sought to investigate the association of regional cerebellar gray matter volume with effectiveness of CI in the elderly. This retrospective cross-sectional study included CI implantees and healthy controls aged ≥ 70 years. We used voxel-based morphometry to investigate the cerebellar gray matter associated with speech perception outcome in the CI group. Among the study participants, cerebellar gray matter volume loss, particularly in the Crus I region, was associated with poorer CI outcomes. Notably, this association was stronger than that observed for the duration of hearing deprivation (DoD). Moreover, the degree of cerebellar atrophy and DoD were found to be independent of each other. No significant correlation was found between the age of the implant and CI outcomes. The findings suggest that cerebellar gray matter atrophy, specifically in the Crus I region, may serve as a predictor of poor outcomes following cochlear implantation in elderly individuals. These results underscore the importance of assessing cerebellar volume loss alongside other factors when counseling elderly patients considering CI.

Cognitive changes and brain structural abnormalities in female carriers of DMD pathogenic variants

BACKGROUND

Skeletal and cardiac muscle damage have been increasingly recognized in female carriers of DMD pathogenic variants (DMDc). Little is known about cognitive impairment in these women or whether they have structural brain damage.

OBJECTIVE

To characterize the cognitive profile in a Brazilian cohort of DMDc and determine whether they have structural brain abnormalities using multimodal MRI.

METHODS

Thirty-three DMDc and 33 age-matched healthy women were recruited. The Addenbrooke cognitive examination revised (ACE-R) and the Beck depression inventory (BDI) were used to assess cognition and depressive symptoms. 3T Brain MRI was acquired for both groups. Using volumetric T1 sequence, we computed cerebral cortical thickness (FreeSurfer), basal ganglia (T1 Multi-atlas) and cerebellar (Cerebnet) volumetry. Diffusion tensor imaging (DTI) assessed white-matter integrity (DTI Multi-atlas). Groups were compared using a generalized linear model with Bonferroni-corrected p values < 0.05.

RESULTS

Mean age of DMDc was 38.2 ± 8.2 years, 48.5% of them had abnormal cognition, but only 15% showed meaningful depressive symptoms. Multiple cognitive domains were affected: Attention in 51.5%, Verbal Fluency in 36.4%, Visuospatial Ability in 36.4%, Language in 27.3%, and Memory in 21.2%. Multimodal MRI revealed bilateral, symmetric atrophy in parieto-occipital cortices in DMDc relative to controls, but no basal ganglia, white matter, or cerebellar changes. Parietal cortex thinning correlated with attention, memory, and verbal fluency scores.

INTERPRETATION

DMDc should no longer be seen as 'asymptomatic'. They have cognitive abnormalities and cerebral structural changes compared to healthy women. These manifestations should be actively identified and managed in clinical practice.

Increased structural covariance of cortical measures in individuals with an at-risk mental state

An anomalous pattern of structural covariance has been reported in schizophrenia, which has been suggested to represent connectome changes during brain maturation and neuroprogressive processes. It remains unclear whether similar differences exist in a clinical high-risk state for psychosis, and if they are associated with a prodromal phenotype and/or later psychosis onset. This multicenter magnetic resonance imaging study cross-sectionally examined structural covariance in a large at-risk mental state (ARMS) sample with different outcomes. The whole-brain structural covariance of four cortical measures (thickness, area, volume, and gyrification) was assessed in 155 individuals with ARMS, who were subclassified into 26 (16.8 %) with a later psychosis onset (ARMS-P), 44 with persistent subthreshold psychotic symptoms, and 53 with the remission of psychotic symptoms (ARMS-R) during the clinical follow-up, and 191 healthy controls. The relationships of changes in structural covariance with clinical symptoms and cognitive impairments were also investigated in the ARMS subsample. Structural covariance was significantly higher in widespread cortical regions in the ARMS group than in the controls, with each cortical measure having a different pattern in affected cortical regions. The higher structural covariance of the cortical area was partly related to severe suspiciousness-persecutory ideation. Structural covariance was significantly higher, mainly in fronto-parietal gyrification, in the ARMS-P group than in the ARMS-R group. The present results suggest that changes in structural covariance result in psychosis vulnerability and the excessive structural covariance of brain gyrification in ARMS subjects may contribute to their later clinical course.

DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods for the treatment of AD, but early intervention can delay the deterioration of the disease. Therefore, the early diagnosis of AD and mild cognitive impairment (MCI) is significant. Structural magnetic resonance imaging (sMRI) is widely used to present structural changes in the subject's brain tissue. The relatively mild structural changes in the brain with MCI have led to ongoing challenges in the task of conversion prediction in MCI. Moreover, many multimodal AD diagnostic models proposed in recent years ignore the potential relationship between multimodal information.

OBJECTIVE

To solve these problems, we propose a multimodal fine-grained classification model based on deep metric learning for AD diagnosis (DML-MFCM), which can fully exploit the fine-grained feature information of sMRI and learn the potential relationships between multimodal feature information.

METHODS

First, we propose a fine-grained feature extraction module that can effectively capture the fine-grained feature information of the lesion area. Then, we introduce a multimodal cross-attention module to learn the potential relationships between multimodal data. In addition, we design a hybrid loss function based on deep metric learning. It can guide the model to learn the feature representation method between samples, which improves the model's performance in disease diagnosis.

RESULTS

We have extensively evaluated the proposed models on the ADNI and AIBL datasets. The ACC of AD vs. NC, MCI vs. NC, and sMCI vs. pMCI tasks in the ADNI dataset are 98.75%, 95.88%, and 88.00%, respectively. The ACC on the AD vs. NC and MCI vs. NC tasks in the AIBL dataset are 94.33% and 91.67%.

CONCLUSIONS

The results demonstrate that our method has excellent performance in AD diagnosis.

Dissecting neural correlates of theory of mind and executive functions in behavioral variant frontotemporal dementia

Behavioral variant frontotemporal dementia (bvFTD) is characterized by profound and early deficits in social cognition (SC) and executive functions (EF). To date it remains unclear whether deficits of the respective cognitive domains are based on the degeneration of distinct brain regions. In 103 patients with a diagnosis of bvFTD (possible/probable/definite: N = 40/58/5) from the frontotemporal lobar degeneration (FTLD) consortium Germany cohort (age 62.5±9.4 years, gender 38 female/65 male) we applied multimodal structural imaging, i.e. voxel-based morphometry, cortical thickness (CTH) and networks of structural covariance via source based morphometry. We cross-sectionally investigated associations with performance in a modified Reading the Mind in the Eyes Test (RMET; reflective of theory of mind - ToM) and five different tests reflective of EF (i.e. Hamasch-Five-Point Test, semantic and phonemic Fluency, Trail Making Test, Stroop interference). Finally, we investigated the conjunction of RMET correlates with functional networks commonly associated with SC respectively ToM and EF as extracted meta-analytically within the Neurosynth database. RMET performance was mainly associated with gray matter volume (GMV) and CTH within temporal and insular cortical regions and less within the prefrontal cortex (PFC), whereas EF performance was mainly associated with prefrontal regions (GMV and CTH). Overlap of RMET and EF associations was primarily located within the insula, adjacent subcortical structures (i.e. putamen) and the dorsolateral PFC (dlPFC). These patterns were more pronounced after adjustment for the respective other cognitive domain. Corroborative results were obtained in analyses of structural covariance networks. Overlap of RMET with meta-analytically extracted functional networks commonly associated with SC, ToM and EF was again primarily located within the temporal and insular region and the dlPFC. In addition, on a meta-analytical level, strong associations were found for temporal cortical RMET correlates with SC and ToM in particular. These data indicate a temporo-frontal dissociation of bvFTD related disturbances of ToM and EF, with atrophy of the anterior temporal lobe being critically involved in ToM deficits. The consistent overlap within the insular cortex may be attributable to the multimodal and integrative role of this region in socioemotional and cognitive processing.

Gray matter volumetric changes in tinnitus: The impact of hearing loss and severity

Tinnitus is a phantom auditory sensation that commonly co-occurs with hearing loss. Both tinnitus and hearing loss can impact the quality of life, emotional well-being, and cognitive functioning of the affected individuals. While previous studies have highlighted structural alterations in hearing loss and/or tinnitus, the fundamental neural mechanisms underpinning tinnitus severity remain poorly understood. In this study, we conducted a voxel-based morphometry to investigate gray matter (GM) volume differences among groups of participants with varying tinnitus severity and hearing status, and controls within a large sample. We observed reduced GM volume in the left anterior insula and right planum polare in participants with hearing loss, regardless of their tinnitus status, compared to normal hearing controls. We noted decreased GM volume in the bilateral anterior and posterior insula for those with tinnitus and normal hearing compared to a normal hearing control group. Further, the tinnitus with hearing loss group showed decreased GM volume in the left planum polare, left inferior temporal gyrus, bilateral anterior temporal gyri, and right superior frontal gyrus compared to the normal hearing control group, suggesting a combined effect of hearing loss and tinnitus. While tinnitus severity did not show a significant overall effect, there was a significant positive correlation between tinnitus distress and GM volume in bilateral planum polare. Our findings enhance the understanding of structural brain changes related to hearing loss and tinnitus, and advance the overall knowledge of tinnitus pathophysiology, which can contribute to the development of more effective treatments for tinnitus.

Medial prefrontal neuroplasticity during extended-release naltrexone treatment of opioid use disorder - a longitudinal structural magnetic resonance imaging study

Opioid use disorder (OUD) has been linked to macroscopic structural alterations in the brain. The monthly injectable, extended-release formulation of μ-opioid antagonist naltrexone (XR-NTX) is highly effective in reducing opioid craving and preventing opioid relapse. Here, we investigated the neuroanatomical effects of XR-NTX by examining changes in cortical thickness during treatment for OUD. Forty-seven OUD patients underwent structural magnetic resonance imaging and subjectively rated their opioid craving ≤1 day before (pre-treatment) and 11 ± 3 days after (on-treatment) the first XR-NTX injection. A sample of fifty-six non-OUD individuals completed a single imaging session and served as the comparison group. A publicly available [¹¹C]carfentanil positron emission tomography dataset was used to assess the relationship between changes in cortical thickness and μ-opioid receptor (MOR) binding potential across brain regions. We found that the thickness of the medial prefrontal and anterior cingulate cortices (mPFC/aCC; regions with high MOR binding potential) was comparable between the non-OUD individuals and the OUD patients at pre-treatment. However, among the OUD patients, mPFC/aCC thickness significantly decreased from pre-treatment to on-treatment. A greater reduction in mPFC/aCC thickness was associated with a greater reduction in opioid craving. Taken together, our study suggests XR-NTX-induced cortical thickness reduction in the mPFC/aCC regions in OUD patients. The reduction in thickness does not appear to indicate a restoration to the non-OUD level but rather reflects XR-NTX's distinct therapeutic impact on an MOR-rich brain structure. Our findings highlight the neuroplastic effects of XR-NTX that may inform the development of novel OUD interventions.

Neurobiological mechanisms of electroconvulsive therapy for depression: Insights into hippocampal volumetric increases from clinical and preclinical studies

Depression is a highly prevalent and disabling psychiatric disorder. The hippocampus, which plays a central role in mood regulation and memory, has received considerable attention in depression research. Electroconvulsive therapy (ECT) is the most effective treatment for severe pharmacotherapy-resistant depression. Although the working mechanism of ECT remains unclear, recent magnetic resonance imaging (MRI) studies have consistently reported increased hippocampal volumes following ECT. The clinical implications of these volumetric increases and the specific cellular and molecular significance are not yet fully understood. This narrative review brings together evidence from animal models and human studies to provide a detailed examination of hippocampal volumetric increases following ECT. In particular, our preclinical MRI research using a mouse model is consistent with human findings, demonstrating a marked increase in hippocampal volume following ECT. Notable changes were observed in the ventral hippocampal CA1 region, including dendritic growth and increased synaptic density at excitatory synapses. Interestingly, inhibition of neurogenesis did not affect the ECT-related hippocampal volumetric increases detected on MRI. However, it remains unclear whether these histological and volumetric changes would be correlated with the clinical effect of ECT. Hence, future research on the relationships between cellular changes, ECT-related brain volumetric changes, and antidepressant effect could benefit from a bidirectional translational approach that integrates human and animal models. Such translational research may provide important insights into the mechanisms and potential biomarkers associated with ECT-induced hippocampal volumetric changes, thereby advancing our understanding of ECT for the treatment of depression.

Mapping brain morphology to cognitive deficits: a study on PD-CRS scores in Parkinson's disease with mild cognitive impairment

Background

The Parkinson's Disease-Cognitive Rating Scale (PD-CRS) is a widely used tool for detecting mild cognitive impairment (MCI) in Parkinson's Disease (PD) patients, however, the neuroanatomical underpinnings of this test's outcomes require clarification. This study aims to: (a) investigate cortical volume (CVol) and cortical thickness (CTh) disparities between PD patients exhibiting mild cognitive impairment (PD-MCI) and those with preserved cognitive abilities (PD-IC); and (b) identify the structural correlates in magnetic resonance imaging (MRI) of overall PD-CRS performance, including its subtest scores, within a non-demented PD cohort.

Materials and methods

This study involved 51 PD patients with Hoehn & Yahr stages I-II, categorized into two groups: PD-IC (n = 36) and PD-MCI (n = 15). Cognitive screening evaluations utilized the PD-CRS and the Montreal Cognitive Assessment (MoCA). PD-MCI classification adhered to the Movement Disorder Society Task Force criteria, incorporating extensive neuropsychological assessments. The interrelation between brain morphology and cognitive performance was determined using FreeSurfer.

Results

Vertex-wise analysis of the entire brain demonstrated a notable reduction in CVol within a 2,934 mm2 cluster, encompassing parietal and temporal regions, in the PD-MCI group relative to the PD-IC group. Lower PD-CRS total scores correlated with decreased CVol in the middle frontal, superior temporal, inferior parietal, and cingulate cortices. The PD-CRS subtests for Sustained Attention and Clock Drawing were associated with cortical thinning in distinct regions: the Clock Drawing subtest correlated with changes in the parietal lobe, insula, and superior temporal cortex morphology; while the PD-CRS frontal-subcortical scores presented positive correlations with CTh in the transverse temporal, medial orbitofrontal, superior temporal, precuneus, fusiform, and supramarginal regions. Additionally, PD-CRS subtests for Semantic and Alternating verbal fluency were linked to CTh changes in orbitofrontal, temporal, fusiform, insula, and precentral regions.

Conclusion

PD-CRS performance mirrors neuroanatomical changes across extensive fronto-temporo-parietal areas, covering both lateral and medial cortical surfaces, in PD patients without dementia. The observed changes in CVol and CTh associated with this cognitive screening tool suggest their potential as surrogate markers for cognitive decline in PD. These findings warrant further exploration and validation in multicenter studies involving independent patient cohorts.

Clinical symptoms and neuroanatomical substrates of daytime sleepiness in Parkinson's disease

Clinical and neuroanatomical correlates of daytime sleepiness in Parkinson's disease (PD) remain inconsistent in the literature. Two studies were conducted here. The first evaluated the interrelation between non-motor and motor symptoms, using a principal component analysis, associated with daytime sleepiness in PD. The second identified the neuroanatomical substrates associated with daytime sleepiness in PD using magnetic resonance imaging (MRI). In the first study, 77 participants with PD completed an extensive clinical, cognitive testing and a polysomnographic recording. In the second study, 29 PD participants also underwent MRI acquisition of T1-weighted images. Vertex-based cortical and subcortical surface analysis, deformation-based morphometry, and voxel-based morphometry were performed to assess the association between daytime sleepiness severity and structural brain changes in participants. In both studies, the severity of daytime sleepiness and the presence of excessive daytime sleepiness (EDS; total score >10) were measured using the Epworth Sleepiness Scale. We found that individuals with EDS had a higher score on a component including higher dosage of dopamine receptor agonists, motor symptoms severity, shorter sleep latency, and greater sleep efficiency. Moreover, increased daytime sleepiness severity was associated with a larger surface area in the right insula, contracted surfaces in the right putamen and right lateral amygdala, and a larger surface in the right posterior amygdala. Hence, daytime sleepiness in PD was associated with dopaminergic receptor agonists dosage, motor impairment, and objective sleep measures. Moreover, neuroanatomical changes in cortical and subcortical regions related to vigilance, motor, and emotional states were associated with more severe daytime sleepiness.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Distinct Patterns of Brain Atrophy in Amnestic Mild Cognitive Impairment and Motoric Cognitive Risk Syndromes

INTRODUCTION

Motoric cognitive risk (MCR) and amnestic mild cognitive impairment (aMCI) syndromes are each reliable predictors of incident Alzheimer's disease (AD), but MCR may be a stronger predictor of vascular dementia than AD. This study contrasted cortical and hippocampal atrophy patterns in MCR and aMCI.

METHODS

Cross-sectional data from 733 older adults without dementia or disability (M age = 73.6; 45% women) in the multicountry MCR consortium were examined. MCR was defined as presence of slow gait and cognitive concerns. Amnestic MCI was defined as poor episodic memory performance and cognitive concerns. Cortical thickness and hippocampal volumes were quantified from structural MRIs. Multivariate and univariate general linear models were used to examine associations between cortical thickness and hippocampal volume in MCR and aMCI, adjusting for age, sex, education, total intracranial volume, white matter lesions, and study site.

RESULTS

The prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. MCR was associated with widespread cortical atrophy, including prefrontal, insular, cingulate, motor, parietal, and temporal atrophy. aMCI was associated with hippocampal atrophy.

CONCLUSION

Distinct patterns of atrophy were associated with MCR and aMCI. A distributed pattern of cortical atrophy - that is more consistent with VaD or mixed dementia- was observed in MCR. A more restricted pattern of atrophy - that is more consistent with AD - was observed in aMCI. The biological underpinnings of MCR and aMCI likely differ and may require tailored interventions.

INTRODUCTION

Motoric cognitive risk (MCR) and amnestic mild cognitive impairment (aMCI) syndromes are each reliable predictors of incident Alzheimer's disease (AD), but MCR may be a stronger predictor of vascular dementia than AD. This study contrasted cortical and hippocampal atrophy patterns in MCR and aMCI.

METHODS

Cross-sectional data from 733 older adults without dementia or disability (M age = 73.6; 45% women) in the multicountry MCR consortium were examined. MCR was defined as presence of slow gait and cognitive concerns. Amnestic MCI was defined as poor episodic memory performance and cognitive concerns. Cortical thickness and hippocampal volumes were quantified from structural MRIs. Multivariate and univariate general linear models were used to examine associations between cortical thickness and hippocampal volume in MCR and aMCI, adjusting for age, sex, education, total intracranial volume, white matter lesions, and study site.

RESULTS

The prevalence of MCR and aMCI was 7.64% and 12.96%, respectively. MCR was associated with widespread cortical atrophy, including prefrontal, insular, cingulate, motor, parietal, and temporal atrophy. aMCI was associated with hippocampal atrophy.

CONCLUSION

Distinct patterns of atrophy were associated with MCR and aMCI. A distributed pattern of cortical atrophy - that is more consistent with VaD or mixed dementia- was observed in MCR. A more restricted pattern of atrophy - that is more consistent with AD - was observed in aMCI. The biological underpinnings of MCR and aMCI likely differ and may require tailored interventions.

Menstrual cycle-driven hormone concentrations co-fluctuate with white and gray matter architecture changes across the whole brain

Cyclic fluctuations in hypothalamic-pituitary-gonadal axis (HPG-axis) hormones exert powerful behavioral, structural, and functional effects through actions on the mammalian central nervous system. Yet, very little is known about how these fluctuations alter the structural nodes and information highways of the human brain. In a study of 30 naturally cycling women, we employed multidimensional diffusion and T1-weighted imaging during three estimated menstrual cycle phases (menses, ovulation, and mid-luteal) to investigate whether HPG-axis hormone concentrations co-fluctuate with alterations in white matter (WM) microstructure, cortical thickness (CT), and brain volume. Across the whole brain, 17β-estradiol and luteinizing hormone (LH) concentrations were directly proportional to diffusion anisotropy (μFA; 17β-estradiol: β1 = 0.145, highest density interval (HDI) = [0.211, 0.4]; LH: β1 = 0.111, HDI = [0.157, 0.364]), while follicle-stimulating hormone (FSH) was directly proportional to CT (β1 = 0 .162, HDI = [0.115, 0.678]). Within several individual regions, FSH and progesterone demonstrated opposing relationships with mean diffusivity (Diso) and CT. These regions mainly reside within the temporal and occipital lobes, with functional implications for the limbic and visual systems. Finally, progesterone was associated with increased tissue (β1 = 0.66, HDI = [0.607, 15.845]) and decreased cerebrospinal fluid (CSF; β1 = -0.749, HDI = [-11.604, -0.903]) volumes, with total brain volume remaining unchanged. These results are the first to report simultaneous brain-wide changes in human WM microstructure and CT coinciding with menstrual cycle-driven hormone rhythms. Effects were observed in both classically known HPG-axis receptor-dense regions (medial temporal lobe, prefrontal cortex) and in other regions located across frontal, occipital, temporal, and parietal lobes. Our results suggest that HPG-axis hormone fluctuations may have significant structural impacts across the entire brain.

Greater cortical thinning and microstructural integrity loss in myotonic dystrophy type 1 compared to myotonic dystrophy type 2

BACKGROUND

Myotonic dystrophy is a multisystem disorder characterized by widespread organic involvement including central nervous system symptoms. Although myotonic dystrophy disease types 1 (DM1) and 2 (DM2) cover a similar spectrum of symptoms, more pronounced clinical and brain alterations have been described in DM1. Here, we investigated brain volumetric and white matter alterations in both disease types and compared to healthy controls (HC).

METHODS

MRI scans were obtained from 29 DM1, 27 DM2, and 56 HC. We assessed macro- and microstructural brain changes by surface-based analysis of cortical thickness of anatomical images and tract-based spatial statistics of fractional anisotropy (FA) obtained by diffusion-weighted imaging, respectively. Global MRI measures were related to clinical and neuropsychological scores to evaluate their clinical relevance.

RESULTS

Cortical thickness was reduced in both patient groups compared to HC, showing similar patterns of regional distribution in DM1 and DM2 (occipital, temporal, frontal) but more pronounced cortical thinning for DM1. Similarly, FA values showed a widespread decrease in DM1 and DM2 compared to HC. Interestingly, FA was significantly lower in DM1 compared to DM2 within most parts of the brain.

CONCLUSION

Comparisons between DM1 and DM2 indicate a more pronounced cortical thinning of grey matter and a widespread reduction in microstructural integrity of white matter in DM1. Future studies are required to unravel the underlying and separating mechanisms for the disease courses of the two types and their neuropsychological symptoms.

Genetic and environmental influences on structural brain development from childhood to adolescence: A longitudinal twin study on cortical thickness, surface area, and subcortical volume

The human brain undergoes structural development from childhood to adolescence, with specific regions in the sensorimotor, social, and affective networks continuing to grow into adulthood. While genetic and environmental factors contribute to individual differences in these brain trajectories, the extent remains understudied. Our longitudinal study, utilizing up to three biennial MRI scans (n=485), aimed to assess the genetic and environmental effects on brain structure (age 7) and development (ages 7-14) in these regions. Heritability estimates varied across brain regions, with all regions showing genetic influence (ranging from 18 % to 59 %) with additional shared environmental factors affecting the primary motor cortex (30 %), somatosensory cortex (35 %), DLPFC (5 %), TPJ (17 %), STS (17 %), precuneus (10 %), hippocampus (22 %), amygdala (5 %), and nucleus accumbens (10 %). Surface area was more genetically driven (38 %) than cortical thickness (14 %). Longitudinal brain changes were primarily driven by genetics (ranging from 1 % to 29 %), though shared environment factors (additionally) influenced the somatosensory cortex (11 %), DLPFC (7 %), cerebellum (28 %), TPJ (16 %), STS (20 %), and hippocampus (17 %). These findings highlight the importance of further investigating brain-behavior associations and the influence of enriched and deprived environments from childhood to adolescence. Ultimately, our study can provide insights for interventions aimed at supporting children's development.

Association of Arterial Stiffness Index and Brain Structure in the UK Biobank: A 10-Year Retrospective Analysis

Arterial stiffening and changes in brain structure both occur with normal aging and can be exacerbated via acquired health conditions. While cross-sectional associations exist, the longitudinal relationship between arterial stiffness and brain structure remains unclear. In this study, we investigated 1) associations between baseline arterial stiffness index (ASI) and brain structure (global and regional grey matter volumes (GMV), white matter hyperintensities (WMH)) 10-years post-baseline (10.4±0.8 years) and 2) associations between the 10-year change in ASI from baseline and brain structure 10-years post-baseline in 650 healthy middle- to older-aged adults (53.4±7.5 years) from the UK Biobank. We observed significant associations between baseline ASI and GMV (p<0.001) and WMH (p=0.0036) 10-years post-baseline. No significant associations between 10-year change in ASI and brain structure (global GMV p=0.24; WMH volume p=0.87) were observed. There were significant associations of baseline ASI in 2 of 60 regional brain volumes analyzed (right posterior superior temporal gyrus p=0.001; left superior lateral occipital cortex p<0.001). Strong associations with baseline ASI, but not changes in ASI over 10-years, suggest arterial stiffness at the entry point of older adulthood is more impactful on brain structure 10-years later compared to age-related stiffening. Based on these associations, we suggest clinical monitoring and potential intervention for reducing arterial stiffness should occur in midlife to reduce vascular contributions to structural changes in the brain, supporting a healthy trajectory of brain aging. Our findings also support use of ASI as a surrogate for gold standard measures in showing overall relationships between arterial stiffness and brain structure.

A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients

Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.

Dysfunction of motor cortices in Parkinson's disease

The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.

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 pathways to neuronal atrophy in healthy, mid-aged adults: From chronic stress to systemic inflammation to neurodegeneration?

Growing evidence implicates systemic inflammation in the loss of structural brain integrity in natural ageing and disorder development. Chronic stress and glucocorticoid exposure can potentiate inflammatory processes and may also be linked to neuronal atrophy, particularly in the hippocampus and the human neocortex. To improve understanding of emerging maladaptive interactions between stress and inflammation, this study examined evidence for glucocorticoid- and inflammation-mediated neurodegeneration in healthy mid-aged adults. N = 169 healthy adults (mean age = 39.4, 64.5% female) were sampled from the general population in the context of the ReSource Project. Stress, inflammation and neuronal atrophy were quantified using physiological indices of chronic stress (hair cortisol (HCC) and cortisone (HEC) concentration), systemic inflammation (interleukin-6 (IL-6), high-sensitive C-reactive protein (hs-CRP)), the systemic inflammation index (SII), hippocampal volume (HCV) and cortical thickness (CT) in regions of interest. Structural equation models were used to examine evidence for pathways from stress and inflammation to neuronal atrophy. Model fit indices indicated good representation of stress, inflammation, and neurological data through the constructed models (CT model: robust RMSEA = 0.041, robust χ2 = 910.90; HCV model: robust RMSEA <0.001, robust χ2 = 40.95). Among inflammatory indices, only the SII was positively associated with hair cortisol as one indicator of chronic stress (β = 0.18, p < 0.05). Direct and indirect pathways from chronic stress and systemic inflammation to cortical thickness or hippocampal volume were non-significant. In exploratory analysis, the SII was inversely related to mean cortical thickness. Our results emphasize the importance of considering the multidimensionality of systemic inflammation and chronic stress, with various indicators that may represent different aspects of the systemic reaction. We conclude that inflammation and glucocorticoid-mediated neurodegeneration indicated by IL-6 and hs-CRP and HCC and HEC may only emerge during advanced ageing and disorder processes, still the SII could be a promising candidate for detecting associations between inflammation and neurodegeneration in younger and healthy samples. Future work should examine these pathways in prospective longitudinal designs, for which the present investigation serves as a baseline.

Cognitive reserve involves decision making and is associated with left parietal and hippocampal hypertrophy in neurodegeneration

Cognitive reserve is the ability to actively cope with brain deterioration and delay cognitive decline in neurodegenerative diseases. It operates by optimizing performance through differential recruitment of brain networks or alternative cognitive strategies. We investigated cognitive reserve using Huntington's disease (HD) as a genetic model of neurodegeneration to compare premanifest HD, manifest HD, and controls. Contrary to manifest HD, premanifest HD behave as controls despite neurodegeneration. By decomposing the cognitive processes underlying decision making, drift diffusion models revealed a response profile that differs progressively from controls to premanifest and manifest HD. Here, we show that cognitive reserve in premanifest HD is supported by an increased rate of evidence accumulation compensating for the abnormal increase in the amount of evidence needed to make a decision. This higher rate is associated with left superior parietal and hippocampal hypertrophy, and exhibits a bell shape over the course of disease progression, characteristic of compensation.

Neuroanatomical Signature of the Transition from Normal Cognition to MCI in Parkinson's Disease

The progression of Parkinson's disease (PD) is often accompanied by cognitive decline. We had previously developed a brain age estimation program utilizing structural MRI data of 949 healthy individuals from publicly available sources. Structural MRI data of 244 PD patients who were cognitively normal at baseline was acquired from the Parkinson Progression Markers Initiative (PPMI). 192 of these showed stable normal cognitive function from baseline out to 5 years (PD-SNC), and the remaining 52 had unstable normal cognition and developed mild cognitive impairment within 5 years (PD-UNC). 105 healthy controls were also included in the analysis as a reference. First, we examined if there were any baseline differences in regional brain structure between PD-UNC and PD-SNC cohorts utilizing the three most widely used atrophy estimation pipelines, i.e., voxel-based morphometry (VBM), deformation-based morphometry and cortical thickness analyses. We then investigated if accelerated brain age estimation with our multivariate regressive machine learning algorithm was different across these groups (HC, PD-SNC, and PD-UNC). As per the VBM analysis, PD-UNC patients demonstrated a noticeable increase in GM volume in the posterior and anterior lobes of the cerebellum, sub-lobar, extra-nuclear, thalamus, and pulvinar regions when compared to PD-SNC at baseline. PD-UNC patients were observed to have significantly older brain age compared to both PD-SNC patients (p=0.009) and healthy controls (p<0.009). The increase in GM volume in the PD-UNC group could potentially indicate an inflammatory or neuronal hypertrophy response, which could serve as a biomarker for future cognitive decline among this population.

Association between hearing ability and cortical morphology in the elderly: multiparametric mapping, cognitive relevance, and neurobiological underpinnings

BACKGROUND

Hearing impairment is a common condition in the elderly. However, a comprehensive understanding of its neural correlates is still lacking.

METHODS

We recruited 284 elderly adults who underwent structural MRI, magnetic resonance spectroscopy, audiometry, and cognitive assessments. Individual hearing abilities indexed by pure tone average (PTA) were correlated with multiple structural MRI-derived cortical morphological indices. For regions showing significant correlations, mediation analyses were performed to examine their role in the relationship between hearing ability and cognitive function. Finally, the correlation maps between hearing ability and cortical morphology were linked with publicly available connectomic gradient, transcriptomic, and neurotransmitter maps.

FINDINGS

Poorer hearing was related to cortical thickness (CT) reductions in widespread regions and gyrification index (GI) reductions in the right Area 52 and Insular Granular Complex. The GI in the right Area 52 mediated the relationship between hearing ability and executive function. This mediating effect was further modulated by glutamate and N-acetylaspartate levels in the right auditory region. The PTA-CT correlation map followed microstructural connectomic hierarchy, were related to genes involved in certain biological processes (e.g., glutamate metabolic process), cell types (e.g., excitatory neurons and astrocytes), and developmental stages (i.e., childhood to young adulthood), and covaried with dopamine receptor 1, dopamine transporter, and fluorodopa. The PTA-GI correlation map was related to 5-hydroxytryptamine receptor 2a.

INTERPRETATION

Poorer hearing is associated with cortical thinning and folding reductions, which may be engaged in the relationship between hearing impairment and cognitive decline in the elderly and have different neurobiological substrates.

FUNDING

See the Acknowledgements section.

Cocaine self-administration behavior is associated with subcortical and cortical morphometry measures in individuals with cocaine use disorder

Background: Individual differences in gray-matter morphometry in the limbic system and frontal cortex have been linked to clinical features of cocaine use disorder (CUD). Self-administration paradigms can provide more direct measurements of the relationship between the regulation of cocaine use and gray-matter morphometry when compared to self-report assessments.Objectives: Our goal was to investigate associations with self-administration behavior in subcortical and cortical brain regions. We hypothesized the number of cocaine infusions self-administered would be correlated with gray-matter volumes (GMVs) in the striatum, amygdala, and hippocampus. Due to scarcity in human studies, we did not hypothesize subcortical directionality. In the frontal cortex, we hypothesized thickness would be negatively correlated with self-administered cocaine.Methods: We conducted an analysis of cocaine self-administration and structural MRI data from 33 (nFemales = 10) individuals with moderate-to-severe CUD. Self-administration lasted 60-minutes and cocaine (8, 16, or 32 mg/70 kg) was delivered on an FR1 schedule (5-minute lockout). Subcortical and cortical regression analyses were performed that included combined bilateral regions and age, experimental variables and use history as confounders.Results: Self-administered cocaine infusions were positively associated with caudal GMV (b = 0.18, p = 0.030) and negatively with putamenal GMV (b = -0.10, p = 0.041). In the cortical model, infusions were positively associated with insular thickness (b = 0.39, p = 0.008) and women appeared to self-administer cocaine more frequently (b = 0.23, p = 0.019).Conclusions: Brain morphometry features in the striatum and insula may contribute to cocaine consumption in CUD. These differences in morphometry may reflect consequences of prolonged use, predisposed vulnerability, or other possibilities.Clinical Trial Numbers: NCT01978431; NCT03471182.

Gray matter and episodic memory associations with olfaction in middle-aged to older adults

BACKGROUND

Age-related declines in olfaction contribute to low quality of life and appear to occur with declines in cognitive function, including diminished episodic memory. We tested the hypothesis that low gray matter volume within cortical regions that support olfaction and episodic memory can explain age-related differences in olfactory and episodic memory functions.

METHODS

T1-weighted images, Sniffin' Sticks olfactory measures, and the NIH Toolbox-Cognition Battery were administered to 131 middle-aged to older adults (50-86 years; 66% female). Correlation was used to examine the associations between these measures. A network-based image processing approach was then used to examine the degree to which spatial patterns of gray matter variance were related to the olfactory and cognitive measures. Structural equation modeling was used to characterize the relative specificity of olfactory, cognitive, gray matter, and aging associations.

RESULTS

Olfactory threshold, discrimination, and identification exhibited small to medium effect size associations with episodic memory performance (rs = 0.27-0.42, ps < 0.002). Gray matter volume within medial temporal and orbitofrontal cortex was also related to olfactory (discrimination and identification) and episodic memory function (rs = 0.21-0.36, ps < 0.019). Age and episodic memory explained the same variance in olfaction that was explained by the medial temporal and orbitofrontal pattern of gray matter volume.

CONCLUSIONS

The results of this cross-sectional study suggest that identifying mechanisms contributing to differences in medial temporal and orbitofrontal cortex will advance our understanding of co-morbid olfactory and cognitive declines.

Asymmetry in Cortical Thickness of the Heschl's Gyrus in Unilateral Ear Canal Atresia

HYPOTHESIS

Unilateral congenital conductive hearing impairment in ear canal atresia leads to atrophy of the gray matter of the contralateral primary auditory cortex or changes in asymmetry pattern if left untreated in childhood.

BACKGROUND

Unilateral ear canal atresia with associated severe conductive hearing loss results in deteriorated sound localization and difficulties in understanding of speech in a noisy environment. Cortical atrophy in the Heschl's gyrus has been reported in acquired sensorineural hearing loss but has not been studied in unilateral conductive hearing loss.

METHODS

We obtained T1w and T2w FLAIR MRI data from 17 subjects with unilateral congenital ear canal atresia and 17 matched controls. Gray matter volume and thickness were measured in the Heschl's gyrus using Freesurfer.

RESULTS

In unilateral congenital ear canal atresia, Heschl's gyrus exhibited cortical thickness asymmetry (right thicker than left, corrected p = 0.0012, mean difference 0.25 mm), while controls had symmetric findings. Gray matter volume and total thickness did not differ from controls with normal hearing.

CONCLUSION

We observed cortical thickness asymmetry in congenital unilateral ear canal atresia but no evidence of contralateral cortex atrophy. Further research is needed to understand the implications of this asymmetry on central auditory processing deficits.

Contributions of Polygenic Risk and Disease Status to Gray Matter Abnormalities in Major Depression

BACKGROUND

Gray matter (GM) abnormalities in depression are potentially attributable to some combination of trait, state, and illness history factors. Here, we sought to determine the contributions of polygenic risk for depression, depressive disease status, and the interaction of these factors to these GM abnormalities.

METHODS

We conducted a cross-sectional comparison using a 2 × 3 factorial design examining effects of polygenic risk for depression (lower vs. upper quartile) and depression status (never depressed, currently depressed, or remitted depression) on regional GM concentration and GM volume. Participants were a subset of magnetic resonance imaging-scanned UK Biobank participants comprising 2682 people (876 men, 1806 women) algorithmically matched on 16 potential confounders.

RESULTS

In women but not men, we observed that elevated polygenic risk for depression was associated with reduced cerebellar GM volume. This deficit occurred in salience and dorsal attention network regions of the cerebellum and was associated with poorer performance on tests of attention and executive function but not fluid intelligence. Moreover, in women with current depression compared to both women with remitted depression and women who never had depression, we observed GM reductions in ventral and medial prefrontal, insular, and medial temporal regions. These state-related abnormalities remained when accounting for antidepressant medication status.

CONCLUSIONS

Neuroanatomical deficits attributed broadly to major depression are more likely due to an aggregation of independent factors. Polygenic risk for depression accounted for cerebellar structural abnormalities that themselves accounted for cognitive deficits observed in this disorder. Medial and ventral prefrontal, insular, and temporal cortex deficits constituted a much larger proportion of the aggregate deficit and were attributable to the depressed state.

Periodontitis is associated with altered brain structure and function in normal cognition middle-aged and elderly individuals

BACKGROUND

Numerous studies have proposed that periodontitis is a potential risk factor for Alzheimer's disease. However, the association between periodontitis and brain normal cognition in aged and elderly individuals (NCs) is unclear. Such a link could provide clues to Alzheimer's disease development and strategies for early prevention.

OBJECTIVE

To explore the associations between periodontal condition and metrics of both brain structure and function among NCs with the help of multimodal magnetic resonance imaging (MRI).

METHODS

High-resolution T1-weighted structural data, resting-state functional-MRI data, and measures of periodontal condition were collected from 40 NCs. Cortical volume, thickness, and area as well as regional homogeneity were calculated with the aid of DPABISurf software. Correlation analyses were then conducted between each imaging metric and periodontal index.

RESULTS

Consistent negative correlations were observed between severity of periodontitis (mild, moderate, severe) and cortical volume, area, and thickness, not only in brain regions that took charge of primary function but also in brain regions associated with advanced cognition behavior. Among participants with mild attachment loss (AL) and a shallow periodontal pocket depth (PPD), periodontal index was positively correlated with most measures of brain structure and function, while among participants with severe AL and deep PPD, periodontal index was negatively correlated with measures of brain structure and function (all p < .005 for each hemisphere).

CONCLUSIONS

Our results demonstrate that periodontitis is associated with widespread changes in brain structure and function among middle-aged and elderly adults without signs of cognitive decline, which might be a potential risk factor for brain damage.

Plasma oligomer beta-amyloid is associated with disease severity and cerebral amyloid deposition in Alzheimer's disease spectrum

BACKGROUND

Multimer detection system-oligomeric amyloid-β (MDS-OAβ) is a measure of plasma OAβ, which is associated with Alzheimer's disease (AD) pathology. However, the relationship between MDS-OAβ and disease severity of AD is not clear. We aimed to investigate MDS-OAβ levels in different stages of AD and analyze the association between MDS-OAβ and cerebral Aβ deposition, cognitive function, and cortical thickness in subjects within the AD continuum.

METHODS

In this cross-sectional study, we analyzed a total 126 participants who underwent plasma MDS-OAβ, structural magnetic resonance image of brain, and neurocognitive measures using Korean version of the Consortium to Establish a Registry for Alzheimer's Disease, and cerebral Aβ deposition or amyloid positron emission tomography (A-PET) assessed by [18F] flutemetamol PET. Subjects were divided into 4 groups: N = 39 for normal control (NC), N = 31 for A-PET-negative mild cognitive impairment (MCI) patients, N = 30 for A-PET-positive MCI patients, and N = 22 for AD dementia patients. The severity of cerebral Aβ deposition was expressed as standard uptake value ratio (SUVR).

RESULTS

Compared to the NC (0.803 ± 0.27), MDS-OAβ level was higher in the A-PET-negative MCI group (0.946 ± 0.137) and highest in the A-PET-positive MCI group (1.07 ± 0.17). MDS-OAβ level in the AD dementia group was higher than in the NC, but it fell to that of the A-PET-negative MCI group level (0.958 ± 0.103). There were negative associations between MDS-OAβ and cognitive function and both global and regional cerebral Aβ deposition (SUVR). Cortical thickness of the left fusiform gyrus showed a negative association with MDS-OAβ when we excluded the AD dementia group.

CONCLUSIONS

These findings suggest that MDS-OAβ is not only associated with neurocognitive staging, but also with cerebral Aβ burden in patients along the AD continuum.

Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking

OBJECTIVE

Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance.

METHOD

Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions.

RESULTS

Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02).

CONCLUSIONS

Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.

Distinctive cortical morphological patterns in primary trigeminal neuralgia: a cross-sectional clinical study

PURPOSE

The characteristics of surface-based morphological patterns to primary trigeminal neuralgia (PTN) are still not well understood. This study aims to screen the useful cortical indices for the prediction of PTN and the quantification of pain severity.

METHODS

Fifty PTN patients and 48 matched healthy subjects enrolled in the study from March 2016 to August 2021. High-resolution T1 data were performed at 3.0 Tesla scanner and were analyzed with FreeSurfer software to detect the abnormalities of cortical mean curve (CMC), cortical thickness (CT), surface area (SA), and cortical volume (CV) in PTN patients compared to healthy controls. Logistic regression analysis was conducted to determine whether certain morphological patterns could predict PTN disorder. Then, the relationships of cortical indices to the pain characteristics in patient group were examined using linear regression model.

RESULTS

Distinctive cortical alterations were discovered through surface-based analysis, including increased temporal CMC, decreased insular CT and fusiform SA, along with decreased CV in several temporal and occipital areas. Moreover, the difference of temporal CMC was greater than other cortical parameters between the two groups, and the combination of certain morphological indices was of good value in the diagnosis for PTN. Besides, CT of left insula was negatively associated with the pain intensity in PTN patients.

CONCLUSION

The patients with PTN demonstrate distinctive morphological patterns in several cortical regions, which may contribute to the imaging diagnosis of this refractory disorder and be useful for the quantification of the orofacial pain.

CLINICAL TRIALS

The registry name of this study in https://clinicaltrials.gov/ : Magnetic Resonance Imaging Study on Patients with Trigeminal Neuralgia (MRI-TN) https://clinicaltrials.gov/ ID: NCT02713646 A link to the full application: https://clinicaltrials.gov/ct2/results?cond=&term=NCT02713646&cntry=&state=&city=&dist= The first patient with primary trigeminal neuralgia was recruited on November 28, 2016.

Regional cortical thickness recovery with extended abstinence after treatment in those with alcohol use disorder

Several cross-sectional investigations reported widespread cortical thinning in those with alcohol use disorder (AUD). The few longitudinal studies investigating cortical thickness changes during abstinence are limited to the first month of sobriety. Consequently, cortical thickness changes during extended abstinence in those with AUD is unclear. In this study, AUD participants were studied at approximately 1 week (n = 68), 1 month (n = 88), and 7.3 months (n = 40) of abstinence. Forty-five never-smoking controls (CON) completed a baseline study, and 15 were reassessed after approximately 9.6 months. Participants completed magnetic resonance imaging studies at 1.5T, and cortical thickness for 34 bilateral regions of interest (ROI) was quantitated with FreeSurfer. AUD participants demonstrated significant linear thickness increases in 25/34 ROI over 7.3 months of abstinence. The rate of change from 1 week to 1 month was greater than 1 month to 7.3 months in 19/34 ROIs. Proatherogenic conditions were associated with lower thickness recovery in anterior frontal, inferior parietal, and lateral/mesial temporal regions. After 7.3 months of abstinence, AUD participants were statistically equivalent to CON on cortical thickness in 24/34 ROIs; the cortical thickness differences between AUD and CON in the banks superior temporal gyrus, post central, posterior cingulate, superior parietal, supramarginal, and superior frontal cortices were driven by thinner cortices in AUD with proatherogenic conditions relative to CON. In actively smoking AUD, increasing pack-years was associated with decreasing thickness recovery primarily in the anterior frontal ROIs. Widespread bilateral cortical thickness recovery over 7.3 months of abstinence was the central finding for this AUD cohort. The longitudinal and cross-sectional findings for AUD with proatherogenic suggests alterations in perfusion or vascular integrity may relate to structural recovery in those with AUD. These results support the adaptive and beneficial effects of sustained sobriety on brain structural recovery in people with AUD.

Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models

This work illustrates the use of normative models in a longitudinal neuroimaging study of children aged 6-17 years and demonstrates how such models can be used to make meaningful comparisons in longitudinal studies, even when individuals are scanned with different scanners across successive study waves. More specifically, we first estimated a large-scale reference normative model using Hierarchical Bayesian Regression from N = 42,993 individuals across the lifespan and from dozens of sites. We then transfer these models to a longitudinal developmental cohort (N = 6285) with three measurement waves acquired on two different scanners that were unseen during estimation of the reference models. We show that the use of normative models provides individual deviation scores that are independent of scanner effects and efficiently accommodate inter-site variations. Moreover, we provide empirical evidence to guide the optimization of sample size for the transfer of prior knowledge about the distribution of regional cortical thicknesses. We show that a transfer set containing as few as 25 samples per site can lead to good performance metrics on the test set. Finally, we demonstrate the clinical utility of this approach by showing that deviation scores obtained from the transferred normative models are able to detect and chart morphological heterogeneity in individuals born preterm.

Dorsal and ventral fronto-amygdala networks underlie risky decision-making in age-related cognitive decline

Older adults often have difficulty in making decisions under uncertainty, increasing the risk of financial exploitation. However, it is still under investigation about the extent to which cognitive decline influences risky decision-making and the underlying neural correlates. We hypothesized that the individual differences of risk-taking behavior depend on cognitive integrity, in which the dorsal and ventral fronto-amygdala connectivity would play dissociable roles. In the current study, thirty-six young and 51 older adults were tested with the Iowa gambling task combing resting-state and task-related functional magnetic resonance imaging. The results showed significant changes in behaviors and the fronto-amygdala network in older adults relative to young adults. More importantly, age-effect on risk-taking behaviors was remarkably different in cognitively normal and impaired older adults. In resting-state analysis, task performance was positively correlated with the ventral fronto-amygdala connectivity and negatively correlated with the dorsal fronto-amygdala connectivity in cognitively impaired older adults, compared with cognitively normal individuals. Furthermore, task-related analysis confirmed the relationships between dorsal/ventral fronto-amygdala network and risk-taking behaviors depending on cognitive integrity. These findings indicate that the fronto-amygdala network is crucial for understanding altered risky decision-making in aging, suggesting dissociable contributions of the dorsal and ventral pathways in the context of cognitive decline.

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.

The cortical thickness of tricenarian cocaine users assembles features of an octogenarian brain

It has been suggested that substance use disorders could lead to accelerated biological aging, but only a few neuroimaging studies have investigated this hypothesis so far. In this cross-sectional study, structural neuroimaging was performed to measure cortical thickness (CT) in tricenarian adults with cocaine use disorder (CUD, n1  = 30) and their age-paired controls (YC, n1  = 30), and compare it with octogenarian elder controls (EC, n1  = 20). We found that CT in the right fusiform gyrus was similar between CUD and EC, thinner than the expected values of YC. We also found that regarding CT of the right inferior temporal gyrus, right inferior parietal cortex, and left superior parietal cortex, the CUD group exhibited parameters that fell in between EC and YC groups. Finally, CT of the right pars triangularis bordering with orbitofrontal gyrus, right superior temporal gyrus, and right precentral gyrus were reduced in CUD when contrasted with YC, but those areas were unrelated to CT of EC. Despite the 50-year age gap between our age groups, CT of tricenarian cocaine users assembles features of an octogenarian brain, reinforcing the accelerated aging hypothesis in CUD.

Is vestibular function related to human hippocampal volume?

BACKGROUND

Recent studies implicate the effect of vestibular loss on cognitive decline, including hippocampal volume loss. As hippocampal atrophy is an important biomarker of Alzheimer's disease, exploring vestibular dysfunction as a risk factor for dementia and its role in hippocampal atrophy is of interest.

OBJECTIVE

To replicate previous literature on whole-brain and hippocampal volume in semicircular canal dysfunction (bilateral vestibulopathy; BV) and explore the association between otolith function and hippocampal volume.

METHODS

Hippocampal and whole-brain MRI volumes were compared in adults aged between 55 and 83 years. Participants with BV (n = 16) were compared to controls individually matched on age, sex, and hearing status (n = 16). Otolith influence on hippocampal volume in preserved semicircular canal function was evaluated (n = 34).

RESULTS

Whole-brain and targeted hippocampal approaches using volumetric and surface-based measures yielded no significant differences when comparing BV to controls. Binary support vector machines were unable to classify inner ear health status above chance level. Otolith parameters were not associated with hippocampal volume in preserved semicircular canal function.

CONCLUSIONS

No significant differences in whole-brain or hippocampal volume were found when comparing BV participants with healthy controls. Saccular parameters in subjects with preserved semicircular canal function were not associated with hippocampal volume changes.

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.

oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.

Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease

This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.

Cortical thickness in the right medial frontal gyrus predicts planning performance in healthy children and adolescents

The ability to plan is an important part of the set of the cognitive skills called "executive functions." To be able to plan actions in advance is of great importance in everyday life and constitutes one of the major key features for academic as well as economic success. The present study aimed to investigate the neuroanatomical correlates of planning in normally developing children, as measured by the cortical thickness of the prefrontal cortex. Eighteen healthy children and adolescents underwent structural MRI examinations and the Tower of London (ToL) task. A multiple regression analysis revealed that the cortical thickness of the right caudal middle frontal gyrus (cMFG) was a significant predictor of planning performance. Neither the cortical thickness of any other prefrontal area nor gender were significantly associated with performance in the ToL task. The results of the present exploratory study suggest that the cortical thickness of the right, but not the left cMFG, is positively correlated with performance in the ToL task. We, therefore, conclude that increased cortical thickness may be more beneficial for higher-order processes, such as information integration, than for lower-order processes, such as the analysis of external information.