Source-based morphometry: a decade of covarying structural brain patterns

Relationship between brain white matter damage and grey matter atrophy in hereditary spastic paraplegia types 4 and 5

BACKGROUND AND PURPOSE

White matter (WM) damage is the main target of hereditary spastic paraplegia (HSP), but mounting evidence indicates that genotype-specific grey matter (GM) damage is not uncommon. Our aim was to identify and compare brain GM and WM damage patterns in HSP subtypes and investigate how gene expression contributes to these patterns, and explore the relationship between GM and WM damage.

METHODS

In this prospective single-centre cohort study from 2019 to 2022, HSP patients and controls underwent magnetic resonance imaging evaluations. The alterations of GM and WM patterns were compared between groups by applying a source-based morphometry approach. Spearman rank correlation was used to explore the associations between gene expression and GM atrophy patterns in HSP subtypes. Mediation analysis was conducted to investigate the interplay between GM and WM damage.

RESULTS

Twenty-one spastic paraplegia type 4 (SPG4) patients (mean age 50.7 years ± 12.0 SD, 15 men), 21 spastic paraplegia type 5 (SPG5) patients (mean age 29.1 years ± 12.8 SD, 14 men) and 42 controls (sex- and age-matched) were evaluated. Compared to controls, SPG4 and SPG5 showed similar WM damage but different GM atrophy patterns. GM atrophy patterns in SPG4 and SPG5 were correlated with corresponding gene expression (ρ = 0.30, p = 0.008, ρ = 0.40, p < 0.001, respectively). Mediation analysis indicated that GM atrophy patterns were mediated by WM damage in HSP.

CONCLUSIONS

Grey matter atrophy patterns were distinct between SPG4 and SPG5 and were not only secondary to WM damage but also associated with disease-related gene expression.

CLINICAL TRIAL REGISTRATION NO

NCT04006418.

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder

Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance personalized suicide control efforts. This study used unstructured psychiatric charts and brain magnetic resonance imaging (MRI) records from a psychiatric outpatient clinic to develop a machine learning-based suicidal thought classification model. The study included 152 patients with new depressive episodes for development and 58 patients from a geographically different hospital for validation. We developed an eXtreme Gradient Boosting (XGBoost)-based classification models according to the combined types of data: independent components-map weightings from brain T1-weighted MRI and topic probabilities from clinical notes. Specifically, we used 5 psychiatric symptom topics and 5 brain networks for models. Anxiety and somatic symptoms topics were significantly more common in the suicidal group, and there were group differences in the default mode and cortical midline networks. The clinical symptoms plus structural brain patterns model had the highest area under the receiver operating characteristic curve (0.794) versus the clinical notes only and brain MRI only models (0.748 and 0.738, respectively). The results were consistent across performance metrics and external validation. Our findings suggest that focusing on personalized neuroimaging and natural language processing variables improves evaluation of suicidal thoughts.

Gray matter morphometric biomarkers for distinguishing manganese-exposed welders from healthy adults revealed by source-based morphometry

BACKGROUND

Chronic overexposure to manganese (Mn) may result in neurotoxicity, which is characterized by motor and cognitive dysfunctions. This study aimed to utilize multivariate source-based morphometry (SBM) to explore the biomarkers for distinguishing Mn-exposed welders from healthy controls (HCs).

METHODS

High-quality 3D T1-weighted MRI scans were obtained from 45 Mn-exposed full-time welders and 33 age-matched HCs in this study. After extracting gray matter structural covariation networks by SBM, multiple classic interaction linear models were applied to investigate distinct patterns in welders compared to HCs, and Z-transformed loading coefficients were compared between the two groups. A receiver operating characteristic (ROC) curve was used to identify potential biomarkers for distinguishing Mn-exposed welders from HCs. Additionally, we assessed the relationships between clinical features and gray matter volumes in the welders group.

RESULTS

A total of 78 subjects (45 welders, mean age 46.23±4.93 years; 33 HCs, mean age 45.55±3.40 years) were evaluated. SBM identified five components that differed between the groups. These components displayed lower loading weights in the basal ganglia, thalamus, default mode network (including the lingual gyrus and precuneus), and temporal lobe network (including the temporal pole and parahippocampus), as well as higher loading weights in the sensorimotor network (including the supplementary motor cortex). ROC analysis identified the highest classification power in the thalamic network.

CONCLUSIONS

Altered brain structures might be implicated in Mn overexposure-related disturbances in motivative modulation, cognitive control and information integration. These results encourage further studies that focus on the interaction mechanisms, including the basal ganglia network, thalamic network and default mode network. Our study identified potential neurobiological markers in Mn-exposed welders and illustrated the utility of a multivariate method of gray matter analysis.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease

OBJECTIVE

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI).

METHODS

We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population.

RESULTS

The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier.

INTERPRETATION

Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Gray matter atrophy and white matter lesions burden in delayed cognitive decline following carbon monoxide poisoning

Gray matter (GM) atrophy and white matter (WM) lesions may contribute to cognitive decline in patients with delayed neurological sequelae (DNS) after carbon monoxide (CO) poisoning. However, there is currently a lack of evidence supporting this relationship. This study aimed to investigate the volume of GM, cortical thickness, and burden of WM lesions in 33 DNS patients with dementia, 24 DNS patients with mild cognitive impairment, and 51 healthy controls. Various methods, including voxel-based, deformation-based, surface-based, and atlas-based analyses, were used to examine GM structures. Furthermore, we explored the connection between GM volume changes, WM lesions burden, and cognitive decline. Compared to the healthy controls, both patient groups exhibited widespread GM atrophy in the cerebral cortices (for volume and cortical thickness), subcortical nuclei (for volume), and cerebellum (for volume) (p < .05 corrected for false discovery rate [FDR]). The total volume of GM atrophy in 31 subregions, which included the default mode network (DMN), visual network (VN), and cerebellar network (CN) (p < .05, FDR-corrected), independently contributed to the severity of cognitive impairment (p < .05). Additionally, WM lesions impacted cognitive decline through both direct and indirect effects, with the latter mediated by volume reduction in 16 subregions of cognitive networks (p < .05). These preliminary findings suggested that both GM atrophy and WM lesions were involved in cognitive decline in DNS patients following CO poisoning. Moreover, the reduction in the volume of DMN, VN, and posterior CN nodes mediated the WM lesions-induced cognitive decline.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Progressive increase of brain gray matter volume in individuals with regular soccer training

The study aimed to investigate alterations in gray matter volume in individuals undergoing regular soccer training, using high-resolution structural data, while also examining the temporal precedence of such structural alterations. Both voxel-based morphometry and source-based morphometry (SBM) methods were employed to analyze volumetric changes in gray matter between the soccer and control groups. Additionally, a causal network of structural covariance (CaSCN) was built using granger causality analysis on brain structural data ordering by training duration. Significant increases in gray matter volume were observed in the cerebellum in the soccer group. Additionally, the results of the SBM analysis revealed significant increases in gray matter volume in the calcarine and thalamus of the soccer group. The analysis of CaSCN demonstrated that the thalamus had a prominent influence on other brain regions in the soccer group, while the calcarine served as a transitional node, and the cerebellum acted as a prominent node that could be easily influenced by other brain regions. In conclusion, our study identified widely affected regions with increased gray matter volume in individuals with regular soccer training. Furthermore, a temporal precedence relationship among these regions was observed.

Brain connectivity for subtypes of parkinson's disease using structural MRI

Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.

Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Distinct developmental changes in regional gray matter volume and covariance in individuals with attention-deficit hyperactivity disorder: A longitudinal voxel-based morphometry study

BACKGROUND

Very few studies have investigated longitudinal clinical cohorts of attention-deficit/hyperactivity disorder (ADHD). Moreover, how baseline brain changes could affect the development of ADHD symptoms later in life remains elusive. Therefore, we aimed to fill this gap by exploring brain and clinical changes in youth with ADHD using a longitudinal design.

METHODS

This prospective study consisted of 74 children and adolescents with ADHD and 50 age-, sex-, intelligence-matched typically developing controls (TDC), evaluated at baseline (aged 7-19 years) and re-evaluated 5.3 years later (a mean follow-up latency). We applied voxel-based morphometry to characterize brain structures, followed by both mass-univariate and multivariate structural covariance statistics to identify brain regions with significant diagnosis-by-time interactions from late childhood/adolescence to early adulthood. We used the cross-lagged panel model to investigate the longitudinal association between structural brain metrics and core ADHD symptoms.

RESULTS

The mass-univariate statistic revealed significant diagnosis-by-time interactions in the right striatum and the sixth lobule of the cerebellum. This was expressed by increased striatal and decreased cerebellar volume in ADHD, while TDC showed inverse volume changes over time. The multivariate method showed significant diagnosis-by-time interactions in a structural covariance network consisting of the regions involved in the functional sensory-motor and default-mode networks. Higher baseline right striatal and cerebellar volumes were associated with elevated ADHD symptoms at follow-up.

CONCLUSIONS

Our findings suggest a temporal association between the divergent development of striatal and cerebellar regions and dynamical ADHD phenotypic expression through young adulthood. These results highlight a potential brain marker of future outcomes.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

Neuroanatomical asymmetry in the canine brain

The brains of humans and non-human primates exhibit left/right asymmetries in grey matter morphology, white matter connections, and functional responses. These asymmetries have been implicated in specialized behavioral adaptations such as language, tool use, and handedness. Left/right asymmetries are also observed in behavioral tendencies across the animal kingdom, suggesting a deep evolutionary origin for the neural mechanisms underlying lateralized behavior. However, it is still unclear to what extent brain asymmetries supporting lateralized behaviors are present in other large-brained animals outside the primate order. Canids and other carnivorans evolved large, complex brains independently and convergently with primates, and exhibit lateralized behaviors. Therefore, domestic dogs offer an opportunity to address this question. We examined T2-weighted MRI images of 62 dogs from 33 breeds, opportunistically collected from a veterinary MRI scanner from dogs who were referred for neurological examination but were not found to show any neuropathology. Volumetrically asymmetric regions of gray matter included portions of the temporal and frontal cortex, in addition to portions of the cerebellum, brainstem, and other subcortical regions. These results are consistent with the perspective that asymmetry may be a common feature underlying the evolution of complex brains and behavior across clades, and provide neuro-organizational information that is likely relevant to the growing field of canine behavioral neuroscience.

Differential alterations in brain structural network organization during addiction between adolescents and adults

BACKGROUND

The adolescent brain may be susceptible to the influences of illicit drug use. While compensatory network reorganization is a unique developmental characteristic that may restore several brain disorders, its association with methamphetamine (MA) use-induced damage during adolescence is unclear.

METHODS

Using independent component (IC) analysis on structural magnetic resonance imaging data, spatially ICs described as morphometric networks were extracted to examine the effects of MA use on gray matter (GM) volumes and network module connectivity in adolescents (51 MA users v. 60 controls) and adults (54 MA users v. 60 controls).

RESULTS

MA use was related to significant GM volume reductions in the default mode, cognitive control, salience, limbic, sensory and visual network modules in adolescents. GM volumes were also reduced in the limbic and visual network modules of the adult MA group as compared to the adult control group. Differential patterns of structural connectivity between the basal ganglia (BG) and network modules were found between the adolescent and adult MA groups. Specifically, adult MA users exhibited significantly reduced connectivity of the BG with the default network modules compared to control adults, while adolescent MA users, despite the greater extent of network GM volume reductions, did not show alterations in network connectivity relative to control adolescents.

CONCLUSIONS

Our findings suggest the potential of compensatory network reorganization in adolescent brains in response to MA use. The developmental characteristic to compensate for MA-induced brain damage can be considered as an age-specific therapeutic target for adolescent MA users.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Brain structural abnormalities in adult major depressive disorder revealed by voxel- and source-based morphometry: evidence from the REST-meta-MDD Consortium

BACKGROUND

Neuroimaging studies on major depressive disorder (MDD) have identified an extensive range of brain structural abnormalities, but the exact neural mechanisms associated with MDD remain elusive. Most previous studies were performed with voxel- or surface-based morphometry which were univariate methods without considering spatial information across voxels/vertices.

METHODS

Brain morphology was investigated using voxel-based morphometry (VBM) and source-based morphometry (SBM) in 1082 MDD patients and 990 healthy controls (HCs) from the REST-meta-MDD Consortium. We first examined group differences in regional grey matter (GM) volumes and structural covariance networks between patients and HCs. We then compared first-episode, drug-naïve (FEDN) patients, and recurrent patients. Additionally, we assessed the effects of symptom severity and illness duration on brain alterations.

RESULTS

VBM showed decreased GM volume in various regions in MDD patients including the superior temporal cortex, anterior and middle cingulate cortex, inferior frontal cortex, and precuneus. SBM returned differences only in the prefrontal network. Comparisons between FEDN and recurrent MDD patients showed no significant differences by VBM, but SBM showed greater decreases in prefrontal, basal ganglia, visual, and cerebellar networks in the recurrent group. Moreover, depression severity was associated with volumes in the inferior frontal gyrus and precuneus, as well as the prefrontal network.

CONCLUSIONS

Simultaneous application of VBM and SBM methods revealed brain alterations in MDD patients and specified differences between recurrent and FEDN patients, which tentatively provide an effective multivariate method to identify potential neurobiological markers for depression.

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be "hardwired" to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level.

Prediction of gender from longitudinal MRI data via deep learning on adolescent data reveals unique patterns associated with brain structure and change over a two-year period

Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs > 200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.

World Trade Center Site Exposure Duration Is Associated with Hippocampal and Cerebral White Matter Neuroinflammation

Responders to the World Trade Center (WTC) attacks on 9/11/2001 inhaled toxic dust and experienced severe trauma for a prolonged period. Studies report that WTC site exposure duration is associated with peripheral inflammation and risk for developing early-onset dementia (EOD). Free Water Fraction (FWF) can serve as a biomarker for neuroinflammation by measuring in vivo movement of free water across neurons. The present case-controlled study aimed to examine associations between WTC site exposure duration as well as EOD status with increased hippocampal and cerebral neuroinflammation. Ninety-nine WTC responders (mean age of 56) were recruited between 2017 and 2019 (N = 48 with EOD and 51 cognitively unimpaired). Participants were matched on age, sex, occupation, race, education, and post-traumatic stress disorder (PTSD) status. Participants underwent neuroimaging using diffusion tensor imaging protocols for FWF extraction. Region of interest (ROI) analysis and correlational tractography explored topographical distributions of FWF associations. Apolipoprotein-e4 allele (APOEε4) status was available for most responders (N = 91). Hippocampal FWF was significantly associated with WTC site exposure duration (r = 0.30, p = 0.003), as was cerebral white matter FWF (r = 0.20, p = 0.044). ROI analysis and correlational tractography identified regions within the limbic, frontal, and temporal lobes. Hippocampal FWF and its association with WTC exposure duration were highest when the APOEε4 allele was present (r = 0.48, p = 0.039). Our findings demonstrate that prolonged WTC site exposure is associated with increased hippocampal and cerebral white matter neuroinflammation in WTC responders, possibly exacerbated by possession of the APOEε4 allele.

Associations between levels of Internet Gaming Disorder symptoms and striatal morphology-replication and associations with social anxiety

Background

Brain structural alterations of the striatum have been frequently observed in internet gaming disorder (IGD); however, the replicability of the results and the associations with social-affective dysregulations such as social anxiety remain to be determined.

Methods

The present study combined a dimensional neuroimaging approach with both voxel-wise and data-driven multivariate approaches to (i) replicate our previous results on a negative association between IGD symptom load (assessed by the Internet Gaming Disorder Scale-Short Form) and striatal volume, (ii) extend these findings to female individuals, and (iii) employ multivariate and mediation models to determine common brain structural representations of IGD and social anxiety (assessed by the Liebowitz Social Anxiety Scale).

Results

In line with the original study, the voxel-wise analyses revealed a negative association between IGD and volumes of the bilateral caudate. Going beyond the earlier study investigating only male participants, the present study demonstrates that the association in the right caudate was comparable in both the male and the female subsamples. Further examination using the multivariate approach revealed regionally different associations between IGD and social anxiety with striatal density representations in the dorsal striatum (caudate) and ventral striatum (nucleus accumbens). Higher levels of IGD were associated with higher social anxiety and the association was critically mediated by the multivariate neurostructural density variations of the striatum.

Conclusions

Altered striatal volumes may represent a replicable and generalizable marker of IGD symptoms. However, exploratory multivariate analyses revealed more complex and regional specific associations between striatal density and IGD as well as social anxiety symptoms. Variations in both tendencies may share common structural brain representations, which mediate the association between increased IGD and social anxiety.

Clinical and cortical similarities identified between bipolar disorder I and schizophrenia: A multivariate approach

BACKGROUND

Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.

MATERIALS AND METHODS

We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large (N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.

RESULTS

Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.

CONCLUSION

These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.

Intergenerational concordance of brain structure between depressed mothers and their never-depressed daughters

AIM

Parents have significant genetic and environmental influences, which are known as intergenerational effects, on the cognition, behavior, and brain of their offspring. These intergenerational effects are observed in patients with mood disorders, with a particularly strong association of depression between mothers and daughters. The main purpose of our study was to investigate female-specific intergenerational transmission patterns in the human brain among patients with depression and their never-depressed offspring.

METHODS

We recruited 78 participants from 34 families, which included remitted parents with a history of depression and their never-depressed biological offspring. We used source-based and surface-based morphometry analyses of magnetic resonance imaging data to examine the degree of associations in brain structure between four types of parent-offspring dyads (i.e. mother-daughter, mother-son, father-daughter, and father-son).

RESULTS

Using independent component analysis, we found a significant positive correlation of gray matter structure between exclusively the mother-daughter dyads within brain regions located in the default mode and central executive networks, such as the bilateral anterior cingulate cortex, posterior cingulate cortex, precuneus, middle frontal gyrus, middle temporal gyrus, superior parietal lobule, and left angular gyrus. These similar observations were not identified in other three parent-offspring dyads.

CONCLUSIONS

The current study provides biological evidence for greater vulnerability of daughters, but not sons, in developing depression whose mothers have a history of depression. Our findings extend our knowledge on the pathophysiology of major psychiatric conditions that show sex biases and may contribute to the development of novel interventions targeting high-risk individuals.

Neuroanatomical correlates of screening for aphasia in NeuroDegeneration (SAND) battery in non-fluent/agrammatic variant of primary progressive aphasia

Background

Non-fluent/agrammatic variant of Primary Progressive Aphasia (avPPA) is primarily characterized by language impairment due to atrophy of the inferior frontal gyrus and the insula cortex in the dominant hemisphere. The Screening for Aphasia in NeuroDegeneration (SAND) battery has been recently proposed as a screening tool for PPA, with several tasks designed to be specific for different language features. Applying multivariate approaches to neuroimaging data and verbal fluency tasks, Aachener Aphasie Test (AAT) naming subtest and SAND data may help in elucidating the neuroanatomical correlates of language deficits in avPPA.

Objective

To investigate the neuroanatomical correlates of language deficits in avPPA using verbal fluency tasks, AAT naming subtest and SAND scores as proxies of brain structural imaging abnormalities.

Methods

Thirty-one avPPA patients were consecutively enrolled and underwent extensive neuropsychological assessment and MRI scan. Raw scores of verbal fluency tasks, AAT naming subtest, and SAND subtests, namely living and non-living picture naming, auditory sentence comprehension, single-word comprehension, words and non-words repetition and sentence repetition, were used as proxies to explore structural (gray matter volume) neuroanatomical correlates. We assessed univariate (voxel-based morphometry, VBM) as well as multivariate (source-based morphometry, SBM) approaches. Age, gender, educational level, and disease severity were considered nuisance variables.

Results

SAND picture naming (total, living and non-living scores) and AAT naming scores showed a direct correlation with the left temporal network derived from SBM. At univariate analysis, the left middle temporal gyrus was directly correlated with SAND picture naming (total and non-living scores) and AAT naming score. When words and non-words repetition (total score) was considered, a direct correlation with the left temporal network (SBM) and with the left fusiform gyrus (VBM) was also evident.

Conclusion

Naming impairments that characterize avPPA are related to specific network-based involvement of the left temporal network, potentially expanding our knowledge on the neuroanatomical basis of this neurodegenerative condition.

Abnormal cortical morphology in children and adolescents with intermittent exotropia

Purpose

To investigate cortical differences, age-related cortical differences, and structural covariance differences between children with intermittent exotropia (IXT) and healthy controls (HCs) using high-resolution magnetic resonance imaging (MRI).

Methods

Sixteen IXT patients and 16 HCs underwent MRI using a 3-T MR scanner. FreeSurfer software was used to obtain measures of cortical volume, thickness, and surface area. Group differences in cortical thickness, volume and surface area were examined using a general linear model with intracranial volume (ICV), age and sex as covariates. Then, the age-related cortical differences between the two groups and structural covariance in abnormal morphometric changes were examined.

Results

Compared to HCs, IXT patients demonstrated significantly decreased surface area in the left primary visual cortex (PVC), and increased surface area in the left inferior temporal cortex (ITC). We also found increased cortical thickness in the left orbitofrontal cortex (OFC), right middle temporal cortex (MT), and right inferior frontal cortex (IFC). No significant differences were found in cortical volume between the two groups. There were several negative correlations between neuroanatomic measurements and age in the HC group that were not observed in the IXT group. In addition, we identified altered patterns of structural correlations across brain regions in patients with IXT.

Conclusion

To our knowledge, this study is the first to characterize the cortical morphometry of the children and adolescents with IXT. Based on our results, children and adolescents with IXT exhibited significant alterations in the PVC and association cortices, different cortical morphometric development patterns, and disrupted structural covariance across brain regions.

Cortical structural changes after subcortical stroke: Patterns and correlates

Subcortical ischemic stroke can lead to persistent structural changes in the cerebral cortex. The evolution of cortical structural changes after subcortical stroke is largely unknown, as are their relations with motor recovery, lesion location, and early impairment of specific subsets of fibers in the corticospinal tract (CST). In this observational study, cortical structural changes were compared between 181 chronic patients with subcortical stroke involving the motor pathway and 113 healthy controls. The impacts of acute lesion location and early impairments of specific CSTs on cortical structural changes were investigated in the patients by combining voxel-based correlation analysis with an association study that compared CST damage and cortical structural changes. Longitudinal patterns of cortical structural change were explored in a group of 81 patients with subcortical stroke using a linear mixed-effects model. In the cross-sectional analyses, patients with partial recovery showed more significant reductions in cortical thickness, surface area, or gray matter volume in the sensorimotor cortex, cingulate gyrus, and gyrus rectus than did patients with complete recovery; however, patients with complete recovery demonstrated more significant increases in the cortical structural measures in frontal, temporal, and occipital regions than did patients with partial recovery. Voxel-based correlation analysis in these patients showed that acute stroke lesions involving the CST fibers originating from the primary motor cortex were associated with cortical thickness reductions in the ipsilesional motor cortex in the chronic stage. Acute stroke lesions in the putamen were correlated with increased surface area in the temporal pole in the chronic stage. The early impairment of the CST fibers originating from the primary sensory area was associated with increased cortical thickness in the occipital cortex. In the longitudinal analyses, patients with partial recovery showed gradually reduced cortical thickness, surface area, and gray matter volume in brain regions with significant structural damage in the chronic stage. Patients with complete recovery demonstrated gradually increasing cortical thickness, surface area, and gray-matter volume in the frontal, temporal, and occipital regions. The directions of slow structural changes in the frontal, occipital, and cingulate cortices were completely different between patients with partial and complete recovery. Complex cortical structural changes and their dynamic evolution patterns were different, even contrasting, in patients with partial and complete recovery, and were associated with lesion location and with impairment of specific CST fiber subsets.

Deep multimodal predictome for studying mental disorders

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

Phenotypic and genetic associations between gray matter covariation and tool use skill in chimpanzees (Pan troglodytes): Repeatability in two genetically isolated populations

Humans and chimpanzees both exhibit a diverse set of tool use skills which suggests selection for tool manufacture and use occurred in the common ancestors of the two species. Our group has previously reported phenotypic and genetic associations between tool use skill and gray matter covariation, as quantified by source-based morphometry (SBM), in chimpanzees. As a follow up study, here we evaluated repeatability in heritability in SBM components and their phenotypic association with tool use skill in two genetically independent chimpanzee cohorts. Within the two independent cohorts of chimpanzees, we identified 8 and 16 SBM components, respectively. Significant heritability was evident for multiple SBM components within both cohorts. Further, phenotypic associations between tool use performance and the SBM components were largely consistent between the two cohorts; the most consistent finding being an association between tool use performance and an SBM component including the posterior superior temporal sulcus (STS) and superior temporal gyrus (STG), and the interior and superior parietal regions (p< 0.05). These findings indicate that the STS, STG, and parietal cortices are phenotypically and genetically implicated in chimpanzee tool use abilities.

Decreased gray matter volume is associated with theory of mind deficit in adolescents with schizophrenia

Schizophrenia patients often suffer from deficit in theory of mind (TOM). Prior neuroimaging studies revealed neuroimaging correlates of TOM deficit in adults with schizophrenia, neuroimaging correlates of TOM in adolescents is less well established. This study aimed to investigate gray matter volume (GMV) abnormalities and TOM deficits in schizophrenic adolescents, and examine the relationship between them. Twenty adolescent schizophrenic patients and 25 age, sex-matched healthy controls underwent T1-weighted magnetic resonance imaging (MRI) scans, and were examined for TOM based on the Reading the Mind in the Eyes test (RMET). Univariate voxel-based morphometry (VBM) and multivariate source-based morphometry (SBM) were employed to examine alterations of two GMV phenotypes in schizophrenic adolescents: voxel-wise GMV and covarying structural brain patterns (SBPs). Compared with controls, our results revealed a significant deficit in RMET performance of the patients, Voxel-wise VBM analysis revealed that patients exhibited decreased GMV in bilateral insula, orbitofrontal cortex, and right rolandic operculum, and GMV of these brain regions were positively correlated with RMET performance. Multivariate SBM analysis identified a significantly different between-group SBP comprising of bilateral insula and inferior frontal cortex, bilateral superior temporal cortex, and bilateral lateral parietal cortex and right rolandic operculum. The loading scores of this SBP was positively correlated with RMET performance. This study revealed impairment of TOM ability in schizophrenic adolescents and revealed an association between TOM deficit and decreased GMV in regions which are crucial for social cognition, thereby provided insight and possible target regions for understanding the neural pathology and normalizing TOM deficit in adolescent schizophrenia patients.

Structural and functional brain networks of individual differences in trait anger and anger control: An unsupervised machine learning study

The ability to experience, use and eventually control anger is crucial to maintain well-being and build healthy relationships. Despite its relevance, the neural mechanisms behind individual differences in experiencing and controlling anger are poorly understood. To elucidate these points, we employed an unsupervised machine learning approach based on independent component analysis to test the hypothesis that specific functional and structural networks are associated with individual differences in trait anger and anger control. Structural and functional resting state images of 71 subjects as well as their scores from the State-Trait Anger Expression Inventory entered the analyses. At a structural level, the concentration of grey matter in a network including ventromedial temporal areas, posterior cingulate, fusiform gyrus and cerebellum was associated with trait anger. The higher the concentration, the higher the proneness to experience anger in daily life due to the greater tendency to orient attention towards aversive events and interpret them with higher hostility. At a functional level, the activity of the default mode network (DMN) was associated with anger control. The higher the DMN temporal frequency, the stronger the exerted control over anger, thus extending previous evidence on the role of the DMN in regulating cognitive and emotional functions in the domain of anger. Taken together, these results show, for the first time, two specialized brain networks for encoding individual differences in trait anger and anger control.

Structural Covariance of Depth-Dependent Intracortical Myelination in the Human Brain and Its Application to Drug-Naïve Schizophrenia: A T1w/T2w MRI Study

Aberrations in intracortical myelination are increasingly being considered as a cardinal feature in the pathophysiology of schizophrenia. We investigated the network-level distribution of intracortical myelination across various cortex depths. We enrolled 126 healthy subjects and 106 first-episode drug-naïve schizophrenia patients. We used T1w/T2w ratio as a proxy of intracortical myelination, parcellated cortex into several equivolumetric surfaces based on cortical depths and mapped T1w/T2w ratios to each surface. Non-negative matrix factorization was used to generate depth-dependent structural covariance networks (dSCNs) of intracortical myelination from 2 healthy controls datasets-one from our study and another from 100-unrelated dataset of the Human Connectome Project. For patient versus control comparisons, partial least squares approach was used; we also related myelination to clinical features of schizophrenia. We found that dSCNs were highly reproducible in 2 independent samples. Network-level myelination was reduced in prefrontal and cingulate cortex and increased in perisylvian cortex in schizophrenia. The abnormal network-level myelination had a canonical correlation with symptom burden in schizophrenia. Moreover, myelination of prefrontal cortex correlated with duration of untreated psychosis. In conclusion, we offer a feasible and sensitive framework to study depth-dependent myelination and its relationship with clinical features.

Gray matter volume covariance networks are associated with altered emotional processing in bipolar disorder: a source-based morphometry study

Widespread regional gray matter volume (GMV) alterations have been reported in bipolar disorder (BD). Structural networks, which are thought to better reflect the complex multivariate organization of the brain, and their clinical and psychological function have not been investigated yet in BD. 24 patients with BD type-I (BD-I), and 30 with BD type-II (BD-II), and 45 controls underwent MRI scan. Voxel-based morphometry and source-based morphometry (SBM) were performed to extract structural covariation patterns of GMV. SBM components associated with morphometric differences were compared among diagnoses. Executive function and emotional processing correlated with morphometric characteristics. Compared to controls, BD-I showed reduced GMV in the temporo-insular-parieto-occipital cortex and in the culmen. An SBM component spanning the prefrontal-temporal-occipital network exhibited significantly lower GMV in BD-I compared to controls, but not between the other groups. The structural network covariance in BD-I was associated with the number of previous manic episodes and with worse executive performance. Compared to BD-II, BD-I showed a loss of GMV in the temporal-occipital regions, and this was correlated with impaired emotional processing. Altered prefrontal-temporal-occipital network structure could reflect a neural signature associated with visuospatial processing and problem-solving impairments as well as emotional processing and illness severity in BD-I.

Investigating sexual dimorphism in human brain structure by combining multiple indexes of brain morphology and source-based morphometry

Computational morphometry of magnetic resonance images represents a powerful tool for studying macroscopic differences in human brains. In the present study (N participants = 829), we combined different techniques and measures of brain morphology to investigate one of the most compelling topics in neuroscience: sexual dimorphism in human brain structure. When accounting for overall larger male brains, results showed limited sex differences in gray matter volume (GMV) and surface area. On the other hand, we found larger differences in cortical thickness, favoring both males and females, arguably as a result of region-specific differences. We also observed higher values of fractal dimension, a measure of cortical complexity, for males versus females across the four lobes. In addition, we applied source-based morphometry, an alternative method for measuring GMV based on the independent component analysis. Analyses on independent components revealed higher GMV in fronto-parietal regions, thalamus and caudate nucleus for females, and in cerebellar- temporal cortices and putamen for males, a pattern that is largely consistent with previous findings.

Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes

OBJECTIVE

In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS).

METHODS

We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening.

RESULTS

We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05).

CONCLUSIONS

The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

Background

Identifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD.

Methods

This study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes.

Results

Source-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2.

Conclusions

Overall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Neuromorphological Changes following Selection for Tameness and Aggression in the Russian Farm-Fox experiment

The Russian farm-fox experiment is an unusually long-running and well-controlled study designed to replicate wolf-to-dog domestication. As such, it offers an unprecedented window onto the neural mechanisms governing the evolution of behavior. Here we report evolved changes to gray matter morphology resulting from selection for tameness versus aggressive responses toward humans in a sample of 30 male fox brains. Contrasting with standing ideas on the effects of domestication on brain size, tame foxes did not show reduced brain volume. Rather, gray matter volume in both the tame and aggressive strains was increased relative to conventional farm foxes bred without deliberate selection on behavior. Furthermore, tame- and aggressive-enlarged regions overlapped substantially, including portions of motor, somatosensory, and prefrontal cortex, amygdala, hippocampus, and cerebellum. We also observed differential morphologic covariation across distributed gray matter networks. In one prefrontal-cerebellum network, this covariation differentiated the three populations along the tame-aggressive behavioral axis. Surprisingly, a prefrontal-hypothalamic network differentiated the tame and aggressive foxes together from the conventional strain. These findings indicate that selection for opposite behaviors can influence brain morphology in a similar way.SIGNIFICANCE STATEMENT Domestication represents one of the largest and most rapid evolutionary shifts of life on earth. However, its neural correlates are largely unknown. Here we report the neuroanatomical consequences of selective breeding for tameness or aggression in the seminal Russian farm-fox experiment. Compared with a population of conventional farm-bred control foxes, tame foxes show neuroanatomical changes in the PFC and hypothalamus, paralleling wolf-to-dog shifts. Surprisingly, though, aggressive foxes also show similar changes. Moreover, both strains show increased gray matter volume relative to controls. These results indicate that similar brain adaptations can result from selection for opposite behavior, that existing ideas of brain changes in domestication may need revision, and that significant neuroanatomical change can evolve very quickly, within the span of <100 generations.

Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

Cannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

Interhemispheric co-alteration of brain homotopic regions

Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.

Microstructural plasticity in nociceptive pathways after spinal cord injury

OBJECTIVE

To track the interplay between (micro-) structural changes along the trajectories of nociceptive pathways and its relation to the presence and intensity of neuropathic pain (NP) after spinal cord injury (SCI).

METHODS

A quantitative neuroimaging approach employing a multiparametric mapping protocol was used, providing indirect measures of myelination (via contrasts such as magnetisation transfer (MT) saturation, longitudinal relaxation (R1)) and iron content (via effective transverse relaxation rate (R2*)) was used to track microstructural changes within nociceptive pathways. In order to characterise concurrent changes along the entire neuroaxis, a combined brain and spinal cord template embedded in the statistical parametric mapping framework was used. Multivariate source-based morphometry was performed to identify naturally grouped patterns of structural variation between individuals with and without NP after SCI.

RESULTS

In individuals with NP, lower R1 and MT values are evident in the primary motor cortex and dorsolateral prefrontal cortex, while increases in R2* are evident in the cervical cord, periaqueductal grey (PAG), thalamus and anterior cingulate cortex when compared with pain-free individuals. Lower R1 values in the PAG and greater R2* values in the cervical cord are associated with NP intensity.

CONCLUSIONS

The degree of microstructural changes across ascending and descending nociceptive pathways is critically implicated in the maintenance of NP. Tracking maladaptive plasticity unravels the intimate relationships between neurodegenerative and compensatory processes in NP states and may facilitate patient monitoring during therapeutic trials related to pain and neuroregeneration.

Genetic factors influencing a neurobiological substrate for psychiatric disorders

A retrospective meta-analysis of magnetic resonance imaging voxel-based morphometry studies proposed that reduced gray matter volumes in the dorsal anterior cingulate and the left and right anterior insular cortex-areas that constitute hub nodes of the salience network-represent a common substrate for major psychiatric disorders. Here, we investigated the hypothesis that the common substrate serves as an intermediate phenotype to detect genetic risk variants relevant for psychiatric disease. To this end, after a data reduction step, we conducted genome-wide association studies of a combined common substrate measure in four population-based cohorts (n = 2271), followed by meta-analysis and replication in a fifth cohort (n = 865). After correction for covariates, the heritability of the common substrate was estimated at 0.50 (standard error 0.18). The top single-nucleotide polymorphism (SNP) rs17076061 was associated with the common substrate at genome-wide significance and replicated, explaining 1.2% of the common substrate variance. This SNP mapped to a locus on chromosome 5q35.2 harboring genes involved in neuronal development and regeneration. In follow-up analyses, rs17076061 was not robustly associated with psychiatric disease, and no overlap was found between the broader genetic architecture of the common substrate and genetic risk for major depressive disorder, bipolar disorder, or schizophrenia. In conclusion, our study identified that common genetic variation indeed influences the common substrate, but that these variants do not directly translate to increased disease risk. Future studies should investigate gene-by-environment interactions and employ functional imaging to understand how salience network structure translates to psychiatric disorder risk.

Common and different gray and white matter alterations in bipolar and borderline personality disorder: A source-based morphometry study

According to the nosological classification, Bipolar Disorder (BD) and Borderline Personality Disorder (BPD) are different syndromes. However, these pathological conditions share a number of affective symptoms that make the diagnosis difficult. Affective symptoms range from abnormal mood swings, characterizing both BD and BPD, to regulation dysfunctions, more specific to BPD. To shed light on the neural bases of these aspects, and to better understand differences and similarities between the two disorders, we analysed for the first time gray and white matter features of both BD and BPD. Structural T1 images from 30 patients with BD, 20 with BPD, and 45 controls were analysed by capitalizing on an innovative whole-brain multivariate method known as Source-based Morphometry. Compared to controls, BD patients showed increased gray matter concentration (p = .003) in a network involving mostly subcortical structures and cerebellar areas, possibly related to abnormal mood experiences. Notably, BPD patients showed milder alterations in the same circuit, standing in the middle of a continuum between BD and controls. In addition to this, we found an altered white matter network specific to BPD (p = .018), including frontal-parietal and temporal regions possibly associated with dysfunctional top-down emotion regulation. These findings may shed light on a better understanding of affective disturbances behind the two disorders, with BD patients more characterized by abnormalities in neural structures involved in mood oscillations, and BPD by deficits in the cognitive regulation of emotions. These results may help developing better treatments tailored to the specific affective disturbances displayed by these patients.

SS-Detect: Development and Validation of a New Strategy for Source-Based Morphometry in Multiscanner Studies

BACKGROUND AND PURPOSE

Source-based morphometry(SBM) has been used in multicenter studies pooling magnetic resonance imaging data across different scanners to advance the reproducibility of neuroscience research. In the present study, we developed an analysis strategy for Scanner-Specific Detection (SS-Detect) of SBPs in multiscanner studies, and evaluated its performance relative to a conventional strategy.

METHODS

In the first experiment, the SimTB toolbox was used to generate simulated datasets mimicking 20 different scanners with common and scanner-specific SBPs. In the second experiment, we generated one simulated SBP from empirical gray matter volume (GMV) datasets from two different scanners. Moreover, we applied two strategies to compare SBPs between schizophrenia patients' and healthy controls' GMV from two scanners.

RESULTS

The outputs of the conventional strategy were limited to whole-sample-level results across all scanners; the outputs of SS-Detect included whole-sample-level and scanner-specific results. In the first simulation experiment, SS-Detect successfully estimated all simulated SBPs, including the common and scanner-specific SBPs, whereas the conventional strategy detected only some of the whole-sample SBPs. The second simulation experiment showed that both strategies could detect the simulated SBP. Quantitative evaluations of both experiments demonstrated greater accuracy of the SS-Detect in estimating spatial SBPs and subject-specific loading parameters. In the third experiment, SS-Detect detected more significant between-group SBPs, and these SBPs corresponded with the results from voxel-based morphometry analysis, suggesting that SS-Detect has higher sensitivity in detecting between-group differences.

CONCLUSIONS

SS-Detect outperformed the conventional strategy and can be considered advantageous when SBM is applied to a multiscanner study.

An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease

OBJECTIVE

To identify Parkinson's disease with mild cognitive impairment (PD-MCI) through surface-based morphometry (SBM) based machine learning model.

METHODS

93 patients with parkinson's disease (35 PD with normal cognition, 58 PD-MCI) were examined, obtaining 276 SBM variables per subject. 20 healthy control subjects were used as the reference. After extracting features with statistically significance, support vector machine (SVM) model with grid search method was applied to identify patients with PD-MCI. Accuracy, matthews correlation coefficient (MCC), receiver operating characteristic curve (ROC), precision-recall curve (PR), AUC-ROC, AUC-PR and leave-one-out cross validation (LOOCV) strategy were employed for model evaluation.

RESULTS

PD-MCI is characterized by widespread structural abnormality. SVM model with SBM features achieved an accuracy of 80.00% and area under the ROC of 0.86 for identifying PD-MCI. MCC, AUC-PR, and LOOCV classification accuracy were 0.56, 0.89, and 78.08%, respectively.

CONCLUSION

Automatic identification of PD-MCI could be realized by SBM-based machine learning model.

Genetic Contributions to Multivariate Data-Driven Brain Networks Constructed via Source-Based Morphometry

Identifying genetic factors underlying neuroanatomical variation has been difficult. Traditional methods have used brain regions from predetermined parcellation schemes as phenotypes for genetic analyses, although these parcellations often do not reflect brain function and/or do not account for covariance between regions. We proposed that network-based phenotypes derived via source-based morphometry (SBM) may provide additional insight into the genetic architecture of neuroanatomy given its data-driven approach and consideration of covariance between voxels. We found that anatomical SBM networks constructed on ~ 20 000 individuals from the UK Biobank were heritable and shared functionally meaningful genetic overlap with each other. We additionally identified 27 unique genetic loci that contributed to one or more SBM networks. Both GWA and genetic correlation results indicated complex patterns of pleiotropy and polygenicity similar to other complex traits. Lastly, we found genetic overlap between a network related to the default mode and schizophrenia, a disorder commonly associated with neuroanatomic alterations.