Abstract Sex differences are commonly observed in neuroimaging phenotypes and in the risk of brain diseases, yet the underlying genetic mechanisms remain poorly understood. We investigated sex differences in the genetic architecture of 805 neuroimaging phenotypes in 22,950 males and 22,950 females matched for sample size and covariates, and systematically compared sex-stratified with sex-combined genetic analyses. We found eight variant-trait associations with significant sex differences, 235 fine-mapped sex-dominant causal associations, 457 sex-dominant colocalizations with sex hormones, and 96 sex-dominant colocalizations with schizophrenia. Compared with sex-combined analysis, sex-stratified analysis identified 47 new genetic associations, 170 new fine-mapped causal associations, 1,019 new colocalizations with sex hormones, and 191 new colocalizations with schizophrenia. Additionally, sex-stratified analysis improved global heritability and genetic-correlation estimates and enhanced polygenic prediction for certain phenotypes. This work highlights the need to routinely perform sex-stratified genetic association analyses to elucidate sex-specific and sex-shared genetic control of neuroimaging phenotypes and related disorders.

Background and Hypothesis Schizophrenia manifests large heterogeneities in either symptoms or brain abnormalities. However, the neurobiological basis of symptomatic diversity remains poorly understood. We hypothesized that schizophrenia’s diverse symptoms arise from the interplay of structural and functional alterations across multiple brain regions, rather than isolated abnormalities in a single area. Study Design A total of 495 schizophrenia patients and 507 healthy controls from 8 sites were recruited. Five symptomatic dimensions of schizophrenia patients were derived from the Positive and Negative Syndrome Scale. Multivariate canonical correlation analysis was introduced to identify symptom-related multimodal magnetic resonance imaging composite indicators (MRICIs) derived from gray matter volume, functional connectivity strength, and white matter fractional anisotropy. The intergroup differences in MRICIs were compared, and the paired-wise correlations between symptom dimensions and MRICIs were resolved. Finally, K-means clustering was used to identify the underlying biological subtypes of schizophrenia based on MRICIs. Study Results Canonical correlation analysis identified 15 MRICIs in schizophrenia that were specifically contributed by the neuroimaging measures of multiple regions, respectively. These MRICIs can effectively characterize the complexity of symptoms, showing correlations within and across symptom dimensions, and were consistent across both first-episode and chronic patients. Additionally, some of these indicators could moderately differentiate schizophrenia patients from healthy controls. K-means clustering identified 2 schizophrenia subtypes with distinct MRICI profiles and symptom severity. Conclusions Symptom-guided multimodal and multivariate MRICIs could decode the symptom heterogeneity of schizophrenia patients and might be considered as potential biomarkers for schizophrenia.

Early life adversity (ELA) is a robust transdiagnostic risk factor for mental health disorders, yet the neurobiological mechanisms mediating its long-term impact remain poorly understood. Network control theory offers a novel framework for capturing how structural brain networks constrain and support brain dynamics. Controllability increases over development, associates with executive function and mental health, and appears sensitive to environmental insults. Thus, it may reflect a neurobiological mediator between ELA and behavioral outcomes. We tested whether alterations in modal controllability mediate the impact of multidimensional ELA on cognitive and behavioral outcomes in youth, and whether these pathways are shaped by genetic risk for neurodevelopmental conditions. Using data from 7,970 children aged 9-11 years in the Adolescent Brain Cognitive Development (ABCD) Study, we derived five latent ELA dimensions from 67 indicators, and computed polygenic risk scores (PRS) for attention-deficit/hyperactivity disorder (ADHDPRS) and autism spectrum disorder (ASDPRS). Distinct ELA dimensions were associated with increased controllability in medial frontal, frontoparietal, default mode, and motor networks, as well as with externalizing symptoms and impaired crystallized cognition. Controllability partially mediated these associations, and indirect effects were significantly moderated ADHDPRS and ASDPRS. Longitudinal analyses further demonstrated that baseline controllability predicted cognitive performance two years later. These findings delineate a neurodevelopmental cascade linking early adversity and genetic vulnerability to transdiagnostic mental health risk, positioning brain controllability as a promising mechanistic marker and potential target for early intervention.

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

BACKGROUND:Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain predicted age difference (PAD) has emerged as a sensitive biomarker of both sensorimotor and cognitive function after stroke. Our previous study showed a higher global brain PAD associated with poorer motor function after stroke. However, the association between local stroke lesion load, regional brain age, and motor impairment is unclear. This study aimed to investigate the associations between focal lesion damage, regional brain PAD in both hemispheres, and motor outcomes in chronic stroke, and to identify key predictors of motor impairment. METHODS:In this multicohort, retrospective, observational study, we included individuals with chronic unilateral stroke (>180 days post stroke) from the ENIGMA Stroke Recovery Working Group dataset and used individuals from the UK Biobank cohort to train the regional brain age prediction model. Structural T1-weighted MRI scans were used to estimate regional brain PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated on the basis of lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain PADs. Structural equation modelling examined directional relationships among corticospinal tract lesion load, ipsilesional brain PAD, motor outcomes, and contralesional brain PAD. FINDINGS:We included 501 individuals from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts in eight countries) and 17 791 individuals from the UK Biobank dataset. Larger total lesion size was positively associated with higher ipsilesional regional brain PADs (older brain age) across most regions (β=0·5420 to 0·9458 across significantly correlated regions, false discovery rate [FDR]-corrected p<0·05), and with lower brain PAD in the contralesional ventral attention and language network region (β=-0·3747, 95% CI -0·6961 to -0·0534, FDR-corrected p<0·05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain PADs across both hemispheres. Machine learning models identified corticospinal tract lesion load (adjusted mean difference -0·0905, 95% CI -0·1221 to -0·0589, p<0·0001), salience network lesion load (-0·0632, -0·0906 to -0·0358, p<0·0001), and regional brain PAD in the contralesional frontoparietal network (0·9939, 0·4929 to 1·4950, p=0·0001) as the top three predictors of motor outcomes. Structural equation modelling revealed that higher corticospinal tract lesion load was associated with poorer motor outcomes (β=-0·355, 95% CI -0·446 to -0·267, p<0·0001), which were further linked to younger contralesional brain age (0·204, 0·111 to 0·295, p<0·0001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age. INTERPRETATION:Our findings reveal that larger stroke lesions are associated with accelerated ageing in the ipsilesional hemisphere and paradoxically decelerated brain ageing in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age might serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke. FUNDING:US National Institutes of Health.

The human cortical functional hierarchy, spanning from primary sensorimotor to transmodal association regions, represents a fundamental principle of brain organisation. Here, we show lifespan changes in the sensorimotor-association (S-A) gradient in the cortical functional hierarchy using multimodal neuroimaging data from 33,247 participants aged 32 postmenstrual weeks to 80 years. We identify three critical neurodevelopmental milestones: initiation (third trimester to perinatal period), establishment (infancy to early childhood), and expansion-stabilisation (late childhood to adulthood). Pronounced gradient changes are predominantly observed during the first decade, with continued refinement extending into mid-adulthood. Spatiotemporally heterogeneous growth patterns in functional gradients align with evolutionary hierarchies, segregation-integration dynamics, structural maturation, and cognitive spectrum development, proceeding along a dominant S-A growth axis. These findings establish a unified neurodevelopmental framework that links connectome gradient dynamics to multifaceted functional and structural properties, advancing our understanding of cortical hierarchy maturation across the lifespan.

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

Socioeconomic status (SES) correlates with both mental symptoms and systemic health traits, suggesting systemic health conditions as potential complementary pathways for SES-mental health associations alongside neural mechanisms. Here, we distinguished independent associations of areal and personal SES with 95 whole-body health traits, including anxiety and depression, tested whether these associations differ by age and sex, and assessed the extent to which associations between areal SES and mental health are statistically attributable to personal SES and whole-body health traits in 489,543 participants from the UK Biobank. We identified 144 independent associations of areal and personal SES with whole-body health traits, including 14 with significant age differences and 65 with significant sex differences. The associations between areal SES and mental symptoms were primarily mediated by personal SES and whole-body health traits. The pathways from areal SES to depression and anxiety symptoms may inform multiple-level interventions for mental health improvement.

Heart-brain comorbidities are common and devastating, yet their genetic mechanisms remain unclear. Here, we explored the genetic mechanisms underlying comorbidities between five heart diseases and ten brain disorders. We observed varying degrees of polygenic overlap (dice coefficient: 0.04-0.60) among heart-brain disease pairs, along with 12 positive genetic correlations, 25 colocalizations, and 392 shared loci with consistent effects. Genes shared across different disease pairs were enriched for distinct biological processes; for example, genes shared by coronary artery disease with stroke, Alzheimer's disease, depression, and multiple sclerosis showed enrichment for heart development, lipid metabolism, synapse development, and immune cell differentiation, respectively. We conducted genome-wide association studies for the first time on ten heart-brain comorbidities and identified 51 associations, including 12 attributable to genetic sharing between diseases and six unique to comorbidity. This study improves our understanding of genetic mechanisms underlying heart-brain comorbidities and highlight the value of genome-wide association studies of comorbidity.

Schizophrenia manifests complex heterogeneity across multiple dimensions, posing major challenges for precise diagnosis and treatment. Developing objective biomarkers to stratify patients into stable subtypes is essential for heterogeneity resolving and precision medicine. This study aimed to identify neuroimaging subtypes of schizophrenia through a novel individualized radiomic-based texture similarity network (TSN) approach and investigate the biological signatures of these subtypes. K-means clustering identified two schizophrenia TSN-subtypes, which were validated across different samples, datasets, and disease stages. The subtypes exhibited distinct TSN dysconnectivity patterns, primarily involving the prefrontal-sensorimotor, prefrontal-limbic, subcortical-sensorimotor, subcortical-limbic, and within-subcortical networks. Additionally, these dysconnectivity patterns of the two subtypes were differentially associated with the expression of schizophrenia risk genes, which are enriched in protein binding, nervous system development, and neuron-specific structures. Finally, the density distributions of several neurotransmitter receptors, including M1, 5HT6, and mGluR5, showed broadly diverse associations with TSN dysconnectivity between subtypes. In summary, the TSN-defined subtypes are highly reproducible and reflect distinct neurobiological mechanisms at both the network and the molecular levels, highlighting divergent biological origins between subtypes.

In the human cerebral cortex, surface area follows a more prolonged developmental trajectory than cortical thickness; however, the genetic mechanisms underlying this difference remain unclear. We conducted genome-wide association meta-analyses for 64 cortical traits in 100,628 participants and identified 213 loci for cortical thickness and 417 loci for surface area (45 and 79 new loci), mapping to 184 and 431 genes (82 overlaps), respectively. Although thickness- and area-related genes exhibited similar functional enrichments, their cell type-specific expression curves during development showed distinct associations with growth-rate curves, with thickness growth showing a greater proportion of correlations with gene expression in inhibitory neurons. Even the shared genes influenced thickness growth and area expansion through distinct cell types and temporal lags. These findings indicate that the differing developmental trajectories of cortical thickness and surface area may arise from distinct cell type-specific gene expression and temporal dynamics.

BACKGROUND:As an extension of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) quantifies non-Gaussian water diffusion and has been applied to explore brain disorders. However, the genetic architecture of brain DKI phenotypes remains unknown. METHODS:Here, we estimated heritability and conducted genome-wide association studies (GWASs) for 804 DKI phenotypes across 188 brain structures in 4183 participants. To determine whether DKI-GWASs provides genetic insights beyond DTI-GWASs, we compared results from 804 DKI-GWASs and 752 DTI-GWASs in the same cohort. To clarify the biological significance of DKI phenotypes, we examined associations between DKI phenotypes and brain health-related outcomes within the CHIMGEN, and explored associations between polygenic risk scores (PRSs) of DKI phenotypes and mental disorders in the UK Biobank. FINDINGS:Of 804 DKI phenotypes, 275 showed significant heritability (P < 0.05; h2 range: 0.143-0.602). We detected 280 significant associations (P < 5 × 10-8), with 38 surviving Bonferroni correction (P < 1.54 × 10-10). These associations were unevenly distributed across chromosomes, DKI phenotype subgroups, and brain structures. Among 229 independent variant-structure associations for DKI, 175 (76.4%) were DKI-specific. We observed 930 associations between DKI phenotypes and brain health-related outcomes (P < 0.05; ten Bonferroni-significant with P < 1.02 × 10-5), and 200 between PRSs and mental disorders (P < 0.05; one Bonferroni-significant with P < 9.61 × 10-5). INTERPRETATION:This study delineates the genetic architecture of brain DKI phenotypes, identifies complementary genetic insights into brain microstructure, and provides biologically relevant endophenotypes for investigating neural mechanisms underlying brain disorders. FUNDING:National Natural Science Foundation of China, National Key Research and Development Program of China, Tianjin Key Medical Discipline Construction Project, and Tianjin Natural Science Foundation.

Background and Hypothesis Identifying generalizable brain imaging markers from large multi-center datasets remains challenging due to varying statistical aggregation approaches and p-hacking with increasing big data. We hypothesized that effect size (ES) inference surpasses P-value-based inference in reliably identifying core brain damage of schizophrenia, regardless of whether Mega- or Meta-analyses are used. Study Design We examined voxel-wise inter-group differences in gray matter volume (GMV) based on individual data from 976 schizophrenia patients and 801 healthy controls across 16 datasets, along with published coordinates data from 103 studies involving 5151 patients and 5438 controls, using Mega-analysis (Mega), Image-Based Meta-analysis (IBMA), and Coordinate-Based Meta-analysis (CBMA) under P-value and ES inference frameworks, respectively. We then compared the performances of different statistical aggregation (Mega, IBMA, and CBMA) and statistical inference (P-value and ES) strategies in revealing brain abnormalities in schizophrenia. Study Results P-value Mega identified significant GMV abnormalities in nearly all gray matter voxels (94.85%) with high sensitivity to sample size; in contrast, ES Mega detected core abnormalities in only 24.63% of voxels that had large ES and manifested higher resistance to sample size. ES IBMA and CBMA also demonstrated superior detection performance and were less affected by sample size than P-value ones. Finally, IBMA exhibited comparable performance with the Mega-analysis and superior performance than all types of CBMAs. Conclusions These results underscore the advantages of using ES inference in multi-center statistical aggregation and highlight the potential of IBMA for enhanced detection of brain structural abnormalities in schizophrenia.

Cerebral asymmetry is a core principle of human brain organization, showing dynamic changes across the lifespan and alterations in brain disorders. However, it remains unclear whether lifespan trajectories of asymmetry differ across populations. We compared lifespan structural asymmetry normative charts of 221 cerebral imaging phenotypes from 43,037 Chinese and 56,339 Western participants aged 0–100 years. The two populations showed distinct lifespan asymmetry patterns in 26.2% of the phenotypes. Chinese-minus-Western asymmetry difference curves displayed distinct patterns across brain phenotypes: rightward (45.7%), leftward (26.2%), rightward-to-leftward (11.8%), leftward-to-rightward (10.0%), and unclassified (6.3%). Population-matched normative models outperformed population-unmatched normative models in capturing normal asymmetry variability among healthy individuals and in detecting abnormal asymmetry deviations in patients with Alzheimer’s disease, mild cognitive impairment, schizophrenia, and major depressive disorder. These findings indicate that population mismatch can bias chart-based individual-level asymmetry assessment and underscore the need for population-representative brain asymmetry normative charts.

Plasma proteins are key biomarkers of physiological homeostasis and disease pathology, shaped by gene–environment interactions. We examined genome-wide and exposome-wide gene–environment interactions for 2,920 plasma proteins in 51,736 UK Biobank participants using variance quantitative trait locus analyses, and assessed their added value for predicting 321 incident diseases. We identified 1,357 variance quantitative trait loci for 1,001 proteins and evaluated their interactions with 536 exposures, discovering 498 interactions, 487 (97.79%) of which were novel. We extended genotype-specific exposure effects to genotype-specific indirect pathways from external exposures to plasma proteins via internal exposures. Adding gene–environment interactions to a demographic-protein model improved predictive accuracies for 46 diseases, with the greatest improvement (14.3%) for implicit sepsis. This study provides a comprehensive atlas of variance quantitative trait loci and gene–environment interactions in the human plasma proteome and reveals the added contribution of gene–environment interactions to disease prediction.

Purpose To examine common patterns among different computer-aided diagnosis (CAD) models for Alzheimer disease (AD) using structural MRI data and to characterize the clinical and imaging features associated with their misclassifications. Materials and Methods This retrospective study used 3258 baseline structural MRI scans from five multisite datasets and two multidisease datasets collected between September 2005 and December 2019. The 3D Nested Hierarchical Transformer (3DNesT) model and other CAD techniques were used for AD classification using 10-fold cross-validation and cross-dataset validation. Subgroup analysis of CAD-misclassified individuals compared clinical and neuroimaging biomarkers using independent t tests with Bonferroni correction. Results This study included 1391 patients with AD (mean age, 72.1 years ± 9.2 [SD]; 757 female), 205 with other neurodegenerative diseases (mean age, 64.9 years ± 9.9; 117 male), and 1662 healthy controls (mean age, 70.6 years ± 7.6; 935 female). The 3DNesT model achieved 90.0% ± 2.3 cross-validation accuracy and 82.2%, 90.1%, and 91.6% accuracy in three external datasets. Further analysis suggested that the false-negative subgroup (n = 223) exhibited minimal atrophy and better cognitive performance on the Mini-Mental State Examination (MMSE) than the true-positive subgroup (MMSE score in false-negative subgroup, 21.4 ± 4.4; true-positive subgroup, 19.7 ± 5.7; P value family-wise error [PFWE] < .001), despite displaying similar levels of amyloid β (false-negative subgroup, 705.9 pg/mL; true-positive subgroup, 665.7 pg/mL; PFWE = .99) and tau (false-negative subgroup, 352.4 pg/mL; true-positive subgroup, 371.0 pg/mL; PFWE = .99) burden. Conclusion A subgroup of patients with false-negative classification for Alzheimer disease exhibited atypical structural MRI patterns and clinical measures, fundamentally limiting the diagnostic performance of CAD models based solely on structural MRI. Keywords: MR Imaging, Dementia, Computer Applications-3D, Alzheimer's Disease, Computer-aided Diagnosis, Misclassification, Atypical AD Supplemental material is available for this article. © RSNA, 2025 See also commentary by Nasrallah in this issue.

The mechanism by which the increasing environmental challenge of urbanicity impacts the brain, personality and mental disorders remains unclear. Here, grounded in life history theory, we tested the hypothesis that age at menarche (AAM) mediates the effects of early-life urbanicity on adult regional brain volumes and personality traits associated with mental disorders. In a sample of 2,950 young Chinese women, we discovered that higher levels of early-life urbanicity were associated with earlier AAM, which in turn correlated with reduced medial prefrontal volume and lower levels of agreeableness and reward dependence in adulthood. Urbanicity-related factors, particularly family socioeconomic status, also influenced these neurobehavioral traits through AAM. The urbanicity- and AAM-related brain and personality traits were changed in patients with major depressive disorder and schizophrenia. These findings suggest that life history theory may serve as a mechanism through which early-life urbanicity influences the adult brain and personality traits associated with mental disorders in women. It is unclear how early-life urbanicity influences adult neurobehavioral traits. This study reveals that earlier menarche mediates the relationship between early-life urbanicity and adult neurobehavioral traits associated with mental disorders.

Chronic pain is a prevalent and debilitating condition that imposes substantial personal and societal burdens. Despite its significance, the neural mechanisms underlying individual susceptibility to chronic pain remain inadequately understood. In this study, we examined the prospective associations between 1325 brain structural imaging phenotypes and the future risk of developing chronic pain in a UK Biobank cohort of 5754–5756 participants. These phenotypes encompassed regional and tissue volume, cortical surface area and thickness. General linear models (GLMs) were employed to identify brain structural variations associated with the risk of developing chronic pain, and then Mendelian randomization (MR) was employed to explore potential causal relationships between brain structure and chronic pain development. GLMs identified three significant associations between imaging phenotypes and the future development of chronic pain. All three imaging phenotypes pertained to the cortical surface area of the frontal operculum, albeit derived from three different brain atlases. Specifically, reduced cortical surface area in the frontal operculum was significantly associated with an increased risk of developing chronic pain: BA atlas area 44 (T=−4.10, p=4.24×10−5), Desikan atlas pars opercularis (T=−4.21, p=2.55×10−5), and DKT atlas pars opercularis (T=−3.96, p=7.47×10−5). Subsequent MR analysis further demonstrated a causally protective effect of larger cortical area in the prefrontal operculum against the risk of developing chronic pain (OR = 0.91, p=1.91×10−2). These results indicate a critical role of the surface area of frontal operculum in individual chronic pain susceptibility and provide a potential risk predictor for chronic pain development.

Structural covariance refers to the concurrent changes in one morphological measure between two brain regions. Structural covariance of cortical morphological measures such as cortical thickness (CT), surface area (SA), and cortical volume (CV) have been applied to identify brain structural differences between patients with neuropsychiatric disorders and healthy controls. However, the precise relationships between structural covariance patterns of different cortical measures remain largely unknown. Here, we optimized the preprocessing and calculation approaches of structural covariances and investigated both global (whole-brain-level) and regional (brain-region-level) structural covariance similarities between CT, SA, and CV in 35,580 individuals. We found that Pearson correlation outperformed partial correlation due to generating fewer negative correlations of uncertain biological significance and principal component regression outperformed the regressions of total intracranial volume and respective global measures in removing global effects and reducing negative correlations. We observed that both global and regional covariance similarities of SA-CV were much higher than those of CT-CV and CT-SA, although they were influenced by the selection of atlases and covariance values. We also found age and sex effects on structural covariances and age effects on covariance similarities. The higher SA-CV covariance similarities than CT-CV indicates that SA contributes more to CV covariance than CT, although CV is derived from both CT and SA. The lack of CT-SA covariance similarities suggests that CT and SA have different covariance patterns and should be used in combination in structural covariance studies.