Brain age of rhesus macaques over the lifespan.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Executive functioning, behavior, and white matter microstructure in the chronic phase after pediatric mild traumatic brain injury: results from the adolescent brain cognitive development study.

BACKGROUND: Mild traumatic brain injury (mTBI) is common in children. Long-term cognitive and behavioral outcomes as well as underlying structural brain alterations following pediatric mTBI have yet to be determined. In addition, the effect of age-at-injury on long-term outcomes is largely unknown. METHODS: Children with a history of mTBI (n = 406; Mage = 10 years, SDage = 0.63 years) who participated in the Adolescent Brain Cognitive Development (ABCD) study were matched (1:2 ratio) with typically developing children (TDC; n = 812) and orthopedic injury (OI) controls (n = 812). Task-based executive functioning, parent-rated executive functioning and emotion-regulation, and self-reported impulsivity were assessed cross-sectionally. Regression models were used to examine the effect of mTBI on these domains. The effect of age-at-injury was assessed by comparing children with their first mTBI at either 0-3, 4-7, or 8-10 years to the respective matched TDC controls. Fractional anisotropy (FA) and mean diffusivity (MD), both MRI-based measures of white matter microstructure, were compared between children with mTBI and controls. RESULTS: Children with a history of mTBI displayed higher parent-rated executive dysfunction, higher impulsivity, and poorer self-regulation compared to both control groups. At closer investigation, these differences to TDC were only present in one respective age-at-injury group. No alterations were found in task-based executive functioning or white matter microstructure. CONCLUSIONS: Findings suggest that everyday executive function, impulsivity, and emotion-regulation are affected years after pediatric mTBI. Outcomes were specific to the age at which the injury occurred, suggesting that functioning is differently affected by pediatric mTBI during vulnerable periods. Groups did not differ in white matter microstructure.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization.

Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.

The decoupling of structural and functional connectivity of auditory networks in individuals at clinical high-risk for psychosis.

OBJECTIVES: Disrupted auditory networks play an important role in the pathophysiology of psychosis, with abnormalities already observed in individuals at clinical high-risk for psychosis (CHR). Here, we examine structural and functional connectivity of an auditory network in CHR utilising state-of-the-art electroencephalography and diffusion imaging techniques. METHODS: Twenty-six CHR subjects and 13 healthy controls (HC) underwent diffusion MRI and electroencephalography while performing an auditory task. We investigated structural connectivity, measured as fractional anisotropy in the Arcuate Fasciculus (AF), Cingulum Bundle, and Superior Longitudinal Fasciculus-II. Gamma-band lagged-phase synchronisation, a functional connectivity measure, was calculated between cortical regions connected by these tracts. RESULTS: CHR subjects showed significantly higher structural connectivity in the right AF than HC (p < .001). Although non-significant, functional connectivity between cortical areas connected by the AF was lower in CHR than HC (p = .078). Structural and functional connectivity were correlated in HC (p = .056) but not in CHR (p = .29). CONCLUSIONS: We observe significant differences in structural connectivity of the AF, without a concomitant significant change in functional connectivity in CHR subjects. This may suggest that the CHR state is characterised by a decoupling of structural and functional connectivity, possibly due to abnormal white matter maturation.

Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study.

Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).

Association between peripheral inflammation and free-water imaging in Major Depressive Disorder before and after ketamine treatment - A pilot study.

BACKGROUND: Alterations in the peripheral inflammatory profile and white matter (WM) deterioration are frequent in Major Depressive Disorder (MDD). The present study applies free-water imaging to investigate the relationship between altered peripheral inflammation and WM microstructure and their predictive value in determining response to ketamine treatment in MDD. METHODS: Ten individuals with MDD underwent diffusion-weighted magnetic resonance imaging and a blood-draw before and 24 h after ketamine infusion. We utilized MANCOVAs and ANCOVAs to compare tissue-specific fractional anisotropy (FAT) and free-water (FW) of the forceps and cingulum, and the ratio of pro-inflammatory interleukin(IL)-8/anti-inflammatory IL-10 between individuals with MDD and 15 healthy controls at baseline. Next, we compared all baseline measures between ketamine responders (6) and non-responders (4) and analyzed changes in imaging and blood data after ketamine infusion. RESULTS: The MDD group exhibited an increased IL-8/IL-10 ratio compared to controls at baseline (p = .040), which positively correlated with average FW across regions of interest (p = .013). Ketamine responders demonstrated higher baseline FAT in the left cingulum than non-responders (p = .023). Ketamine infusion did not influence WM microstructure but decreased the IL-8/IL-10 ratio (p = .043). LIMITATIONS: The small sample size and short follow-up period limit the conclusion regarding the longer-term effects of ketamine in MDD. CONCLUSIONS: This pilot study provides evidence for the role of inflammation in MDD by illustrating an association between peripheral inflammation and WM microstructure. Additionally, we demonstrate that free-water diffusion-weighted imaging might be a valuable tool to determine which individuals with MDD benefit from the anti-inflammatory mediated effects of ketamine treatment.

Longitudinal trajectory of response to electroconvulsive therapy associated with transient immune response & white matter alteration post-stimulation.

Research suggests electroconvulsive therapy (ECT) induces an acute neuroinflammatory response and changes in white matter (WM) structural connectivity. However, whether these processes are related, either to each other or to eventual treatment outcomes, has yet to be determined. We examined the relationship between levels of peripheral pro-inflammatory cytokines and diffusion imaging-indexed changes in WM microstructure in individuals with treatment-resistant depression (TRD) who underwent ECT. Forty-two patients were assessed at baseline, after their second ECT (T2), and after completion of ECT (T3). A Montgomery Åsberg Depression Rating Scale improvement of >50% post-ECT defined ECT-responders (n = 19) from non-responders (n = 23). Thirty-four controls were also examined. Tissue-specific fractional anisotropy (FAt) was estimated using diffusion imaging data and the Free-Water method in 17 WM tracts. Inflammatory panels were evaluated from peripheral blood. Cytokines were examined to characterize the association between potential ECT-induced changes in an inflammatory state and WM microstructure. Longitudinal trajectories of both measures were also examined separately for ECT-responders and non-responders. Patients exhibited elevated Interleukin-8 (IL-8) levels at baseline compared to controls. In patients, correlations between IL-8 and FAt changes from baseline to T2 were significant in the positive direction in the right superior longitudinal fasciculus (R-SLF) and right cingulum (R-CB) (psig = 0.003). In these tracts, linear mixed-effects models revealed that trajectories of IL-8 and FAt were significantly positively correlated across all time points in responders, but not non-responders (R-CB-p = .001; R-SLF-p = 0.008). Our results suggest that response to ECT in TRD may be mediated by IL-8 and WM microstructure.

How Machine Learning is Powering Neuroimaging to Improve Brain Health.

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology.

Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.

Sex-Related Differences in White Matter Asymmetry and Its Implications for Verbal Working Memory in Psychosis High-Risk State.

Objective: Sexual dimorphism has been investigated in schizophrenia, although sex-specific differences among individuals who are at clinical high-risk (CHR) for developing psychosis have been inconclusive. This study aims to characterize sexual dimorphism of language areas in the brain by investigating the asymmetry of four white matter tracts relevant to verbal working memory in CHR patients compared to healthy controls (HC). HC typically show a leftward asymmetry of these tracts. Moreover, structural abnormalities in asymmetry and verbal working memory dysfunctions have been associated with neurodevelopmental abnormalities and are considered core features of schizophrenia. Methods: Twenty-nine subjects with CHR (17 female/12 male) for developing psychosis and twenty-one HC (11 female/10 male) matched for age, sex, and education were included in the study. Two-tensor unscented Kalman filter tractography, followed by an automated, atlas-guided fiber clustering approach, were used to identify four fiber tracts related to verbal working memory: the superior longitudinal fasciculi (SLF) I, II and III, and the superior occipitofrontal fasciculus (SOFF). Using fractional anisotropy (FA) of tissue as the primary measure, we calculated the laterality index for each tract. Results: There was a significantly greater right>left asymmetry of the SLF-III in CHR females compared to HC females, but no hemispheric difference between CHR vs. HC males. Moreover, the laterality index of SLF-III for CHR females correlated negatively with Backward Digit Span performance, suggesting a greater rightward asymmetry was associated with poorer working memory functioning. Conclusion: This study suggests increased rightward asymmetry of the SLF-III in CHR females. This finding of sexual dimorphism in white matter asymmetry in a language-related area of the brain in CHR highlights the need for a deeper understanding of the role of sex in the high-risk state. Future work investigating early sex-specific pathophysiological mechanisms, may lead to the development of novel personalized treatment strategies aimed at preventing transition to a more chronic and difficult-to-treat disorder.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1-5 over 1 year) in 286 subjects (aged 12-32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p < 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FAT or FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis-regardless of transition outcome-is characterized by subtle age-related white matter changes that occur in tandem with development.

Network Analysis of Symptom Comorbidity in Schizophrenia: Relationship to Illness Course and Brain White Matter Microstructure.

INTRODUCTION: Recent network-based analyses suggest that schizophrenia symptoms are intricately connected and interdependent, such that central symptoms can activate adjacent symptoms and increase global symptom burden. Here, we sought to identify key clinical and neurobiological factors that relate to symptom organization in established schizophrenia. METHODS: A symptom comorbidity network was mapped for a broad constellation of symptoms measured in 642 individuals with a schizophrenia-spectrum disorder. Centrality analyses were used to identify hub symptoms. The extent to which each patient's symptoms formed clusters in the comorbidity network was quantified with cluster analysis and used to predict (1) clinical features, including illness duration and psychosis (positive symptom) severity and (2) brain white matter microstructure, indexed by the fractional anisotropy (FA), in a subset (n = 296) of individuals with diffusion-weighted imaging (DWI) data. RESULTS: Global functioning, substance use, and blunted affect were the most central symptoms within the symptom comorbidity network. Symptom profiles for some patients formed highly interconnected clusters, whereas other patients displayed unrelated and disconnected symptoms. Stronger clustering among an individual's symptoms was significantly associated with shorter illness duration (t = 2.7; P = .0074), greater psychosis severity (ie, positive symptoms expression) (t = -5.5; P < 0.0001) and lower fractional anisotropy in fibers traversing the cortico-cerebellar-thalamic-cortical circuit (r = .59, P < 0.05). CONCLUSION: Symptom network structure varies over the course of schizophrenia: symptom interactions weaken with increasing illness duration and strengthen during periods of high positive symptom expression. Reduced white matter coherence relates to stronger symptom clustering, and thus, may underlie symptom cascades and global symptomatic burden in individuals with schizophrenia.

Elucidating the relationship between white matter structure, demographic, and clinical variables in schizophrenia-a multicenter harmonized diffusion tensor imaging study.

White matter (WM) abnormalities are repeatedly demonstrated across the schizophrenia time-course. However, our understanding of how demographic and clinical variables interact, influence, or are dependent on WM pathologies is limited. The most well-known barriers to progress are heterogeneous findings due to small sample sizes and the confounding influence of age on WM. The present study leverages access to the harmonized diffusion magnetic-resonance-imaging data and standardized clinical data from 13 international sites (597 schizophrenia patients (SCZ)). Fractional anisotropy (FA) values for all major WM structures in patients were predicted based on FA models estimated from a healthy population (n = 492). We utilized the deviations between predicted and real FA values to answer three essential questions. (1) "Which clinical variables explain WM abnormalities?". (2) "Does the degree of WM abnormalities predict symptom severity?". (3) "Does sex influence any of those relationships?". Regression and mediator analyses revealed that a longer duration-of-illness is associated with more severe WM abnormalities in several tracts. In addition, they demonstrated that a higher antipsychotic medication dose is related to more severe corpus callosum abnormalities. A structural equation model revealed that patients with more WM abnormalities display higher symptom severity. Last, the results exhibited sex-specificity. Males showed a stronger association between duration-of-illness and WM abnormalities. Females presented a stronger association between WM abnormalities and symptom severity, with IQ impacting this relationship. Our findings provide clear evidence for the interaction of demographic, clinical, and behavioral variables with WM pathology in SCZ. Our results also point to the need for longitudinal studies, directly investigating the casualty and sex-specificity of these relationships, as well as the impact of cognitive resiliency on structure-function relationships.

White matter microstructure across brain-based biotypes for psychosis - findings from the bipolar-schizophrenia network for intermediate phenotypes.

The B-SNIP consortium identified three brain-based Biotypes across the psychosis spectrum, independent of clinical phenomenology. To externally validate the Biotype model, we used free-water fractional volume (FW) and free-water corrected fractional anisotropy (FAT) to compare white matter differences across Biotypes and clinical diagnoses. Diffusion tensor imaging data from 167 individuals were included: 41 healthy controls, 55 schizophrenia probands, 47 schizoaffective disorder probands, and 24 probands with psychotic bipolar disorder. Compared to healthy controls, FAt reductions were observed in the body of corpus callosum (BCC) for schizoaffective disorder (d = 0.91) and schizophrenia (d = 0.64). Grouping by Biotype, Biotype 1 showed FAt reductions in the CC and fornix, with largest effect in the BCC (d = 0.87). Biotype 2 showed significant FAt reductions in the BCC (d = 0.90). Schizoaffective disorder individuals had elevated FW in the CC, fornix and anterior corona radiata (ACR), with largest effect in the BCC (d = 0.79). Biotype 2 showed elevated FW in the CC, fornix and ACR, with largest effect in the BCC (d = 0.94). While significant diagnosis comparisons were observed, overall greater discrimination from healthy controls was observed for lower FAt in Biotype 1 and elevated FW in Biotype 2. However, between-group differences were modest, with one region (cerebral peduncle) showing a between-Biotype effect. No between-group effects were observed for diagnosis groupings.

Investigating Sexual Dimorphism of Human White Matter in a Harmonized, Multisite Diffusion Magnetic Resonance Imaging Study.

Axonal myelination and repair, critical processes for brain development, maturation, and aging, remain controlled by sexual hormones. Whether this influence is reflected in structural brain differences between sexes, and whether it can be quantified by neuroimaging, remains controversial. Diffusion-weighted magnetic resonance imaging (dMRI) is an in vivo method that can track myelination changes throughout the lifespan. We utilize a large, multisite sample of harmonized dMRI data (n = 551, age = 9-65 years, 46% females/54% males) to investigate the influence of sex on white matter (WM) structure. We model lifespan trajectories of WM using the most common dMRI measure fractional anisotropy (FA). Next, we examine the influence of both age and sex on FA variability. We estimate the overlap between male and female FA and test whether it is possible to label individual brains as male or female. Our results demonstrate regionally and spatially specific effects of sex. Sex differences are limited to limbic structures and young ages. Additionally, not only do sex differences diminish with age, but tracts within each subject become more similar to one another. Last, we show the high overlap in FA between sexes, which implies that determining sex based on WM remains open.

Large-Scale Evidence for an Association Between Peripheral Inflammation and White Matter Free Water in Schizophrenia and Healthy Individuals.

INTRODUCTION: Clarifying the role of neuroinflammation in schizophrenia is subject to its detection in the living brain. Free-water (FW) imaging is an in vivo diffusion-weighted magnetic resonance imaging (dMRI) technique that measures water molecules freely diffusing in the brain and is hypothesized to detect inflammatory processes. Here, we aimed to establish a link between peripheral markers of inflammation and FW in brain white matter. METHODS: All data were obtained from the Australian Schizophrenia Research Bank (ASRB) across 5 Australian states and territories. We first tested for the presence of peripheral cytokine deregulation in schizophrenia, using a large sample (N = 1143) comprising the ASRB. We next determined the extent to which individual variation in 8 circulating pro-/anti-inflammatory cytokines related to FW in brain white matter, imaged in a subset (n = 308) of patients and controls. RESULTS: Patients with schizophrenia showed reduced interleukin-2 (IL-2) (t = -3.56, P = .0004) and IL-12(p70) (t = -2.84, P = .005) and increased IL-6 (t = 3.56, P = .0004), IL-8 (t = 3.8, P = .0002), and TNFα (t = 4.30, P < .0001). Higher proinflammatory signaling of IL-6 (t = 3.4, P = .0007) and TNFα (t = 2.7, P = .0007) was associated with higher FW levels in white matter. The reciprocal increases in serum cytokines and FW were spatially widespread in patients encompassing most major fibers; conversely, in controls, the relationship was confined to the anterior corpus callosum and thalamic radiations. No relationships were observed with alternative dMRI measures, including the fractional anisotropy and tissue-related FA. CONCLUSIONS: We report widespread deregulation of cytokines in schizophrenia and identify inflammation as a putative mechanism underlying increases in brain FW levels.

Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort.

The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.