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Objective: Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuroprogressive or neurodegenerative component to schizophrenia, remain largely unknown. Examining fluid biomarkers of diverse types of neuronal damage could increase our understanding of these processes, as well as potentially provide clinically useful biomarkers, for example with assisting with differentiation from progressive neurodegenerative disorders such as Alzheimer and frontotemporal dementias. Methods: This study measured plasma neurofilament light chain protein (NfL) using ultrasensitive Simoa technology, to investigate the degree of neuronal injury in a well-characterised cohort of people with treatment-resistant schizophrenia on clozapine ( n = 82), compared to first-degree relatives (an at-risk group, n = 37), people with schizophrenia not treated with clozapine ( n = 13), and age- and sex-matched controls ( n = 59). Results: We found no differences in NfL levels between treatment-resistant schizophrenia (mean NfL, M = 6.3 pg/mL, 95% confidence interval: [5.5, 7.2]), first-degree relatives (siblings, M = 6.7 pg/mL, 95% confidence interval: [5.2, 8.2]; parents, M after adjusting for age = 6.7 pg/mL, 95% confidence interval: [4.7, 8.8]), controls (M = 5.8 pg/mL, 95% confidence interval: [5.3, 6.3]) and not treated with clozapine (M = 4.9 pg/mL, 95% confidence interval: [4.0, 5.8]). Exploratory, hypothesis-generating analyses found weak correlations in treatment-resistant schizophrenia, between NfL and clozapine levels (Spearman’s r = 0.258, 95% confidence interval: [0.034, 0.457]), dyslipidaemia ( r = 0.280, 95% confidence interval: [0.064, 0.470]) and a negative correlation with weight ( r = −0.305, 95% confidence interval: [−0.504, −0.076]). Conclusion: Treatment-resistant schizophrenia does not appear to be associated with neuronal, particularly axonal degeneration. Further studies are warranted to investigate the utility of NfL to differentiate treatment-resistant schizophrenia from neurodegenerative disorders such as behavioural variant frontotemporal dementia, and to explore NfL in other stages of schizophrenia such as the prodome and first episode.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Episodic memory ability relies on hippocampal-prefrontal connectivity. However, few studies have examined relationships between memory performance and white matter (WM) microstructure in hippocampal-prefrontal pathways in schizophrenia-spectrum disorder (SSDs). Here, we investigated these relationships in individuals with first-episode psychosis (FEP) and chronic schizophrenia-spectrum disorders (SSDs) using tractography analysis designed to interrogate the microstructure of WM tracts in the hippocampal-prefrontal pathway. Measures of WM microstructure (fractional anisotropy [FA], radial diffusivity [RD], and axial diffusivity [AD]) were obtained for 47 individuals with chronic SSDs, 28 FEP individuals, 52 older healthy controls, and 27 younger healthy controls. Tractography analysis was performed between the hippocampus and three targets involved in hippocampal-prefrontal connectivity (thalamus, amygdala, nucleus accumbens). Measures of WM microstructure were then examined in relation to episodic memory performance separately across each group. Both those with FEP and chronic SSDs demonstrated impaired episodic memory performance. However, abnormal WM microstructure was only observed in individuals with chronic SSDs. Abnormal WM microstructure in the hippocampal-thalamic pathway in the right hemisphere was associated with poorer memory performance in individuals with chronic SSDs. These findings suggest that disruptions in WM microstructure in the hippocampal-prefrontal pathway may contribute to memory impairments in individuals with chronic SSDs but not FEP.

Deficits in brain morphology are one of the most widely replicated neuropathological features in schizophrenia-spectrum disorder (SSD), although their biological underpinnings remain unclear. Despite the existence of hypotheses by which peripheral inflammation may impact brain structure, few studies have examined this relationship in SSD. This study aimed to establish the relationship between peripheral markers of inflammation and brain morphology and determine whether such relationships differed across healthy controls and individuals with first episode psychosis (FEP) and chronic schizophrenia. A panel of 13 pro- and anti-inflammatory cytokines were quantified from serum in 175 participants [n = 84 Healthy Controls (HC), n = 40 FEP, n = 51 Chronic SCZ]. We first performed a series of permutation tests to identify the cytokines most consistently associated with brain structural regions. Using moderation analysis, we then determined the extent to which individual variation in select cytokines, and their interaction with diagnostic status, predicted variation in brain structure. We found significant interactions between cytokine level and diagnosis on brain structure. Diagnostic status significantly moderated the relationship of IFNγ, IL4, IL5 and IL13 with frontal thickness, and of IFNγ and IL5 and total cortical volume. Specifically, frontal thickness was positively associated with IFNγ, IL4, IL5 and IL13 cytokine levels in the healthy control group, whereas pro-inflammatory cytokines IFNγ and IL5 were associated with lower total cortical volume in the FEP group. Our findings suggest that while there were no relationships detected in chronic schizophrenia, the relationship between peripheral inflammatory markers and select brain regions are differentially impacted in FEP and healthy controls. Longitudinal investigations are required to determine whether the relationship between brain structure and peripheral inflammation changes over time.

While the biological substrates of brain and behavioural changes in persons with schizophrenia remain unclear, increasing evidence implicates that inflammation is involved. In schizophrenia, including first-episode psychosis and anti-psychotic naive patients, there are numerous reports of increased peripheral inflammation, cognitive deficits and neuropathologies such as cortical thinning. Research defining the relationship between inflammation and schizophrenia symptomatology and neuropathology is needed. Therefore, we analysed the level of C-reactive protein (CRP), a peripheral inflammation marker, and its relationship with cognitive functioning in a cohort of 644 controls and 499 schizophrenia patients. In a subset of individuals who underwent MRI scanning (99 controls and 194 schizophrenia cases), we tested if serum CRP was associated with cortical thickness. CRP was significantly increased in schizophrenia patients compared to controls, co-varying for age, sex, overweight/obesity and diabetes (p

Background Childhood maltreatment is associated with lifelong psychiatric sequelae. However, our understanding of neurobiological mechanisms responsible for this association is limited. Childhood maltreatment may confer risk for psychopathology by altering neurodevelopmental trajectories during childhood and adolescence. Longitudinal research, which is essential for examining this question, has been limited. Methods We investigated maltreatment-associated alterations in the development of neural circuitry. Associations between cumulative childhood maltreatment (assessed using a dimensional measure of abuse and neglect via the Childhood Trauma Questionnaire) and the longitudinal development of resting-state functional connectivity (rsFC) were examined in 130 community-residing adolescents. Functional magnetic resonance imaging data were acquired at age 16 (T1; mean ± SD age, 16.46 ± 0.52 years; 66 females) and age 19 (T2; mean follow-up period, 2.35 years; n = 90 with functional magnetic resonance imaging data at both time points). Results We found maltreatment to be associated with widespread longitudinal increases in rsFC, primarily between default mode, dorsal attention, and frontoparietal systems. We also found sex-dependent increased maltreatment-associated rsFC in male participants in salience and limbic circuits. Cross-sectional analyses revealed a shift in maltreatment-related rsFC alterations, which were localized to subcortical and sensory circuits at T1 and to frontal circuits at T2. Finally, longitudinal increases in rsFC connectivity mediated the relationship between childhood maltreatment and increased depressive symptoms. Conclusions To our knowledge, this is the first study to examine longitudinal maltreatment-related alterations in rsFC in adolescents. Our findings shed light on the neurodevelopmental consequences of childhood maltreatment and provide evidence for their role in risk for depression.

BackgroundCigarette smoking is associated with worse cognition and decreased cortical volume and thickness in healthy cohorts. Chronic cigarette smoking is prevalent in schizophrenia spectrum disorders (SSD), but the effects of smoking status on the brain and cognition in SSD are not clear. This study aimed to understand whether cognitive performance and brain morphology differed between smoking and non-smoking individuals with SSD compared to healthy controls.MethodsData were obtained from the Australian Schizophrenia Research Bank. Cognitive functioning was measured in 299 controls and 455 SSD patients. Cortical volume, thickness and surface area data were analysed from T1-weighted structural scans obtained in a subset of the sample (n = 82 controls, n = 201 SSD). Associations between smoking status (cigarette smoker/non-smoker), cognition and brain morphology were tested using analyses of covariance, including diagnosis as a moderator.ResultsNo smoking by diagnosis interactions were evident, and no significant differences were revealed between smokers and non-smokers across any of the variables measured, with the exception of a significantly thinner left posterior cingulate in smokers compared to non-smokers. Several main effects of smoking in the cognitive, volume and thickness analyses were initially significant but did not survive false discovery rate (FDR) correction.ConclusionsDespite the general absence of significant FDR-corrected findings, trend-level effects suggest the possibility that subtle smoking-related effects exist but were not uncovered due to low statistical power. An investigation of this topic is encouraged to confirm and expand on our findings.

Background: Software-based MRI/TRUS fusion biopsy depends on the coordination of several steps, and inter-examiner differences could influence the results. The aim of this bicentric prospective study was to compare the detection rates of MRI/TRUS fusion targeted biopsy (TG) and systematic biopsy (SB), and the detection rates of examiners with different levels of previous experience in prostate biopsy. Methods: A total of 419 patients underwent MRI based on a suspicion of prostate cancer with elevated PSA levels. MRI was positive in 395 patients (221 in the first biopsy group [FB] and 174 in the repeated biopsy group [RB]). A subsequent TG, followed by a SB, was performed on these patients by four different examiners. Results: In the detection of clinically significant prostate cancer, a significant difference was found for TG+SB against SB in the RB group (35.1% vs. 25.3%, P=0.047). In the detection of clinically insignificant prostate cancer, the SB had a significantly higher detection rate than TG in both subgroups (FB: 11.9% vs. 4.7%, P=0.008; RB: 13.8% vs. 6.9%, P=0.034). A significant difference was found between the four examiners in the FB for TG (P=0.028), SB (P=0.036), and TG+SB (P=0.017). Conclusion: MRI/TRUS TG in combination with SB had significantly higher detection rates than SB in the RB group only. Differences in detection rates between examiners were dependent on the level of previous experience with TRUS guided biopsy.

An autonomous visual navigation algorithm is considered, designed for “home“ return of unmanned aerial vehicle (UAV) equipped with on-board video camera and on-board computer, out of GPS and GLONASS navigation signals. The proposed algorithm is similar to the well-known visual navigation algorithms such as V-SLAM (simultaneous localization and mapping) and visual odometry, however, it differs in separate implementation of mapping and localization processes. It calculates the geographical coordinates of the features on the frames taken by on-board video camera during the flight from the start point until the moment of GPS and GLONASS signals loss. After the loss of the signal the return mission is launched, which provides estimation of the position of UAV relatively the map created by previously found features. Proposed approach does not require such complex calculations as V-SLAM and does not accumulate errors over time, in contrast to visual odometry and traditional methods of inertial navigation. The algorithm was implemented and tested with use of DJI Phantom 3 Pro quadcopter.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD‐associated functional brain alterations using one of the world's largest resting‐state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta‐analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default‐mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia1–5. We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners (n = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders. A new cross-validated neuroimaging biomarker that reflects striatal dysfunctioning can be used to distinguish patients with schizophrenia from healthy controls, and is associated with treatment response to antipsychotics.

Both natural and engineered networks are often modular. Whether a network node interacts with only nodes from its own module or nodes from multiple modules provides insight into its functional role. The participation coefficient (PC) is typically used to measure this attribute, although its value also depends on the size and connectedness of the module it belongs to and may lead to nonintuitive identification of highly connected nodes. Here, we develop a normalized PC that reduces the influence of intramodular connectivity compared with the conventional PC. Using brain, C. elegans, airport, and simulated networks, we show that our measure of participation is not influenced by the size or connectedness of modules, while preserving conceptual and mathematical properties, of the classic formulation of PC. Unlike the conventional PC, we identify London and New York as high participators in the air traffic network and demonstrate stronger associations with working memory in human brain networks, yielding new insights into nodal participation across network modules., Author Summary It is challenging to reliably quantify how single elements (i.e., nodes) in a network are connected to different subcomponents (i.e., modules) of a network; this is important as intermodular connectivity contribute to efficient and distributed information processing. Participation coefficient (PC) calculates how distributed nodes are across modules. But PC is influenced by modularity algorithms that tend to favor large modules with strong intramodule connectivity, that in turn generate low PC values, even if a node has strong intermodule connectivity. We use a network randomization approach and show that by reducing the influence of intramodular connectivity, we obtain node participation results unaffected by size and connectedness of modules. This provides network scientists with new insights into the intermodular connectivity configurations of complex networks.

ObjectiveTo determine whether anatomical and functional brain features relate to key persistent post-concussion symptoms (PPCS) in children recovering from mild traumatic brain injuries (mTBI), and whether such brain indices can predict individual recovery from PPCS.Methods110 children with mixed recovery following mTBI were seen at the concussion clinic at Neurology department Alberta Children’s Hospital. The primary outcome was the Post-Concussion Symptom Inventory (PCSI, parent proxy). Sleep disturbance scores (PCSI sub-domain) and the Neurocognition Index (CNS Vital Signs) were also measured longitudinally. PPCS was assessed at 4 weeks post-injury and 8-10 weeks post-injury. Grey matter volumes were assessed using magnetic resonance imaging (MRI) and voxel-based morphometry. Functional connectivity was estimated using resting-state MRI. Two complementary machine learning methods were used to assess if the combination of grey matter and functional connectivity indices carried meaningful prognostic information.ResultsHigher scores on a composite index of sleep disturbance, including fatigue, were associated with converging decreases in grey matter volume and local functional connectivity in two key nodes of the default mode network: the posterior cingulate cortex and the medial prefrontal cortex. Sleep-related disturbances also significantly correlated with reductions in functional connectivity between these brain regions. The combination of structural and functional brain indices associated to individual variations in the default mode network accurately predicted clinical outcomes at follow-up (area under the curve = 0.86).InterpretationThese results highlight that the function-structure profile of core default mode regions underpins sleep-related problems following mTBI and carries meaningful prognostic information for pediatric concussion recovery.

Cognitive performance can be predicted from an individual’s functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC

Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects' functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.

Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations on optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.HighlightsWe establish a network equivalent of image smoothing for structural connectomes.Connectome Spatial Smoothing (CSS) improves connectome test-retest reliability, identifiability and sensitivity.CSS also facilitates reliable inference and improves power to detect statistical associations.Both high-resolution and atlas-based connectomes can benefit from CSS.

The extent to which resting-state fMRI (rsfMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous rsfMRI and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced during intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is typically increased in brain disorders, we hypothesised that intracranial stimulation would reduce the brain9s switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved rsfMRI connectivity. Network switching was significantly decreased during epochs with brain stimulation compared to epochs with no brain stimulation. The initial stimulation onset of brain stimulation was associated with the greatest decrease in network switching, followed by a more consistent reduction in network switching throughout the scans. These changes were most commonly observed in cortical networks spatially distant from the stimulation targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in neurological disease.

Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.

The neurobiology of major depressive disorder (MDD) remains incompletely understood, and many individuals fail to respond to standard treatments. Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) has emerged as a promising antidepressant therapy. However, the heterogeneity of response underscores a pressing need for biomarkers of treatment outcome. We acquired resting state functional magnetic resonance imaging (rsfMRI) data in 47 MDD individuals prior to 5–8 weeks of rTMS treatment targeted using the F3 beam approach and in 29 healthy comparison subjects. The caudate, prefrontal cortex, and thalamus showed significantly lower blood oxygenation level‐dependent (BOLD) signal power in MDD individuals at baseline. Critically, individuals who responded best to treatment were associated with lower pre‐treatment BOLD power in these regions. Additionally, functional connectivity (FC) in the default mode and affective networks was associated with treatment response. We leveraged these findings to train support vector machines (SVMs) to predict individual treatment responses, based on learned patterns of baseline FC, BOLD signal power and clinical features. Treatment response (responder vs. nonresponder) was predicted with 85–95% accuracy. Reduction in symptoms was predicted to within a mean error of ±16% (r = .68, p

Background and importance Obsessive-compulsive disorder (OCD) is a disabling psychiatric disorder, mainly treated with psychotherapy and pharmacotherapy. Surgical intervention may be appropriate for patients with treatment-refractory OCD. Deep brain stimulation (DBS) is an alternative for previously common ablative surgical procedures. Tractography has been proposed as a method for individualizing DBS treatment and may have the potential to improve efficacy. Clinical presentation We present a patient with treatment-refractory OCD previously treated with bilateral leucotomies, who underwent DBS surgery with targeting informed by tractography. Preoperative tractography to identify suitable DBS targets was undertaken. Structural images were also utilized for standard stereotactic surgical planning. The anteromedial globus pallidus internus (amGPi) was chosen as the target bilaterally after consideration of white matter projections to frontal cortical regions and neurosurgical approach. Bilateral amGPi DBS surgery was undertaken without adverse events. At 16-mo follow-up, there was a 48.5% reduction in OCD symptom severity as measured by the Yale-Brown Obsessive Compulsive Scale. Conclusion The amGPi can be a successful DBS target for OCD. This is the first known case to report on DBS surgery postleucotomies for OCD and highlights the utility of tractography for surgical planning in OCD.

PurposeOrganizational change involves optimizing a firm’s sustainability performance. The purpose of this paper is to explore how strategic orientations concerning the interface between business and society influence organizations’ sustainability performance. To explain how different strategic orientations – especially stewardship and instrumental orientations – impact sustainability performance, dynamic managerial capability theory is explored.Design/methodology/approachOurs is an inductive, qualitative study based on the template analysis of interviews conducted among sustainability managers from stock-listed multinational corporations headquartered in the Eurozone.FindingsCorporations with a stewardship orientation develop different dynamic managerial capabilities underlying sustainability performance than corporations that apply a more instrumental orientation. Results also show an “in-between” position: an equidistant orientation.Research limitations/implicationsThis study proves the emergence of different dynamic managerial capabilities that depend on companies’ strategic orientation, but follow-up research based on appreciative inquiry is needed to investigate the development of these capabilities over time.Practical implicationsFor achieving a higher level of sustainability performance, a stewardship orientation offers a stronger foundation than an instrumental orientation. Also companies with an equidistant orientation have a better sustainability performance than companies with an instrumental orientation, but based on a more central corporate level. The strategic orientation must be grounded in the development of fitting dynamic managerial capabilities that include an emphasis on shared cognition of long-term objectives, inclusion of stakeholders and setting objectives. Also strong internal and external ties, leadership of the CEO, educational background and how to deal with lack of knowledge are important aspects of managerial social and human capital.Social implicationsDue to its focus on the sustainability performance of companies and the identification of the supporting dynamic managerial capabilities, this paper is socially highly relevant.Originality/valuePrevious research has focused on strategic orientation, but little to no research has investigated how various strategic orientations toward the interface between business and society impact sustainability performance or what role dynamic managerial capabilities might play in the related change process.

The complement cascade has been proposed to contribute to the pathogenesis of schizophrenia. However, it remains unclear whether peripheral complement levels differ in cases compared to controls, change over the course of illness and whether they are associated with current symptomatology. This study aimed to: i) investigate whether peripheral complement protein levels are altered at different stages of illness, and ii) identify patterns among complement protein levels that predict clinical symptoms. Complement factors C1q, C3 and C4 were quantified in 183 participants [n = 83 Healthy Controls (HC), n = 10 Ultra-High Risk (UHR) for psychosis, n = 40 First Episode Psychosis (FEP), n = 50 Chronic schizophrenia] using Multiplex ELISA. Permutation-based t-tests were used to assess between-group differences in complement protein levels at each of the three illness stages, relative to age- and gender-matched healthy controls. Canonical correlation analysis was used to identify patterns of complement protein levels that correlated with clinical symptoms. C4 was significantly increased in chronic schizophrenia patients, while C3 and C4 were significantly increased in UHR patients. There were no differences in C1q, C3 and C4 in FEP patients when adjusting for BMI. A molecular pattern of increased C4 and decreased C3 was associated with positive and negative symptom severity in the pooled patient sample. Our findings indicate that peripheral complement concentration is increased across specific stages of psychosis and its imbalance may be associated with symptom severity. Given the small sample size of the UHR group, these findings should be regarded as exploratory, requiring replication.

Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on temporal, topological and anatomical levels. For this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were compared to IPSA. We found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. The association between CSWA and IPSA was stronger for a window length of ∼20 s (shorter than filtered fMRI wavelength) than ∼100 s (longer than filtered fMRI wavelength), irrespective of the sampling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated for both short (∼20 s) and long (∼100 s) windows, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA). IPSA and CSWA were anatomically similar in the default mode network, sensory cortex, insula and cerebellum. Our results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, it does not mandate a (semi-)arbitrary choice of window length and window overlap. A code for calculating IPSA is provided.

Genetic and environmental etiologies may contribute to schizophrenia and its associated neurobiological profile. We examined the interaction between dopaminergic polymorphisms, childhood adversity and diagnosis (schizophrenia/schizoaffective disorder) on dopamine-related brain structures. Childhood adversity histories and structural MRI data were obtained from 249 (153 schizophrenia/schizoaffective, 96 controls) participants registered in the Australian Schizophrenia Research Bank. Polymorphisms in DRD2 and COMT were genotyped and a dopaminergic risk allelic load (RAL) was calculated. Regression analysis was used to test the main and interaction effects of RAL, childhood adversity and diagnosis on volumes of dopamine-related brain structures (caudate, putamen, nucleus accumbens, dorsolateral prefrontal cortex and hippocampus). A schizophrenia/schizoaffective diagnosis showed significant main effects on bilateral hippocampus, left dorsolateral prefrontal cortex and bilateral putamen volumes. RAL showed a significant main effect on left putamen volumes. Furthermore, across the whole sample, a significant two-way interaction between dopaminergic RAL and childhood adversity was found for left putamen volumes. No brain structure volumes were predicted by a three-way interaction that included diagnosis. Our finding suggests the left putamen may be particularly sensitive to dopaminergic gene-environment interactions regardless of diagnosis. However, larger studies are needed to assess whether these interactions are more or less pronounced in those with schizophrenia/schizoaffective disorders.

It has been hypothesized that resting state networks (RSNs), extracted from resting state functional magnetic resonance imaging (rsfMRI), likely display unique temporal complexity fingerprints, quantified by their multiscale entropy patterns (McDonough and Nashiro, 2014). This is a hypothesis with a potential capacity for developing digital biomarkers of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of McDonough and Nashiro (2014) was that rsfMRI data from only 20 healthy individuals was used for the analysis. To validate this hypothesis in a larger cohort, we used rsfMRI datasets of 987 healthy young adults from the Human Connectome Project (HCP), aged 22-35, each with four 14.4-min rsfMRI recordings and parcellated into 379 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We performed effect-size analysis on the data in 8 RSNs. Given that the morphology of multiscale entropy is affected by the choice of its tolerance parameter (r) and embedding dimension (m), we repeated the analyses at multiple values of r and m including the values used in McDonough and Nashiro (2014). Our results reinforced high temporal complexity in the default mode and frontoparietal networks. Lowest temporal complexity was observed in the subcortical areas and limbic system. We investigated the effect of temporal resolution (determined by the repetition time TR) after downsampling of rsfMRI time series at two rates. At a low temporal resolution, we observed increased entropy and variance across datasets. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially when the tolerance parameter r is equal to 0.5. The results confirmed that the relationship between functional brain connectivity strengths and rsfMRI temporal complexity changes over time scales. Finally, a non-random correlation was observed between temporal complexity of RSNs and fluid intelligence suggesting that complex dynamics of the human brain is an important attribute of high-level brain function.

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.

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance related to age and sex as biological variation of interest. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90 % of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modelling where the primary interest is in accurate modelling of inter-subject variation and statistical quantification of deviations from a reference model.1HighlightsDevelopment and presentation of normative modeling approach based on hierarchical Bayesian modeling that can be applied to large multi-site neuroimaging data sets.Comparison of performance of Hierarchical Bayesian model including site as predictor to several common ways to harmonize for multi-site effects.Presentation of normative modeling as site correction tool.

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.

This study assesses whether treatment response for depression could be improved via a single one-site-fits-all dorsolateral prefrontal cortex target, representing the group average optimal site of subgenual cingulate cortex functional connectivity, or whether target site personalization is necessary.

Alcohol consumption is common in adolescence, a time when the human brain undergoes substantial development, raising concerns about the neurodevelopmental impact of drinking alcohol, especially at high levels. Risky drinking may adversely affect the developing white matter, comprised of axonal fibre pathways that integrate anatomically distributed, and functionally specialised, neural systems. We used diffusion-weighted magnetic resonance imaging (MRI) to perform the first prospective, comprehensive and regionally unbiased connectome-wide analysis of longitudinal changes in inter-regional structural connectivity between 16.5 and 18.8 years of age, comparing adolescents who reported frequent risky drinking (n = 20) with those who reported limited risky drinking (n = 19) across this developmental period. We found no difference in the development of structural connectivity between these groups, regardless of whether inter-regional connections were quantified using streamline count, fractional anisotropy, mean diffusivity, axial diffusivity, or radial diffusivity. These findings suggest that risky drinking may have limited adverse effects on the development of inter-regional structural connectivity during mid to late adolescence.

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.

BackgroundThe 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.MethodsFor 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 (95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual.ResultsCompared 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, whereas 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59±2%, pConclusionsAnatomical 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.

MRI is a promising tool for translational research to link brain function and structure in animal models of disease to patients with neuropsychiatric disorders. However, given that mouse functional MRI (fMRI) typically relies on anesthetics to suppress head motion and physiological noise, it has been difficult to directly compare brain fMRI in anesthetized mice with that in conscious patients. Here, we developed a new system to acquire fMRI in awake mice, which includes a head positioner and dedicated radio frequency coil. The system was used to investigate functional brain networks in conscious mice, with the goal of enabling future studies to bridge fMRI of disease model animals with human fMRI. Cranioplastic surgery was performed to affix the head mount and the cupped-hand handling method was performed to minimize stress during MRI scanning. Here we describe the new mouse fMRI system, cranioplastic surgery and acclimation protocol. Graphic abstract: [Image: see text] Awake fMRI system to investigate the neuronal activity in awaked mice.

Evidence-based treatments for children with persistent post-concussion symptoms (PPCS) are few and limited. Common PPCS complaints such as sleep disturbance and fatigue could be ameliorated via the supplementation of melatonin, which has significant neuroprotective and anti-inflammatory properties. This study aims to identify neural correlates of melatonin treatment with changes in sleep disturbances and clinical recovery in a pediatric cohort with PPCS. We examined structural and functional neuroimaging (fMRI) in 62 children with PPCS in a randomized, double-blind, placebo-controlled trial of 3 mg or 10 mg of melatonin (NCT01874847). The primary outcome was the total youth self-report Post-Concussion Symptom Inventory (PCSI) score after 28 days of treatment. Secondary outcomes included the change in the sleep domain PCSI score and sleep-wake behavior (assessed using wrist-worn actigraphy). Whole-brain analyses of (1) functional connectivity (FC) of resting-state fMRI, and (2) structural gray matter volumes via voxel-based morphometry were assessed immediately before and after melatonin treatment and compared with placebo to identify neural effects of melatonin treatment. Increased FC of posterior default mode network (DMN) regions with visual, somatosensory, and dorsal networks was detected in the melatonin groups over time. The FC increases also corresponded with reduced wake periods (r = -0.27, p = 0.01). Children who did not recover (n = 39) demonstrated significant FC increases within anterior DMN and limbic regions compared with those who did recover (i.e., PCSI scores returned to pre-injury level, n = 23) over time, (p = 0.026). Increases in GM volume within the posterior cingulate cortex were found to correlate with reduced wakefulness after sleep onset (r = -0.32, p = 0.001) and sleep symptom improvement (r = 0.29, p = 0.02). Although the melatonin treatment trial was negative and did not result in PPCS recovery (with or without sleep problems), the relationship between melatonin and improvement in sleep parameters was linked to changes in function-structure within and between brain regions interacting with the DMN.

Evidence-based treatments for children with persistent post-concussion symptoms (PPCS) are few and limited. Common PPCS complaints such as sleep disturbance and fatigue could be ameliorated via the supplementation of melatonin, which has significant neuroprotective and anti-inflammatory properties. This study aims to identify neural correlates of melatonin treatment with changes in sleep disturbances and clinical recovery in a pediatric cohort with PPCS. We examined structural and functional neuroimaging (MRI) in 62 children with PPCS in a randomized, double-blind, placebo-controlled trial of 3mg or 10mg of melatonin (NCT01874847). The primary outcome was the total youth self-report Post-Concussion Symptom Inventory (PCSI) score after 28 days of treatment. Secondary outcomes included the change in the sleep domain PCSI score and sleep-wake behavior (assessed using wrist-worn actigraphy). Whole-brain analyses of (i) functional connectivity (FC) of resting-state fMRI, and (ii) structural grey matter (GM) volumes via voxel-based morphometry were assessed immediately before and after melatonin treatment and compared to placebo in order to identify neural effects of melatonin treatment. Increased FC of posterior default mode network (DMN) regions with visual, somatosensory and dorsal networks was detected in the melatonin groups over time. FC increases also corresponded with reduced wake periods (r=−0.27,p=0.01). Children who did not recover (n=39) demonstrated significant FC increases within anterior DMN and limbic regions compared to those that did recover (i.e. PCSI scores returned to pre-injury leveln=23) over time, (p=0.026). Increases in GM volume within the posterior cingulate cortex were found to correlate with reduced wakefulness after sleep onset (r=−0.32,p=0.001) and sleep symptom improvement (r=0.29,p=0.02). Although the melatonin treatment trial was negative and did not result in PPCS recovery (with or without sleep problems), the relationship between melatonin and improvement in sleep parameters were linked to changes in function-structure within and between brain regions interacting with the DMN.

While the function of most biological systems is tightly constrained by their structure, current evidence suggests that coupling between the structure and function of brain networks is relatively modest. We aimed to investigate whether the modest coupling between connectome structure and function is a fundamental property of nervous systems or a limitation of current brain network models. We developed a new deep learning framework to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models (group: R=0.9±0.1, individual: R=0.55±0.1). Crucially, brain function predicted from an individual's structural connectome explained significant inter-individual variation in cognitive performance. Our results suggest that structure-function coupling in human brain networks is substantially tighter than previously suggested. We establish the margin by which current brain network models can be improved and demonstrate how deep learning can facilitate investigation of relations between brain function and behavior.

Objective We aimed to examine white matter microstructure and connectivity in individuals with obsessive-compulsive disorder (OCD) and their unaffected siblings, relative to healthy controls. Methods Diffusion-weighted magnetic resonance imaging (dMRI) scans were acquired in 30 patients with OCD, 21 unaffected siblings, and 31 controls. We examined white matter microstructure using measures of fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). Structural networks were examined using network-based statistic (NBS). Results Compared to controls, OCD patients showed significantly reduced FA and increased RD in clusters traversing the left forceps minor, inferior fronto-occipital fasciculus, anterior thalamic radiation, and cingulum. Furthermore, the OCD group displayed significantly weaker connectivity (quantified by the streamline count) compared to controls in the right hemisphere, most notably in edges connecting subcortical structures to temporo-occipital cortical regions. The sibling group showed intermediate streamline counts, FA and RD values between OCD and healthy control groups in connections found to be abnormal in patients with OCD. However, these reductions did not significantly differ compared to controls. Conclusion Therefore, siblings of OCD patients display intermediate levels in dMRI measures of microstructure and connectivity, suggesting white matter abnormalities might be related to the familial predisposition for OCD.

Background Adolescence is typified by increasing rates of substance use and the development of substance use disorders (SUD). Aberrant connectivity between cortical regions involved in executive control, and subcortical regions has been suggested to be associated with SUD and problematic substance use among adolescents. Few studies, however, have investigated system-level or whole-brain functional connectivity (FC) in order to test this hypothesis. Methods In a sample of 114 adolescents (mean age = 17.62 years, SD = 1.23, 61F) from the community, the present study used resting-state functional magnetic resonance imaging and independent component analysis to study executive control-subcortical network (ECN-SCN) coupling in adolescent SUD (n=18) and problematic substance use (n=34). In addition, whole-brain FC analyses were also conducted. Results Problematic substance use, but not SUD, was associated with increased negative ECN-SCN coupling (p = 0.026). The whole-brain FC analysis showed insula-associated hypoconnectivity in the SUD group (p = 0.037), which was negatively correlated with frequency of substance use. Conclusions Findings implicate different neural circuitry underlying adolescent SUD versus problematic use. Greater negative coupling between the SCN and ECN in adolescents with problematic substance use could underlie risk for future development of SUD or other mental health problems. Although we cannot infer directionality, hypoconnectivity within the insula in adolescents with SUD may indicate addiction-related alterations in interoceptive awareness or impairments in decision-making.

OBJECTIVESchizophrenia has recently been associated with widespread white matter microstructural abnormalities, but the functional effects of these abnormalities remain unclear. Widespread heterogeneity of results from studies published to date preclude any definitive characterization of the relationship between white matter and cognitive performance in schizophrenia. Given the relevance of deficits in cognitive function to predicting social and functional outcomes in schizophrenia, the authors carried out a meta-analysis of available data through the ENIGMA Consortium, using a common analysis pipeline, to elucidate the relationship between white matter microstructure and a measure of general cognitive performance, IQ, in patients with schizophrenia and healthy participants.METHODSThe meta-analysis included 760 patients with schizophrenia and 957 healthy participants from 11 participating ENIGMA Consortium sites. For each site, principal component analysis was used to calculate both a global fractional anisotropy component (gFA) and a fractional anisotropy component for six long association tracts (LA-gFA) previously associated with cognition.RESULTSMeta-analyses of regression results indicated that gFA accounted for a significant amount of variation in cognition in the full sample (effect size [Hedges' g]=0.27, CI=0.17-0.36), with similar effects sizes observed for both the patient (effect size=0.20, CI=0.05-0.35) and healthy participant groups (effect size=0.32, CI=0.18-0.45). Comparable patterns of association were also observed between LA-gFA and cognition for the full sample (effect size=0.28, CI=0.18-0.37), the patient group (effect size=0.23, CI=0.09-0.38), and the healthy participant group (effect size=0.31, CI=0.18-0.44).CONCLUSIONSThis study provides robust evidence that cognitive ability is associated with global structural connectivity, with higher fractional anisotropy associated with higher IQ. This association was independent of diagnosis; while schizophrenia patients tended to have lower fractional anisotropy and lower IQ than healthy participants, the comparable size of effect in each group suggested a more general, rather than disease-specific, pattern of association.

We propose a new framework to map structural connectomes using deep learning and diffusion MRI. We show that our framework not only enables connectome mapping with a convolutional neural network (CNN), but can also be straightforwardly incorporated into conventional connectome mapping pipelines to enhance accuracy. Our framework involves decomposing the entire brain volume into overlapping blocks. Blocks are sufficiently small to ensure that a CNN can be efficiently trained to predict each block's internal connectivity architecture. We develop a block stitching algorithm to rebuild the full brain volume from these blocks and thereby map end-to-end connectivity matrices. To evaluate our block decomposition and stitching (BDS) framework independent of CNN performance, we first map each block's internal connectivity using conventional streamline tractography. Performance is evaluated using simulated diffusion MRI data generated from numerical connectome phantoms with known ground truth connectivity. Due to the redundancy achieved by allowing blocks to overlap, we find that our block decomposition and stitching steps per se can enhance the accuracy of probabilistic and deterministic tractography algorithms by up to 20-30%. Moreover, we demonstrate that our framework can improve the strength of structure-function coupling between in vivo diffusion and functional MRI data. We find that structural brain networks mapped with deep learning correlate more strongly with functional brain networks (r ​= ​0.45) than those mapped with conventional tractography (r ​= ​0.36). In conclusion, our BDS framework not only enables connectome mapping with deep learning, but its two constituent steps can be straightforwardly incorporated as part of conventional connectome mapping pipelines to enhance accuracy.

The connectome provides the structural substrate facilitating communication between brain regions. We aimed to establish whether accounting for polysynaptic communication in structural connectomes would improve prediction of interindividual variation in behavior as well as increase structure-function coupling strength. Connectomes were mapped for 889 healthy adults participating in the Human Connectome Project. To account for polysynaptic signaling, connectomes were transformed into communication matrices for each of 15 different network communication models. Communication matrices were (a) used to perform predictions of five data-driven behavioral dimensions and (b) correlated to resting-state functional connectivity (FC). While FC was the most accurate predictor of behavior, communication models, in particular communicability and navigation, improved the performance of structural connectomes. Communication also strengthened structure-function coupling, with the navigation and shortest paths models leading to 35–65% increases in association strength with FC. We combined behavioral and functional results into a single ranking that provides insight into which communication models may more faithfully recapitulate underlying neural signaling patterns. Comparing results across multiple connectome mapping pipelines suggested that modeling polysynaptic communication is particularly beneficial in sparse high-resolution connectomes. We conclude that network communication models can augment the functional and behavioral predictive utility of the human structural connectome., Author Summary Brain network communication models aim to describe the patterns of large-scale neural signaling that facilitate functional interactions between brain regions. While information can be directly communicated between anatomically connected regions, signaling between disconnected areas must occur via a sequence of intermediate regions. We investigated a number of candidate models of connectome communication and found that they improved structure-function coupling and the extent to which structural connectomes can predict interindividual variation in behavior. Comparing the behavioral and functional predictive utility of different models provided initial insight into which conceptualizations of network communication may more faithfully recapitulate biological neural signaling. Our results suggest network communication models as a promising avenue to unite our understanding of brain structure, brain function, and human behavior.

Background We aimed to explore the utility of prostate specific antigen (PSA) isoform [− 2] proPSA and its derivatives for prediction of pathological outcome after radical prostatectomy (RP). Methods Preoperative blood samples were prospectively and consecutivelyanalyzed from 472 patients treated with RP for clinically localized prostate cancerat four medical centers. Measured parameters were PSA, free PSA (fPSA), fPSA/PSA ratio, [− 2] proPSA (p2PSA), p2PSA/fPSA ratio and Prostate Health Index (PHI)(p2PSA/fPSA)*√PSA]. Logistic regression models were fitted to determine the accuracy of markers for prediction of pathological Gleason score (GS) ≥7, Gleason score upgrading, extracapsular extension of the tumor (pT3) and the presence of positive surgical margin (PSM). The accuracy of predictive models was compared using area under the receiver operating curve (AUC). Results Of 472 patients undergoing RP, 339 (72%) were found to have pathologic GS ≥ 7, out of them 178 (53%) experienced an upgrade from their preoperative GS = 6. The findings of pT3 and PSM were present in 132 (28%) and 133 (28%) cases, respectively. At univariable analysis of all the preoperative parameters, PHI was the most accurate predictor of pathological GS ≥7 (OR 1.02, 95% CI 1.01–1.03, ppppAUC = 66.6 (p = 0.015) and GS upgrading by 5.0% to AUC = 65.9 (p = 0.025), respectively. Conclusions Preoperative PHI levels may contribute significantly to prediction of prostate cancer aggressiveness and expansion of the tumor detected at final pathology.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1‐year follow‐up was assessed in 30 individuals with a schizophrenia‐spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting‐state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1‐year follow‐up varied markedly among individuals (interquartile range: 55%). Dynamic resting‐state connectivity measured within the default‐mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow‐up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (

Highlights • Sleep problems after stimulant use in ADHD were predicted using machine learning. • Step-wise combination of multi-level features enhanced prediction performance. • Fronto-striatal connectivity and DAT1, ADRA2A, SLC6A2 SNPs were key features. • An accuracy of 95.5% was achieved by Logistic Ridge Regression in the training data. • An accuracy of 86.1% was achieved by J48 in the independent validation analysis., Objective Sleep problems is the most common side effect of methylphenidate (MPH) treatment in ADHD youth and carry potential to negatively impact long-term self-regulatory functioning. This study aimed to examine whether applying machine learning approaches to pre-treatment demographic, clinical questionnaire, environmental, neuropsychological, genetic, and neuroimaging features can predict sleep side effects following MPH administration. Method The present study included 83 ADHD subjects as a training dataset. The participants were enrolled in an 8-week, open-label trial of MPH. The Barkley Stimulant Side Effects Rating Scale was used to determine the presence/absence of sleep problems at the 2nd week of treatment. Prediction of sleep side effects were performed with step-wise addition of variables measured at baseline: demographics (age, gender, IQ, height/weight) and clinical variables (ADHD Rating Scale-IV (ADHD-RS) and Disruptive Behavior Disorder rating scale) at stage 1, neuropsychological test (continuous performance test (CPT), Stroop color word test) and genetic/environmental variables (dopamine and norepinephrine receptor gene (DAT1, DRD4, ADRA2A, and SLC6A2) polymorphisms, blood lead, and urine cotinine level) at stage 2, and structural connectivities of frontostriatal circuits at stage 3. Three different machine learning algorithms ((Logistic Ridge Regression (LR), support vector machine (SVM), J48) were used for data analysis. Robustness of classifier model was validated in the independent dataset of 36 ADHD subjects. Results Classification accuracy of LR was 95.5% (area under receiver operating characteristic curve (AUC) 0.99), followed by SVM (91.0%, AUC 0.85) and J48 (90.0%, AUC 0.87) at stage 3 for predicting sleep problems. The inattention symptoms of ADHD-RS, CPT response time variability, the DAT1, ADRA2A DraI, and SLC6A2 A-3081T polymorphisms, and the structural connectivities between frontal and striatal brain regions were identified as the most differentiating subset of features. Validation analysis achieved accuracy of 86.1% (AUC 0.92) at stage 3 with J48. Conclusions Our results provide preliminary support to the combination of multimodal classifier, in particular, neuroimaging features, as an informative method that can assist in predicting MPH side effects in ADHD.

Purpose Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. Methods This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. Results For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). Conclusions Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique that allows for modulating the activity of local neural populations and related neural networks. TMS is touted as a viable intervention to normalize brain activity and alleviate some psychiatric symptoms. However, TMS interventions are known to be only moderately reliable, and the efficacy of such therapies remains to be proven for psychiatric disorders other than depression. We review new opportunities to personalize TMS interventions using neuroimaging and computational modeling, aiming to optimize treatment to suit particular individuals and clinical subgroups. Specifically, we consider the prospect of improving the efficacy of existing TMS interventions by parsing broad diagnostic categories into biologically and clinically homogeneous biotypes. Biotypes can provide distinct treatment targets for optimized TMS interventions. We further discuss the utility of computational models in refining TMS personalization and efficiently establishing optimal cortical targets for distinct biotypes. Personalizing cortical stimulation targets, treatment frequencies, and intensities can improve the therapeutic efficacy of TMS and potentially establish noninvasive brain stimulation as a viable treatment for psychiatric symptoms.