Brain Imaging and Behavior

Background and HypothesisTraditional fMRI analyses often ignore regions that fail to reach statistical significance, assuming they are biologically unimportant. We tested the accuracy of this assumption using causal discovery based-analysis that go beyond associations/correlations to test the causality of one regions influence over the other. We hypothesized that the network of statistically significant (active network, AN) and non-significant regions (silent network, SN) causally interact and their features will causally influence psychopathology severity and working memory performance. Study DesignWe examined AN and SN during N-BACK task on 25 FHR and 37 controls. Clusters with significantly different activations were juxtaposed to 360 Glasser atlas parcellations. The PC algorithm for causal discovery was implemented. Connectivity of regions with the highest alpha-centrality (HAC) were examined. ResultsSeventy-seven Glasser regions were in the AN and the rest were silent nodes. Two regions showed HAC for FHR and HC. Among controls, one HAC region was silent (auditory association cortex) and the other one was active (insula). Among FHR, both were silent nodes (early auditory cortex). These HAC regions in both groups had bidirectional directed edges between each other forming a reciprocal circuit whose edge-weights causally "increased" magical ideation severity. ConclusionCausal connectivity between SN and AN suggests that the statistically non-significant and significant regions influence each other. Our findings question the merit of ignoring statistically non-significant regions and exclusively including statistically significant regions in the pathophysiological models. Our study suggests that causality analysis should receive greater attention.

BackgroundWhile functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. MethodsWe proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification. ResultsIn addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. ConclusionsIn sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact StatementMulti-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.

IntroductionCortical thickness (CT) and surface area (SA) are established biomarkers of brain pathology in posttraumatic stress disorder (PTSD). Structural covariance networks (SCN) constructed from CT and SA may represent developmental associations, or unique interactions between brain regions, possibly influenced by a common causal antecedent. The ENIGMA-PGC PTSD Working Group aggregated PTSD and control subjects data from 29 cohorts in five countries (n=3439). MethodsUsing Destrieux Atlas, we built SCNs and compared centrality measures between PTSD subjects and controls. Centrality is a graph theory measure derived using SCN. ResultsNotable nodes with higher CT-based centrality in PTSD compared to controls were left fusiform gyrus, left superior temporal gyrus, and right inferior temporal gyrus. We found sex-based centrality differences in bilateral frontal lobe regions, left anterior cingulate, left superior occipital cortex and right ventromedial prefrontal cortex (vmPFC). Comorbid PTSD and MDD showed higher CT-based centrality in the right anterior cingulate gyrus, right parahippocampal gyrus and lower SA-based centrality in left insular gyrus. ConclusionUnlike previous studies with smaller sample sizes ([≤]318), our study found differences in centrality measures using a sample size of 3439 subjects. This is the first cross-sectional study to examine SCN interactions with age, sex, and comorbid MDD. Although limited to group level inferences, centrality measures offer insights into a nodes relationship to the entire functional connectome unlike approaches like seed-based connectivity or independent component analysis. Nodes having higher centrality have greater structural or functional connections, lending them invaluable for translational treatments like neuromodulation.

BackgroundOne of the core features of posttraumatic stress disorder (PTSD) is reexperiencing the trauma. The anterior insula (AI) was proposed to play a crucial role in these intrusive experiences. However, the dynamic function of the AI in reexperiencing trauma, as well as its putative modulation by effective therapy, still need to be specified. MethodsThirty PTSD patients were enrolled and exposed to traumatic memory reactivation therapy. Resting-state fMRI scans were acquired before and after treatment. To explore AI directed influences over the rest of the brain, we referred to a mixed-model using pre/post Granger causality analysis seeded on the AI as a within-subject factor and treatment response as a between-subject factor. To further identify correlates of reexperiencing trauma, we investigated how intrusive severity affected: (i) causality maps and (ii) the spatial stability of other intrinsic brain networks. ResultsWe observed dynamic changes in AI effective connectivity in PTSD patients. Many within- and between-network causal paths were found to be less influenced by the AI after effective therapy. Insular influences were found positively correlated with flashback severity, while reexperiencing was linked with a stronger default mode network (DMN) and more unstable central executive network (CEN) connectivity. ConclusionWe showed that directed changes in AI signaling to the DMN and CEN at rest may underlie the degree of intrusive symptoms in PTSD. A positive response to treatment further induced changes in network-to-network anticorrelated patterns. Such findings may guide targeted neuromodulation strategies in PTSD patients not suitably improved by conventional treatment.

BackgroundTo understand the neurobiology underlying psychopathology, we need valid measurements of brain function. Group atlases for brain functional connectivity (FC) allow for efficient comparisons, but they fail to account for inter-individual variability in network topography, a problem that personalized methods address. We assess the validity and predictive utility of group and personalized approaches of quantifying FC by 1) comparing effect sizes of associations with clinical metrics; and 2) accounting for spatial features of brain networks when examining the association between FC and clinical metrics. Methods324 teens ages 13-16 participated. Personalized networks were estimated using a hierarchical Bayesian model. Effect size comparisons were done by comparing the correlations between FC and clinical metrics (depression, ruminative coping style, and sensitivity to punishment/reward) with Steiglers Z-test. We also conducted regressions, with clinical metrics as the dependent variables. Those models included FC and spatial features, together and alone. ResultsThe effect size comparisons did not survive FDR correction. However, exploratory permutation tests show that 1) the magnitude of the correlations with depression are larger on average for the intersection estimates of FC than the group estimates; and 2) the magnitude of the correlations with a ruminative coping style are larger on average for the intersection estimates of FC than the personalized estimate. The other comparisons conducted using permutation tests are not significant. Multiple regression analyses demonstrated that only spatial features of networks, not FC, are associated with sensitivity to reward. DiscussionThese results imply that the intersection estimates are more valid than the group estimates, and that the intersection estimates have greater predictive utility than personalized estimates. Further, spatial features of functions networks may be useful in and of themselves in certain contexts. Therefore, researchers in psychiatry should take into consideration functional network topography in order to gain a better understanding of the neurobiology underlying psychopathology.

About 11% of the population is left-handed, a significant minority of the potential participant pool for functional MRI (fMRI) studies. However, convention in fMRI research dictates these potential participants be excluded due to the supposition that left-handed (LH) people may have different lateralization of neural functioning than right-handed (RH) people. This difference in lateralization may cause different areas of the brain to be activated by the same task. The current study investigates the lateralization differences between N=49 LH and N=50 RH during encoding and recognition memory tasks for verbal and non-verbal stimuli. Additionally, we measured participants laterality index by administering a semantic fluency task. We found no difference between groups for memory encoding activation for either verbal or non-verbal stimuli. Similarly, we found no group differences for verbal retrieval activation. There were quantitative differences between groups in non-verbal retrieval activation, primarily driven by greater spatial extent of activation in the RH group rather than by differences in lateralization in the LH group. To measure if including LH in fMRI studies would dilute results, we calculated memory effects in a priori regions of interest (ROI) for the RH group only and then examined the effect of substituting in progressively more LH for RH. We found significant memory effects in 14 a priori ROIs, 10 of which retained significant effects when adding LH participants. The remaining ROIs had significant memory-related activation in more than 80% of simulations with statistically likely numbers of LH participants. Taken together, these results indicate that the inclusion of left-handed participants does not have a strong detrimental effect on memory-related fMRI activation. On this basis we advocate for the inclusion of left-handed participants in cognitive neuroscience of memory research.

IntroductionDimensional psychopathology strives to associate different domains of cognitive dysfunction with brain circuitry. Connectivity patterns as measured by functional magnetic resonance imaging (fMRI) exist at multiple scales, with global networks of connectivity composed of microscale interactions between individual nodes. It remains unclear how separate dimensions of psychopathology might differentially impact these different scales of organization. MethodsPatients experiencing anxious misery symptomology (depression, anxiety and trauma; n = 192) were assessed for symptomology and received resting-state fMRI scans. Three modeling approaches (seed-based correlation analysis [SCA], support vector regression [SVR] and Brain Basis Set Modeling [BSS]), each relying on increasingly dense representations of functional connectivity patterns, were used to associate connectivity patterns with six different dimensions of psychopathology: anxiety sensitivity, anxious arousal, rumination, anhedonia, insomnia and negative affect. Importantly, a full 50 patients were held-out in a testing dataset, leaving 142 patients as training data. ResultsDifferent symptom dimensions were best modeled by different scales of brain connectivity: anhedonia and anxiety sensitivity were best modeled with single connections (SCA), insomnia and anxious arousal by mesoscale patterns (SVR) and negative affect and ruminative thought by broad, cortex-spanning patterns (BBS). Dysfunction within the default mode network was implicated in all symptom dimensions that were best modeled by multivariate models. ConclusionThese results suggest that symptom dimensions differ in the degree to which they impact different scales of brain organization. In addition to advancing our basic understanding of transdiagnostic psychopathology, this has implications for the translation of basic research paradigms to human disorders.

BACKGROUND AND HYPOTHESISSchizophrenia is associated with widespread neuroanatomical abnormalities affecting both grey matter and white matter (WM). Early symptoms are often linked to dysfunctions in the frontal cortex and the temporal lobe. This study investigates WM disruptions and explores how structural connectivity (SC) may contribute to the underlying mechanisms of the disorder. STUDY DESIGNWe analysed SC derived from diffusion MRI in 127 patients experiencing their first episode of schizophrenia (FES group), compared with healthy controls. Focusing on the fronto-parietal-temporal network, we examined SC across three hierarchical levels: network, node, and connection. SC metrics were compared between groups using 3-factor ANCOVA, accounting for relevant covariates. We also investigated associations between SC metrics and core positive symptoms using non-parametric correlations. RESULTSThe FES group showed significantly reduced average SC strength and global efficiency within the fronto-parietal-temporal network. At the nodal level, SC strength was significantly lower in the left inferior and middle temporal gyri (L.ITG, L.MTG), and in the right inferior parietal gyrus (R.IPG) and temporal pole (R.TP). No significant group differences emerged at the connection level. Notably, SC strength in the R.IPG was negatively correlated with Conceptual Disorganisation scores. CONCLUSIONSOur findings reveal global and regional SC disruptions in early psychosis, particularly in areas supporting cognitive, language, and executive functions. The observed association between R.IPG connectivity and Conceptual Disorganisation supports the link between disrupted SC and formal thought disorder, reinforcing the role of impaired structural integration in early psychosis.

BackgroundSchizophrenia (SZ) typically manifests heterogeneous phenotypes involving positive, negative and cognitive symptoms. However, the underlying neural mechanisms of these symptoms keep unclear. Functional gradient is a fascinating measure to characterize continuous, hierarchical organization of brain. MethodsWe aimed to investigate whether reproducible disruptions of functional gradient existed in SZ compared to normal controls (NC), and these abnormalities were associated with severity of clinical and cognitive symptoms in SZ. All analyses were implemented in two independent large-sample multi-site datasets (discovery dataset, 400 SZ and 336 NC; replication dataset, 279 SZ and 262 NC). First, functional gradient across cerebral cortex was calculated in each subject. Second, vertex-wise comparisons of cortical gradient between SZ and NC groups were performed to identify abnormalities in SZ. Meanwhile, reproducible and robustness analyses were implemented to validate these abnormalities. Finally, regression analyses were performed using generalized additive models to link these abnormalities to severity of clinical and cognitive symptoms in SZ. ResultsWe found an abnormal gradient map in SZ in the discovery dataset, which was reproducible in the replication dataset. The abnormal gradient pattern was also robust when performing methodological alternatives and control analyses. Further, these reproducible abnormalities can reliably predict symptoms of clinical and cognitive domains across the two independent datasets. ConclusionThese findings demonstrated that alterations in functional gradient can provide a reliable signature of SZ, characterizing the heterogenous symptoms of clinical or cognitive domains, and may be further investigated to understand the neurobiological mechanisms of these symptoms. Impact StatementIn our study, using functional gradient measure and statistical learning technology and two independent multi-site case-control resting-state fMRI datasets (discovery dataset: 736 subjects; replication dataset: 541 subjects), we comprehensively investigated functional hierarchical organization in the cerebral cortex of SZ and its association with interindividual severity of symptoms. We found reproducible and robust abnormalities of functional gradient existed in SZ, which provided a reliable signature to characterize negative and general psychopathology symptoms, as well as cognitive deficits. Our findings can provide new insights to understand the neurobiological mechanisms of clinical and cognitive symptoms in SZ.

ObjectivePost-stroke depression (PSD) is one of the most frequent psychiatric symptoms after a stroke event. The role of white matter hyperintensities (WMHs) associated with PSD in older patients remains unclear. This study aimed to examine the volume and location of white matter microstructure abnormalities among older patients with early-onset PSD. MethodsOlder ([≥]55 years) patients with acute cerebral infarction and hospitalized in King Chulalongkorn Memorial Hospitals stroke unit from October 2019 to September 2020 were recruited. Participants were assessed with the Montgomery-[A]sberg Depression Rating Scale (MADRS) within three months after the onset of stroke. All patients had MRI scans. The brain images were segmented into four regions via left/right, frontal/dorsal plains. Two WMHs volume detections (visual rating vs. semi-automated WMHs volumetric detection) were employed on the fluid-attenuated inversion recovery images (FLAIR) for each segment. The study then investigated the association between WMHs volume and MADRS score with regression analysis. ResultsThe study included twenty-nine patients with acute stroke. Total WMHs volume and segmented regions were not statistically associated with the MADRS score. However, there was a trend in different WHMs volume of the left anterior segment between depressed and non-depressed groups (t-test 2.058, p = 0.055). Further, demographic and clinical data showed no association with depressive symptoms. ConclusionThe volume of WHMs might not contribute to the development of early-onset PSD in older patients. This study showed a potential of a quantitative MRI analysis in clinical practice. Further investigation with a larger group of patients is needed.

The arcuate fasciculus (AF) has been implicated in the pathology behind schizophrenia and auditory verbal hallucinations (AVHs). White matter tracts forming the arcuate fasciculus can be quantified and visualized using diffusion tensor imaging (DTI) tractography. Although there have been a number of studies on this topic, the results have been conflicting. Studying the underlying white matter structure of the AF could shed light on functional connectivity between temporal and frontal language areas in AVHs. The participants were 66 patients with a schizophrenia diagnosis, where AVHs were defined from the Positive and Negative Syndrome Scale (PANSS), and compared with a healthy control group. DTI was performed on a 3T MR scanner, and tensor estimation was done using deterministic streamline tractography. Statistical analysis of the data showed significantly longer tracts along the AF in patients with severe and frequent AVHs, as well as an overall significant asymmetry with longer fibers on the left side. In addition, there were significant positive correlations between PANSS scores and tract length, tract volume, and number of streamlines for the posterior AF segment on the left side. It is concluded that the present structural results complement previous functional findings of fronto-temporal connectivity in AVH patients.

IntroductionEarly psychosis (EP) is a critical period in the course of psychotic disorders during which the brain is thought to undergo rapid and significant functional and structural changes 1. Growing evidence suggests that the advent of psychotic disorders is early alterations in the brains functional connectivity and structure, leading to aberrant neural network organization. The Human Connectome Project (HCP) is a global effort to map the human brains connectivity in healthy and disease populations; within HCP, there is a specific dataset that focuses on the EP subjects (i.e., those within five years of the initial psychotic episode) (HCP-EP), which is the focus of our study. Given the critically important role of the midbrain function and structure in psychotic disorders (cite), and EP in particular (cite), we specifically focused on the midbrain macro- and micro-structural alterations and their association with clinical outcomes in HCP-EP. MethodsWe examined macro- and micro-structural brain alterations in the HCP-EP sample (n=179: EP, n=123, Controls, n=56) as well as their associations with behavioral measures (i.e., symptoms severity) using a stepwise approach, incorporating a multimodal MRI analysis procedure. First, Deformation Based Morphometry (DBM) was carried out on the whole brain 3 Tesla T1w images to examine gross brain anatomy (i.e., seed-based and voxel-based volumes). Second, we extracted Fractional Anisotropy (FA), Axial Diffusivity (AD), and Mean Diffusivity (MD) indices from the Diffusion Tensor Imaging (DTI) data; a midbrain mask was created based on FreeSurfer v.6.0 atlas. Third, we employed Tract-Based Spatial Statistics (TBSS) to determine microstructural alterations in white matter tracts within the midbrain and broader regions. Finally, we conducted correlation analyses to examine associations between the DBM-, DTI- and TBSS-based outcomes and the Positive and Negative Syndrome Scale (PANSS) scores. ResultsDBM analysis showed alterations in the hippocampus, midbrain, and caudate/putamen. A DTI voxel-based analysis shows midbrain reductions in FA and AD and increases in MD; meanwhile, the hippocampus shows an increase in FA and a decrease in AD and MD. Several key brain regions also show alterations in DTI indices (e.g., insula, caudate, prefrontal cortex). A seed-based analysis centered around a midbrain region of interest obtained from freesurfer segmentation confirms the voxel-based analysis of DTI indices. TBSS successfully captured structural differences within the midbrain and complementary alterations in other main white matter tracts, such as the corticospinal tract and cingulum, suggesting early altered brain connectivity in EP. Correlations between these quantities in the EP group and behavioral scores (i.e., PANSS and CAINS tests) were explored. It was found that midbrain volume noticeably correlates with the Cognitive score of PA and all DTI metrics. FA correlates with the several dimensions of the PANSS, while AD and MD do not show many associations with PANSS or CAINS. ConclusionsOur findings contribute to understanding the midbrain-focused circuitry involvement in EP and complimentary alteration in EP. Our work provides a path for future investigations to inform specific brain-based biomarkers of EP and their relationships to clinical manifestations of the psychosis course.

Background and HypothesisCognitive and negative symptoms in schizophrenia remain poorly treated. Iron dysregulation has been implicated as a potential mechanism underlying cognitive dysfunction and schizophrenia. While elevated postmortem iron in Brodmann areas 10-11 has been linked to schizophrenia, this has not been assessed in vivo. We therefore used iron-sensitive MRI to test whether cortical iron is elevated in individuals with schizophrenia compared to healthy controls. Study DesignWe acquired quantitative susceptibility mapping (QSM) MRI to measure magnetic susceptibility ({chi}), a marker of iron, in 158 participants aged 18-45 (73 with schizophrenia and 76 matched healthy controls). As {chi} is reduced by myelin, we conducted diffusion tensor imaging (DTI) to assess mean diffusivity, an iron-insensitive marker also reduced by myelin. Study ResultsPrimary analyses showed no significant case-control differences in {chi} in the whole cortex (p=0.675) or Brodmann areas 10-11 (p=0.537). Exploratory analyses examined {chi} for 362 cortical regions and a voxelwise analysis, correcting for multiple comparisons. Two left temporo-parieto-occipital (TPO) junction regions showed significantly elevated {chi} in schizophrenia: the posterior TPO junction (d=0.752, p<0.001) and the superior temporal visual area (d=0.638, p=0.033), which remained significant after adjusting for mean diffusivity and clinical covariates (p=0.001 and p=0.023, respectively). Voxelwise analysis confirmed elevated {chi} in schizophrenia in the left TPO junction (peak t=5.62). ConclusionsThis study provides the first in vivo evidence of elevated cortical iron in schizophrenia, suggesting regional iron accumulation may contribute to cortical pathology.

The National Institute of Mental Healths (NIMH) Research Domain Criteria (RDoC) classifies disorders based on shared aspects of behavioral and neurobiological dysfunction. One common behavioral deficit observed in various psychopathologies, namely ADHD, addiction, bipolar disorder, depression, and schizophrenia, is a deficit in working memory performance. However, it is not known to what extent, if any, these disorders share common neurobiological abnormalities that contribute to decrements in performance. The goal of the present study was to examine convergence and divergence of working memory networks across psychopathologies. We used the Activation Likelihood Estimate (ALE) meta-analytic technique to collapse prior data obtained from published studies using the n-back working memory paradigm in individuals with a DSM-criteria diagnosis of the aforementioned disorders. These studies examined areas in the brain that showed increases in activity as a function of working memory-related load compared to a baseline condition, both within subjects and between healthy individuals and those with psychiatric disorder. A meta-analysis of 281 foci covering 81 experiments and 2,629 participants found significant convergence of hyperactivity in medial prefrontal cortex (mPFC) for DSM-diagnosed individuals compared to healthy controls. Foci from ADHD, addiction, bipolar disorder, schizophrenia, and major depression studies contributed to the formation of this cluster. These results provide evidence that default-mode intrusion may constitute a shared seed of dysregulation across multiple psychopathologies, ultimately resulting in poorer working memory performance.

BackgroundElectroconvulsive Therapy (ECT) is one of the most effective treatments for major depressive disorder (DEP). There is recently increasing attention to evaluate ECTs effect on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of DEP patients with healthy participants, investigate whether dynamic functional network connectivity network (dFNC) estimated from rs-fMRI predicts the ECT outcome, and explore the effect of ECT on brain network states. MethodResting-state fMRI data were collected from 119 patients with depression or DEP (76 females), and 61 Healthy (HC) participants (34 females) with an age mean of 52.25 (N=180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59{+/-}6.14 and 11.48{+/-}9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each participant. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each individual spends in each state, called occupancy rate or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, number of treatment, and site. Finally, we evaluated the effectiveness of ECT by comparing pre-and post-ECT OCR of DEP and HC participants. ResultsThe main findings include: 1) DEP patients had significantly lower OCR values than the HC group in a state, where connectivity between CCN and DMN was relatively higher than other states (corrected p= 0.015), 2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, predicted the HDRS changes (R=0.23 corrected p=0.03). This means that those DEP patients who spend less time in this state showed more HDRS change, and 3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spend in state 2 (corrected p=0.03). Finally, we found ECT increases the total traveled distance in DEP. ConclusionOur finding suggests that dFNC features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identified a possible underlying mechanism associated with the ECT effect in DEP patients.

BackgroundApathy is present in about 50% of patients suffering from schizophrenia (SZ) and mood disorders (MD). Even though these two disorders are different, in psychiatry, apathy refers typically to the same unidimensional clinical entity, sharing the same pathophysiological processes and symptoms regardless of the underlying diagnosis. Neurology proposes another perspective: three forms of apathy --emotional, executive, and initiative-- are related to a disturbance in motivational processing, cognitive control, and effort-based decision-making, respectively. We explored whether this latter model can be applied in psychiatry by identifying differences between SZ and MD through a PRISMA meta-analysis of imaging studies on motivational, cognitive control, and effort-based decision-making networks. MethodsWe searched the Sleuth BrainMap database for studies on SZ, MD, and/or healthy controls using tasks that explore motivation, cognitive control, and effort-based decision-making. Twenty-eight functional MRI studies were identified and included for a coordinate-based activation likelihood estimation analysis. ResultsIn SZ, hypoactivity in the motivational structures, a specific hyperactivity in the right cerebellar vermis that has previously been linked to emotional blunting and apathy, and a lack of activation during effort-based decision-making that could imply an impaired reward processing, suggest a dominant form of emotional apathy. In MD, hypoactivity in both cognitive control and motivational structures, which has previously been linked to the co-occurrence of executive difficulty and amotivation, suggests a dominant mix of emotional/executive apathy. ConclusionsDespite a small number of studies, our results could help target new individualized treatment strategies in precision psychiatry based on a multidimensional approach to apathy. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/634295v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@12ad24dorg.highwire.dtl.DTLVardef@54e4cforg.highwire.dtl.DTLVardef@179ede6org.highwire.dtl.DTLVardef@1f1d86c_HPS_FORMAT_FIGEXP M_FIG C_FIG The results of this meta-analysis highlight different activation profiles in SZ and in MD during motivational, cognitive control, and effort-based decision-making tasks, suggesting a dominant form of emotional apathy in SZ and a dominant coexistence of emotional and executive apathy in MD.

Corticostriatal projections form the input level of a circuitry that connects the cerebral cortex, basal ganglia, and thalamus. Three distinct, functional subcircuits exist according to the tripartite model: Sensorimotor cortices projecting mainly to the dorsolateral striatum; associative cortices projecting to the dorsomedial striatum and limbic cortices projecting to the ventral striatum. However, there is to date no atlas that allows researchers to label cortical projection areas belonging to each of these subcircuits separately. To address this research gap, the aim of this study was threefold: First, to systematically review anatomical tracing studies that focused on corticostriatal projections in non-human primates, and to classify their findings according to the tripartite model. Second, to develop an atlas of the human cerebral cortex based on this classification. Third, to test the hypothesis that labels in this atlas show structural connectivity with specific striatal subregions in humans using diffusion-based tractography in a sample of 24 healthy participants. In total, 98 studies met the inclusion criteria for our systematic review. Information about projections from the cortex to the striatum was systematically extracted by Brodmann area, and cortical areas were classified by their dominant efferent projections. Taking known homological and functional similarities and differences between non-human primate and human cortical regions into account, a new human corticostriatal projection (CSP) atlas was developed. Using human diffusion-based tractography analyses, we found that the limbic and sensorimotor atlas labels showed preferential structural connectivity with the ventral and dorsolateral striatum, respectively. However, the pattern of structural connectivity for the associative label showed the greatest degree of overlap with other labels. We provide this new atlas as a freely available tool for neuroimaging studies, where it allows for the first-time delineation of anatomically informed regions-of-interest to study functional subcircuits within the corticostriatal circuitry. This tool will enable specific investigations of subcircuits involved in the pathogenesis of neuropsychiatric illness such as schizophrenia and bipolar disorders. Highlights- Systematic review of anatomical projections from the cerebral cortex to the striatum in non-human primates. - Development of a novel cortical atlas for use in neuroimaging studies focusing on the corticostriatal brain circuitry. - Tractography in human diffusion-weighted imaging data to test if associative, limbic, and sensorimotor cortical atlas labels show preferential connectivity to regions within the striatum.

BackgroundSchizophrenia is characterized by widespread structural brain abnormalities, but associations between structural abnormalities and negative symptom severity are not well understood. Negative symptoms have been conceptualized in a hierarchical structure of two second-order dimensions--motivation and pleasure (MAP) and expression (EXP)--and five first-order domains: anhedonia, avolition, and asociality (MAP), and blunted affect and alogia (EXP). A better understanding of the neural circuitry underlying negative symptom dimensions and domains is important given their reported association with poor functional outcome and lack of available treatments. Study DesignThe meta-analysis included 1,591 individuals with schizophrenia across 16 samples with structural imaging and Scale for Assessment of Negative Symptoms data. The study generated correlations of cortical thickness and subcortical volumes with the negative symptom dimensions and domains. Study resultsNegative symptoms showed mainly negative associations with cortical thickness and subcortical volumes. The effect sizes were small but there was a pattern of associations in predominantly frontal lobe cortical thickness and limbic subcortical volumes. The regional correlation patterns of cortical thickness and subcortical volumes with symptom domains support the conceptualized hierarchical structure of negative symptoms: correlations of MAP domains were stronger with the MAP than EXP dimension, and vice versa. Exploratory analyses with receptor densities further supported the hierarchy. ConclusionOur findings reveal small but consistent associations between negative symptom dimensions and predominantly prefrontal region cortical thickness, and limbic region volumes. These findings advance our understanding of the network of anatomical regions that may contribute to the severity of negative symptoms in schizophrenia.

BackgroundWhite matter (WM) microstructure deficit may be an underlying factor in the brain dysconnectivity hypothesis of schizophrenia using diffusion tensor imaging (DTI). However, WM dysfunction is unclear in schizophrenia. This study aimed to investigate the association between structural deficits and functional disturbances in major WM tracts in schizophrenia. MethodsUsing functional magnetic resonance imaging (fMRI) and DTI, we developed the skeleton-based white matter functional analysis, which could achieve voxel-wise function-structure coupling by projecting the fMRI signals onto a skeleton in WM. We measured the fractional anisotropy (FA) and WM low-frequency oscillation activation and their couplings in ninety-three schizophrenia patients and 122 healthy controls (HCs). An independent open database (62 schizophrenia patients and 71 HCs) was used to test the reproducibility. Finally, associations between WM activations and five behaviour assessment categories (cognition, emotion, motor, personality and sensory) were examined. ResultsThis study revealed a reversed pattern of structure and function in frontotemporal tracts, as follows. (1) WM hyper-activation was associated with reduced FA in schizophrenia. (2) The function-structure association was positive in healthy controls but negative in schizophrenia patients. Furthermore, function-structure dissociation was exacerbated by long illness duration and severe negative symptoms. (3) WM activations were significantly related to cognition and emotion. ConclusionsThis study indicated function-structure dys-coupling, with higher functional activation and reduced structural integration in frontotemporal WM, which may reflect a potential mechanism in WM neuropathologic processing of schizophrenia.

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed, which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3,049 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1,446 individuals with PTSD and 1,603 controls (2152 males/897 females) between ages 18-83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohens d=-0.12, p=0.0021). The tapetum connects the left and right hippocampus, structures for which structure and function have been consistently implicated in PTSD. Results remained significant/similar after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.