A review of structural neuroimaging in schizophrenia: from connectivity to connectomics

Cerebellar and subcortical interplay in cognitive dysmetria: functional network signatures associate with symptom and trait assessments across schizophrenia, bipolar II, and ADHD patients

Cognitive dysmetria suggests a disorganization of cognitive processes, particularly in relation to the cerebellum's role in coordinating thoughts and actions. This phenomenon has been extensively studied in various psychiatric disorders, including schizophrenia (SCHZ), bipolar disorder II (BIPOL), and attention-deficit/hyperactivity disorder (ADHD). Understanding the relationship between cognitive dysmetria and functional connectivity in these disorders would reveal significant insights into their neurobiological underpinnings. This study explores how distinct and similar functional network connectivity (FNC) patterns between brain regions are associated with clinical symptoms and trait assessments across SCHZ, BIPOL, and ADHD patients by examining both working memory and task-free conditions compared to healthy volunteers (HC). Leveraging an open-source fMRI dataset from the UCLA Consortium for Neuropsychiatric Phenomics, we analyzed FNC patterns across 115 default mode and salience network regions, including cortical, subcortical, and cerebellar regions of interest in 135 participants (39 HC, 27 SCHZ patients, 38 BIPOL patients, and 31 ADHD patients). Abnormal FNC patterns compared to HC were localized to the cerebellar, thalamic, striatal, hippocampal, medial prefrontal and anterior insular cortices. Post-hoc multiple comparison analysis showed abnormal network connectivity predominantly in SCHZ and ADHD patients during rest, while the task condition demonstrated differential effects across all three disorders. Statistical analysis using a factor-by-covariance approach (GLM MANCOVA) suggested that regional functional connectivity was associated with select symptoms and traits pointing to neural signatures underlying psychiatric conditions. Our study suggests that examining and harnessing dysfunctional relationships in subcortical and cerebellar regions could provide a new perspective on the neurobiological basis of psychoses and help improve available treatment strategies.

Harmonization of Structural Brain Connectivity through Distribution Matching

The increasing prevalence of multi-site diffusion-weighted magnetic resonance imaging (dMRI) studies potentially offers enhanced statistical power to investigate brain structure. However, these studies face challenges due to variations in scanner hardware and acquisition protocols. While several methods for dMRI data harmonization exist, few specifically address structural brain connectivity. We introduce a new distribution-matching approach to harmonizing structural brain connectivity across different sites and scanners. We evaluate our method using structural brain connectivity data from three distinct datasets (OASIS-3, ADNI-2, and PREVENT-AD), comparing its performance to the widely used ComBat method and the more recent CovBat approach. We examine the impact of harmonization on the correlation of brain connectivity with the Mini-Mental State Examination score and age. Our results demonstrate that our distribution-matching technique more effectively harmonizes structural brain connectivity, often producing stronger and more significant correlations compared to alternative methods. Qualitative assessments illustrate the desired distributional alignment across datasets, while quantitative evaluations confirm robust performance. This work contributes to the growing field of dMRI harmonization, potentially improving the reliability and comparability of structural connectivity studies that combine data from different sources in neuroscientific and clinical research.

Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis

BACKGROUND

Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown.

METHODS

We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning.

RESULTS

Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes.

CONCLUSIONS

Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Implications of altered pyramidal cell morphology on clinical symptoms of neurodevelopmental disorders

The prevalence of pyramidal cells (PCs) in the mammalian cerebral cortex underscore their value as they play a crucial role in various brain functions, ranging from cognition, sensory processing, to motor output. PC morphology significantly influences brain connectivity and plays a critical role in maintaining normal brain function. Pathological alterations to PC morphology are thought to contribute to the aetiology of neurodevelopmental disorders such as autism spectrum disorder (ASD) and schizophrenia. This review explores the relationship between abnormalities in PC morphology in key cortical areas and the clinical manifestations in schizophrenia and ASD. We focus largely on human postmortem studies and provide evidence that dendritic segment length, complexity and spine density are differentially affected in these disorders. These morphological alterations can lead to disruptions in cortical connectivity, potentially contributing to the cognitive and behavioural deficits observed in these disorders. Furthermore, we highlight the importance of investigating the functional and structural characteristics of PCs in these disorders to illuminate the underlying pathogenesis and stimulate further research in this area.

Characterizing normal perinatal development of the human brain structural connectivity

Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.

Positive Effects of Uric Acid on White Matter Microstructures and Treatment Response in Patients With Schizophrenia

BACKGROUND AND HYPOTHESIS

Schizophrenia involves microstructural changes in white matter (WM) tracts. Oxidative stress is a key factor causing WM damage by hindering oligodendrocyte development and myelin maturation. Uric acid (UA), an endogenous antioxidant, may protect against oxidative stress. We investigated the effect of UA on WM connectivity in antipsychotic-naive or -free patients with early- or chronic-stage schizophrenia.

STUDY DESIGN

A total of 192 patients with schizophrenia (122 recent-onset [ROS] and 70 chronic [CS]) and 107 healthy controls (HCs) participated in this study. Diffusion tensor imaging data and serum UA levels at baseline were obtained.

STUDY RESULTS

Fractional anisotropy was lower in the widespread WM regions across the whole brain, and diffusivity measures were higher in both schizophrenia groups than in HCs. The CS group showed lower diffusivity in some WM tracts than the ROS or HC groups. The linear relationship of serum UA levels with axial and mean diffusivity in the right frontal region was significantly different between schizophrenia stages, which was driven by a negative association in the CS group. WM diffusivity associated with serum UA levels correlated with 8-week treatment responses only in patients with CS, suggesting UA to be protective against long-term schizophrenia.

CONCLUSIONS

UA may protect against the WM damage associated with the progression of schizophrenia by reducing oxidative stress and supporting WM repair against oxidative damage. These results provide insights into the positive role of UA and may facilitate the development of novel disease-modifying therapies.

Whole-brain intrinsic functional connectivity predicts symptoms and functioning in early psychosis

Theories of psychotic illness suggest that abnormal intrinsic functional connectivity may explain its characteristic positive and disorganization symptoms as well as lead to impaired general functioning. Here we used resting state functional magnetic resonance imaging (fMRI) to evaluate associations between these symptoms and the degree to which global connectivity is abnormal in early psychosis (EP). Eighty-six healthy controls (HCs) and 108 individuals with EP with resting state fMRI data were included in primary analyses. The EP group included 83 participants with schizophrenia-spectrum disorders and 25 with bipolar disorder type I with psychotic features. A global intrinsic connectivity "similarity index" for each EP individual was determined by calculating its correlation with the average HC connectivity matrix extracted using Schaefer atlases of multiple parcellations (100, 200, 300, and 400 region parcellations). As hypothesized, connectivity similarity with the average HC matrix was negatively associated with Brief Psychiatric Rating Scale total score, Scale for the Assessment of Positive Symptoms total score, and disorganization symptoms. Similarity was also positively associated with Global Assessment of Functioning score. Results were not driven by sex or diagnosis effects and were consistent across parcellation schemes. These results support the hypothesis that changes in whole-brain connectivity patterns are associated with psychosis symptoms and support the use of functional connectivity as a biomarker for these symptoms in EP.

Multivariate brain-behaviour associations in psychiatric disorders

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Functional myelin in cognition and neurodevelopmental disorders

In vertebrates, oligodendrocytes (OLs) are glial cells of the central nervous system (CNS) responsible for the formation of the myelin sheath that surrounds the axons of neurons. The myelin sheath plays a crucial role in the transmission of neuronal information by promoting the rapid saltatory conduction of action potentials and providing neurons with structural and metabolic support. Saltatory conduction, first described in the peripheral nervous system (PNS), is now generally recognized as a universal evolutionary innovation to respond quickly to the environment: myelin helps us think and act fast. Nevertheless, the role of myelin in the central nervous system, especially in the brain, may not be primarily focused on accelerating conduction speed but rather on ensuring precision. Its principal function could be to coordinate various neuronal networks, promoting their synchronization through oscillations (or rhythms) relevant for specific information processing tasks. Interestingly, myelin has been directly involved in different types of cognitive processes relying on brain oscillations, and myelin plasticity is currently considered to be part of the fundamental mechanisms for memory formation and maintenance. However, despite ample evidence showing the involvement of myelin in cognition and neurodevelopmental disorders characterized by cognitive impairments, the link between myelin, brain oscillations, cognition and disease is not yet fully understood. In this review, we aim to highlight what is known and what remains to be explored to understand the role of myelin in high order brain processes.

Aberrant thalamocortical connectivity and shifts between the resting state and task state in patients with schizophrenia

Prominent pathological hypotheses for schizophrenia include auditory processing deficits and dysconnectivity within cerebral networks. However, most neuroimaging studies have focused on impairments in either resting-state or task-related functional connectivity in patients with schizophrenia. The aims of our study were to examine (1) blood oxygen level-dependent (BOLD) signals during auditory steady-state response (ASSR) tasks, (2) functional connectivity during the resting-state and ASSR tasks and (3) state shifts between the resting-state and ASSR tasks in patients with schizophrenia. To reduce the functional consequences of scanner noise, we employed resting-state and sparse sampling auditory fMRI paradigms in 25 schizophrenia patients and 25 healthy controls. Auditory stimuli were binaural click trains at frequencies of 20, 30, 40 and 80 Hz. Based on the detected ASSR-evoked BOLD signals, we examined the functional connectivity between the thalamus and bilateral auditory cortex during both the resting state and ASSR task state, as well as their alterations. The schizophrenia group exhibited significantly diminished BOLD signals in the bilateral auditory cortex and thalamus during the 80 Hz ASSR task (corrected p < 0.05). We observed a significant inverse relationship between the resting state and ASSR task state in altered functional connectivity within the thalamo-auditory network in schizophrenia patients. Specifically, our findings demonstrated stronger functional connectivity in the resting state (p < 0.004) and reduced functional connectivity during the ASSR task (p = 0.048), which was mediated by abnormal state shifts, within the schizophrenia group. These results highlight the presence of abnormal thalamocortical connectivity associated with deficits in the shift between resting and task states in patients with schizophrenia.

Impaired Cortico-Thalamo-Cerebellar Integration Across Schizophrenia, Bipolar II, and Attention Deficit Hyperactivity Disorder Patients Suggests Potential Neural Signatures for Psychiatric Illness

Understanding aberrant functional changes between brain regions has shown promise for characterizing and differentiating the symptoms associated with progressive psychiatric disorders. The functional integration between the thalamus and cerebellum significantly influences learning and memory in cognition. Observed in schizophrenic patients, dysfunction within the corticalthalamocerebellar (CTC) circuitry is linked to challenges in prioritizing, processing, coordinating, and responding to information. This study explored whether abnormal CTC functional network connectivity patterns are present across schizophrenia (SCHZ) patients, bipolar II disorder (BIPOL) patients, and ADHD patients by examining both task- and task-free conditions compared to healthy volunteers (HC). Leveraging fMRI data from 135 participants (39 HC, 27 SCHZ patients, 38 BIPOL patients, and 31 ADHD patients), we analyzed functional network connectivity (FNC) patterns across 115 cortical, thalamic, subcortical, and cerebellar regions of interest (ROIs). Guiding our investigation: First, do the brain regions of the CTC circuit exhibit distinct abnormal patterns at rest in SCHZ, ADHD, and BIPOL? Second, do working memory tasks in these patients engage common regions of the circuit in similar or unique patterns? Consistent with previous findings, our observations revealed FNC patterns constrained in the cerebellar, thalamic, striatal, hippocampal, medial prefrontal and insular cortices across all three psychiatric cohorts when compared to controls in both task and task-free conditions. Post hoc analysis suggested a predominance in schizophrenia and ADHD patients during rest, while the task condition demonstrated effects across all three disorders. Factor-by-covariance GLM MANOVA further specified regions associated with clinical symptoms and trait assessments. Our study provides evidence suggesting that dysfunctional CTC circuitry in both task-free and task-free conditions may be an important broader neural signature of psychiatric illness.

The Gaze of Schizophrenia Patients Captured by Bottom-up Saliency

Schizophrenia (SCHZ) notably impacts various human perceptual modalities, including vision. Prior research has identified marked abnormalities in perceptual organization in SCHZ, predominantly attributed to deficits in bottom-up processing. Our study introduces a novel paradigm to differentiate the roles of top-down and bottom-up processes in visual perception in SCHZ. We analysed eye-tracking fixation ground truth maps from 28 SCHZ patients and 25 healthy controls (HC), comparing these with two mathematical models of visual saliency: one bottom-up, based on the physical attributes of images, and the other top-down, incorporating machine learning. While the bottom-up (GBVS) model revealed no significant overall differences between groups (beta = 0.01, p = 0.281, with a marginal increase in SCHZ patients), it did show enhanced performance by SCHZ patients with highly salient images. Conversely, the top-down (EML-Net) model indicated no general group difference (beta = -0.03, p = 0.206, lower in SCHZ patients) but highlighted significantly reduced performance in SCHZ patients for images depicting social interactions (beta = -0.06, p < 0.001). Over time, the disparity between the groups diminished for both models. The previously reported bottom-up bias in SCHZ patients was apparent only during the initial stages of visual exploration and corresponded with progressively shorter fixation durations in this group. Our research proposes an innovative approach to understanding early visual information processing in SCHZ patients, shedding light on the interplay between bottom-up perception and top-down cognition.

De Novo Variants Found in Three Distinct Schizophrenia Populations Hit a Common Core Gene Network Related to Microtubule and Actin Cytoskeleton Gene Ontology Classes

Schizophrenia (SZ) is a heterogeneous and debilitating psychiatric disorder with a strong genetic component. To elucidate functional networks perturbed in schizophrenia, we analysed a large dataset of whole-genome studies that identified SNVs, CNVs, and a multi-stage schizophrenia genome-wide association study. Our analysis identified three subclusters that are interrelated and with small overlaps: GO:0007017~Microtubule-Based Process, GO:00015629~Actin Cytoskeleton, and GO:0007268~SynapticTransmission. We next analysed three distinct trio cohorts of 75 SZ Algerian, 45 SZ French, and 61 SZ Japanese patients. We performed Illumina HiSeq whole-exome sequencing and identified de novo mutations using a Bayesian approach. We validated 88 de novo mutations by Sanger sequencing: 35 in French, 21 in Algerian, and 32 in Japanese SZ patients. These 88 de novo mutations exhibited an enrichment in genes encoding proteins related to GO:0051015~actin filament binding (p = 0.0011) using David, and enrichments in GO: 0003774~transport (p = 0.019) and GO:0003729~mRNA binding (p = 0.010) using Amigo. One of these de novo variant was found in CORO1C coding sequence. We studied Coro1c haploinsufficiency in a Coro1c+/- mouse and found defects in the corpus callosum. These results could motivate future studies of the mechanisms surrounding genes encoding proteins involved in transport and the cytoskeleton, with the goal of developing therapeutic intervention strategies for a subset of SZ cases.

Lower fractional anisotropy without evidence for neuro-inflammation in patients with early-phase schizophrenia spectrum disorders

Various lines of research suggest immune dysregulation as a potential therapeutic target for negative and cognitive symptoms in schizophrenia spectrum disorders (SSD). Immune dysregulation would lead to higher extracellular free-water (EFW) in cerebral white matter (WM), which may partially underlie the frequently reported lower fractional anisotropy (FA) in SSD. We aim to investigate differences in EFW concentrations - a presumed proxy for neuro-inflammation - between early-phase SSD patients (n = 55) and healthy controls (HC; n = 37), and to explore immunological and cognitive correlates. To increase specificity for EFW, we study several complementary magnetic resonance imaging contrasts that are sensitive to EFW. FA, mean diffusivity (MD), magnetization transfer ratio (MTR), myelin water fraction (MWF) and quantitative T1 and T2 were calculated from diffusion-weighted imaging (DWI), magnetization transfer imaging (MTI) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT). For each measure, WM skeletons were constructed with tract-based spatial statistics. Multivariate SSD-HC comparisons with WM skeletons and their average values (i.e. global WM) were not statistically significant. In voxel-wise analyses, FA was significantly lower in SSD in the genu of the corpus callosum and in the left superior longitudinal fasciculus (p < 0.04). Global WM measures did not correlate with immunological markers (i.e. IL1-RA, IL-6, IL-8, IL-10 and CRP) or cognition in HC and SSD after corrections for multiple comparisons. We confirmed lower FA in early-phase SSD patients. However, nonFA measures did not provide additional evidence for immune dysregulation or for higher EFW as the primary mechanism underlying the reported lower FA values in SSD.

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

Multivariate Associations Among White Matter, Neurocognition, and Social Cognition Across Individuals With Schizophrenia Spectrum Disorders and Healthy Controls

BACKGROUND AND HYPOTHESIS

Neurocognitive and social cognitive abilities are important contributors to functional outcomes in schizophrenia spectrum disorders (SSDs). An unanswered question of considerable interest is whether neurocognitive and social cognitive deficits arise from overlapping or distinct white matter impairment(s).

STUDY DESIGN

We sought to fill this gap, by harnessing a large sample of individuals from the multi-center Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) dataset, unique in its collection of advanced diffusion imaging and an extensive battery of cognitive assessments. We applied canonical correlation analysis to estimates of white matter microstructure, and cognitive performance, across people with and without an SSD.

STUDY RESULTS

Our results established that white matter circuitry is dimensionally and strongly related to both neurocognition and social cognition, and that microstructure of the uncinate fasciculus and the rostral body of the corpus callosum may assume a "privileged role" subserving both. Further, we found that participant-wise estimates of white matter microstructure, weighted by cognitive performance, were largely consistent with participants' categorical diagnosis, and predictive of (cross-sectional) functional outcomes.

CONCLUSIONS

The demonstrated strength of the relationship between white matter circuitry and neurocognition and social cognition underscores the potential for using relationships among these variables to identify biomarkers of functioning, with potential prognostic and therapeutic implications.

Structural impairment in superficial and deep white matter in schizophrenia

OBJECTIVE

Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects.

METHODS

We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing.

RESULTS

Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing.

CONCLUSION

We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.

Characterizing normal perinatal development of the human brain structural connectivity

Early brain development is characterized by the formation of a highly organized structural connectome. The interconnected nature of this connectome underlies the brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development and imaging difficulties. Combined with high inter-subject variability, these factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational framework, based on spatio-temporal averaging, for determining such baselines. We used this framework to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in perinatal stage. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. We observed increases in global and local efficiency, a decrease in characteristic path length, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. The new computational method and results are useful for assessing normal and abnormal development of the structural connectome early in life.

Association between enhanced carbonyl stress and decreased apparent axonal density in schizophrenia by multimodal white matter imaging

Carbonyl stress is a condition featuring increased rich reactive carbonyl compounds, which facilitate the formation of advanced glycation end products including pentosidine. We previously reported the relationship between enhanced carbonyl stress and disrupted white matter integrity in schizophrenia, although which microstructural component is disrupted remained unclear. In this study, 32 patients with schizophrenia (SCZ) and 45 age- and gender-matched healthy volunteers (HC) were recruited. We obtained blood samples for carbonyl stress markers (plasma pentosidine and serum pyridoxal) and multi-modal magnetic resonance imaging measures of white matter microstructures including apparent axonal density (intra-cellular volume fraction (ICVF)) and orientation (orientation dispersion index (ODI)), and inflammation (free water (FW)). In SCZ, the plasma pentosidine level was significantly increased. Group comparison revealed that mean white matter values were decreased for ICVF, and increased for FW. We found a significant negative correlation between the plasma pentosidine level and mean ICVF values in SCZ, and a significant negative correlation between the serum pyridoxal level and mean ODI value in HC, regardless of age. Our results suggest an association between enhanced carbonyl stress and axonal abnormality in SCZ.

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

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

Neural substrates of verbal memory impairment in schizophrenia: A multimodal connectomics study

While verbal memory is among the most compromised cognitive domains in schizophrenia (SZ), its neural substrates remain elusive. Here, we explored the structural and functional brain network correlates of verbal memory impairment in SZ. We acquired diffusion and resting-state functional MRI data of 49 SZ patients, classified as having preserved (VMP, n = 22) or impaired (VMI, n = 26) verbal memory based on the List Learning task, and 55 healthy controls (HC). Structural and functional connectivity matrices were obtained and analyzed to assess associations with disease status (SZ vs. HC) and verbal memory impairment (VMI vs. VMP) using two complementary data-driven approaches: threshold-free network-based statistics (TFNBS) and hybrid connectivity independent component analysis (connICA). TFNBS showed altered connectivity in SZ patients compared with HC (p < .05, FWER-corrected), with distributed structural changes and functional reorganization centered around sensorimotor areas. Specifically, functional connectivity was reduced within the visual and somatomotor networks and increased between visual areas and associative and subcortical regions. Only a tiny cluster of increased functional connectivity between visual and bilateral parietal attention-related areas correlated with verbal memory dysfunction. Hybrid connICA identified four robust traits, representing fundamental patterns of joint structural-functional connectivity. One of these, mainly capturing the functional connectivity profile of the visual network, was significantly associated with SZ (HC vs. SZ: Cohen's d = .828, p < .0001) and verbal memory impairment (VMP vs. VMI: Cohen's d = -.805, p = .01). We suggest that aberrant connectivity of sensorimotor networks may be a key connectomic signature of SZ and a putative biomarker of SZ-related verbal memory impairment, in consistency with bottom-up models of cognitive disruption.

Connecting Neurobiological Features with Interregional Dysconnectivity in Social-Cognitive Impairments of Schizophrenia

Schizophrenia (SZ) is a devastating psychiatric disorder affecting about 1% of the world's population. Social-cognitive impairments in SZ prevent positive social interactions and lead to progressive social withdrawal. The neurobiological underpinnings of social-cognitive symptoms remain poorly understood, which hinders the development of novel treatments. At the whole-brain level, an abnormal activation of social brain regions and interregional dysconnectivity within social-cognitive brain networks have been identified as major contributors to these symptoms. At the cellular and subcellular levels, an interplay between oxidative stress, neuroinflammation and N-methyl-D-aspartate receptor hypofunction is thought to underly SZ pathology. However, it is not clear how these molecular processes are linked with interregional dysconnectivity in the genesis of social-cognitive symptoms. Here, we aim to bridge the gap between macroscale (connectivity analyses) and microscale (molecular and cellular mechanistic) knowledge by proposing impaired myelination and the disinhibition of local microcircuits as possible causative biological pathways leading to dysconnectivity and abnormal activity of the social brain. Furthermore, we recommend electroencephalography as a promising translational technique that can foster pre-clinical drug development and discuss attractive drug targets for the treatment of social-cognitive symptoms in SZ.

Genetic risk for schizophrenia is associated with increased proportion of indirect connections in brain networks revealed by a semi-metric analysis: evidence from population sample stratified for polygenic risk

Research studies based on tractography have revealed a prominent reduction of asymmetry in some key white-matter tracts in schizophrenia (SCZ). However, we know little about the influence of common genetic risk factors for SCZ on the efficiency of routing on structural brain networks (SBNs). Here, we use a novel recall-by-genotype approach, where we sample young adults from a population-based cohort (ALSPAC:N genotyped = 8,365) based on their burden of common SCZ risk alleles as defined by polygenic risk score (PRS). We compared 181 individuals at extremes of low (N = 91) or high (N = 90) SCZ-PRS under a robust diffusion MRI-based graph theoretical SBN framework. We applied a semi-metric analysis revealing higher SMR values for the high SCZ-PRS group compared with the low SCZ-PRS group in the left hemisphere. Furthermore, a hemispheric asymmetry index showed a higher leftward preponderance of indirect connections for the high SCZ-PRS group compared with the low SCZ-PRS group (PFDR < 0.05). These findings might indicate less efficient structural connectivity in the higher genetic risk group. This is the first study in a population-based sample that reveals differences in the efficiency of SBNs associated with common genetic risk variants for SCZ.

Symptom-circuit mappings of the schizophrenia connectome

OBJECTIVE

This paper aims to model the anatomical circuits underlying schizophrenia symptoms, and to explore patterns of abnormal connectivity among brain networks affected by psychopathology.

METHODS

T1 magnetic resonance imaging (MRI), diffusion weighted imaging (DWI), and resting-state functional MRI (rsfMRI) were obtained from a total of 126 patients with schizophrenia who were recruited for the study. The images were processed using the Omniscient software (https://www.o8t. com). We further apply the use of the Hollow-tree Super (HoTS) method to gain insights into what brain regions had abnormal connectivity that might be linked to the symptoms of schizophrenia.

RESULTS

The Positive and Negative Symptom Scale is characterised into 6 factors. Each symptom is mapped with specific anatomical abnormalities and circuits. Comparison between factors reveals co-occurrence in parcels in Factor 1 and Factor 2. Multiple large-scale networks are involved in SCZ symptomatology, with functional connectivity within Default Mode Network (DMN) and Central Executive Network (CEN) regions most frequently associated with measures of psychopathology.

CONCLUSION

We present a summary of the relevant anatomy for regions of the cortical areas as part of a larger effort to understand its contribution in schizophrenia. This unique machine learning-type approach maps symptoms to specific brain regions and circuits by bridging the diagnostic subtypes and analysing the features of the connectome.

Biological hypotheses, risk factors, and biomarkers of schizophrenia

Both the discovery of biomarkers of schizophrenia and the verification of biological hypotheses of schizophrenia are an essential part of the process of understanding the etiology of this mental disorder. Schizophrenia has long been considered a neurodevelopmental disease whose symptoms are caused by impaired synaptic signal transduction and brain neuroplasticity. Both the onset and chronic course of schizophrenia are associated with risk factors-induced disruption of brain function and the establishment of a new homeostatic setpoint characterized by biomarkers. Different risk factors and biomarkers can converge to the same symptoms of schizophrenia, suggesting that the primary cause of the disease can be highly individual. Schizophrenia-related biomarkers include measurable biochemical changes induced by stress (elevated allostatic load), mitochondrial dysfunction, neuroinflammation, oxidative and nitrosative stress, and circadian rhythm disturbances. Here is a summary of selected valid biological hypotheses of schizophrenia formulated based on risk factors and biomarkers, neurodevelopment, neuroplasticity, brain chemistry, and antipsychotic medication. The integrative neurodevelopmental-vulnerability-neurochemical model is based on current knowledge of the neurobiology of the onset and progression of the disease and the effects of antipsychotics and psychotomimetics and reflects the complex and multifactorial nature of schizophrenia.

The Difficult Path to the Discovery of Novel Treatments in Psychiatric Disorders

CNS diseases, including psychiatric disorders, represent a significant opportunity for the discovery and development of new drugs and therapeutic treatments with the potential to have a significant impact on human health. CNS diseases, however, present particular challenges to therapeutic discovery efforts, and psychiatric diseases/disorders may be among the most difficult. With specific exceptions such as psychostimulants for ADHD, a large number of psychiatric patients are resistant to existing treatments. In addition, clinicians have no way of knowing which psychiatric patients will respond to which drugs. By definition, psychiatric diagnoses are syndromal in nature; determinations of efficacy are often self-reported, and drug discovery is largely model-based. While such models of psychiatric disease are amenable to screening for new drugs, whether cellular or whole-animal based, they have only modest face validity and, more importantly, predictive validity. Multiple academic, pharmaceutical industry, and government agencies are dedicated to the translation of new findings about the neurobiology of major psychiatric disorders into the discovery and advancement of novel therapies. The collaboration of these agencies provide a pathway for developing new therapeutics. These efforts will be greatly helped by recent advances in understanding the genetic bases of psychiatric disorders, the ongoing search for diagnostic and therapy-responsive biomarkers, and the validation of new animal models.

Associated Genetics and Connectomic Circuitry in Schizophrenia and Bipolar Disorder

BACKGROUND

Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions that can involve symptoms of psychosis and cognitive dysfunction. The 2 conditions share symptomatology and genetic etiology and are regularly hypothesized to share underlying neuropathology. Here, we examined how genetic liability to SCZ and BD shapes normative variations in brain connectivity.

METHODS

We examined the effect of the combined genetic liability for SCZ and BD on brain connectivity from two perspectives. First, we examined the association between polygenic scores for SCZ and BD for 19,778 healthy subjects from the UK Biobank and individual variation in brain structural connectivity reconstructed by means of diffusion weighted imaging data. Second, we conducted genome-wide association studies using genotypic and imaging data from the UK Biobank, taking SCZ-/BD-involved brain circuits as phenotypes of interest.

RESULTS

Our findings showed brain circuits of superior parietal and posterior cingulate regions to be associated with polygenic liability for SCZ and BD, circuitry that overlaps with brain networks involved in disease conditions (r = 0.239, p < .001). Genome-wide association study analysis showed 9 significant genomic loci associated with SCZ-involved circuits and 14 loci associated with BD-involved circuits. Genes related to SCZ-/BD-involved circuits were significantly enriched in gene sets previously reported in genome-wide association studies for SCZ and BD.

CONCLUSIONS

Our findings suggest that polygenic liability of SCZ and BD is associated with normative individual variation in brain circuitry.

White matter microstructure and sleep-wake disturbances in individuals at ultra-high risk of psychosis

Aim

White matter changes in individuals at ultra-high risk for psychosis (UHR) may be involved in the transition to psychosis. Sleep-wake disturbances commonly precede the first psychotic episode and predict development of psychosis. We examined associations between white matter microstructure and sleep-wake disturbances in UHR individuals compared to healthy controls (HC), as well as explored the confounding effect of medication, substance use, and level of psychopathology.

Methods

Sixty-four UHR individuals and 35 HC underwent clinical interviews and diffusion weighted imaging. Group differences on global and callosal mean fractional anisotropy (FA) was tested using general linear modeling. Sleep-wake disturbances were evaluated using the subjective measures disturbed sleep index (DSI) and disturbed awakening index (AWI) from the Karolinska Sleep Questionnaire, supported by objective sleep measures from one-night actigraphy. The primary analyses comprised partial correlation analyses between global FA/callosal FA and sleep-wake measures. Secondary analyses investigated multivariate patterns of covariance between measures of sleep-wake disturbances and FA in 48 white matter regions of interest using partial least square correlations.

Results

Ultra-high risk for psychosis individuals displayed lower global FA (F = 14.56, p < 0.001) and lower callosal FA (F = 11.34, p = 0.001) compared to HC. Subjective sleep-wake disturbances were significantly higher among the UHR individuals (DSI: F = 27.59, p < 0.001, AWI: F = 36.42, p < 0.001). Lower callosal FA was correlated with increased wake after sleep onset (r = -0.34, p = 0.011) and increased sleep fragmentation index (r = -0.31, p = 0.019) in UHR individuals. Multivariate analyses identified a pattern of covariance in regional FA which were associated with DSI and AWI in UHR individuals (p = 0.028), but not in HC. Substance use, sleep medication and antipsychotic medication did not significantly confound these associations. The association with objective sleep-wake measures was sustained when controlling for level of depressive and UHR symptoms, but symptom level confounded the covariation between FA and subjective sleep-wake measures in the multivariate analyses.

Conclusion

Compromised callosal microstructure in UHR individuals was related to objectively observed disruptions in sleep-wake functioning. Lower FA in ventrally located regions was associated with subjectively measured sleep-wake disturbances and was partly explained by psychopathology. These findings call for further investigation of sleep disturbances as a potential treatment target.

Cognitive and functional deficits are associated with white matter abnormalities in two independent cohorts of patients with schizophrenia

BACKGROUND

Significant evidence links white matter (WM) microstructural abnormalities to cognitive impairment in schizophrenia (SZ), but the relationship of these abnormalities with functional outcome remains unclear.

METHODS

In two independent cohorts (C1, C2), patients with SZ were divided into two subgroups: patients with higher cognitive performance (SZ-HCP-C1, n = 25; SZ-HCP-C2, n = 24) and patients with lower cognitive performance (SZ-LCP-C1, n = 25; SZ-LCP-C2, n = 24). Healthy controls (HC) were included in both cohorts (HC-C1, n = 52; HC-C2, n = 27). We compared fractional anisotropy (FA) of the whole-brain WM skeleton between the three groups (SZ-LCP, SZ-HCP, HC) by a whole-brain exploratory approach and an atlas-defined WM regions-of-interest approach via tract-based spatial statistics. In addition, we explored whether FA values were associated with Global Assessment of Functioning (GAF) scores in the SZ groups.

RESULTS

In both cohorts, mean FA values of whole-brain WM skeleton were significantly lower in the SCZ-LCP group than in the SCZ-HCP group. Whereas in C1 the FA of the frontal part of the left inferior fronto-occipital fasciculus (IFOF) was positively correlated with GAF score, in C2 the FA of the temporal part of the left IFOF was positively correlated with GAF score.

CONCLUSIONS

We provide robust evidence for WM microstructural abnormalities in SZ. These abnormalities are more prominent in patients with low cognitive performance and are associated with the level of functioning.

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Altered Surface Area Covariance in the Mentalizing Network in Schizophrenia: Insight Into Theory of Mind Processing

BACKGROUND

Theory of mind (ToM), the cognitive capacity to attribute mental states to self and others, is robustly affected in schizophrenia. The neural substrates of ToM impairment have been largely studied with functional imaging, but little is known about structural abnormalities. We compared structural covariance (between-subjects correlations of brain regional measures) of magnetic resonance imaging-based cortical surface area between patients with schizophrenia and healthy control subjects and between schizophrenia subgroups based on the patients' ToM ability to examine ToM-specific effects on structural covariance in schizophrenia.

METHODS

T1-weighted structural images were acquired on a 3T magnetic resonance imaging scanner, and ToM was assessed with the Hinting Task for 104 patients with schizophrenia and 69 healthy control subjects. The sum of surface area was computed for 12 regions of interest selected and compared between groups to examine structural covariance within the often reported mentalizing network: rostral and caudal middle frontal gyrus, inferior parietal lobule, precuneus, and middle and superior temporal gyrus. High and low ToM groups were defined using a median split on the Hinting Task.

RESULTS

Cortical surface contraction was observed in the schizophrenia group, predominantly in temporoparietal regions. Patients with schizophrenia also exhibited significantly stronger covariance between the right rostral middle frontal gyrus and the right superior temporal gyrus than control subjects (r = 4.015; p < .001). Direct comparisons between high and low ToM subgroups revealed stronger contralateral frontotemporal covariances in the low ToM group.

CONCLUSIONS

Our results provide evidence for structural changes underlying ToM impairments in schizophrenia that need to be confirmed to develop new therapeutic perspectives.

Tract-specific white matter microstructural alterations in subjects with schizophrenia and unaffected first-degree relatives

White matter tracts alterations have been reported in schizophrenia (SZ), but whether such abnormalities are associated with the effects of the disorder itself and/or genetic vulnerability remains unclear. Moreover, the specific patterns of different parts of these altered tracts have been less well studied. Thus, diffusion-weighted images were acquired from 38 healthy controls (HCs), 48 schizophrenia patients, and 33 unaffected first-degree relatives of SZs (FDRs). Diffusion properties of the 25 major tracts automatically extracted with probabilistic tractography were calculated and compared among groups. Regarding the peripheral regions of the tracts, significantly higher diffusivity values in the left superior longitudinal fasciculus (SLF) and the left anterior thalamic radiation (ATR) were observed in SZs than in HCs and unaffected FDRs. However, there were no significant differences between HCs and FDRs in these two tracts. While no main effects of group with respect to the core regions of the 25 tracts survived multiple comparisons correction, FDRs had significantly higher diffusivity values in the left medial lemniscus and lower diffusivity values in the middle cerebellar peduncle than HCs and SZs. These findings enhance the understanding of the abnormal patterns in the peripheral and core regions of the tracts in SZs and those at high genetic risk for schizophrenia. Our results suggest that alterations in the peripheral regions of the left SLF and ATR are features of established illness rather than genetic predisposition, which may serve as critical neural substrates for the psychopathology of schizophrenia.

Alterations in white matter network dynamics in patients with schizophrenia and bipolar disorder

Emerging evidence suggests white matter network abnormalities in patients with schizophrenia (SZ) and bipolar disorder (BD), but the alterations in dynamics of the white matter network in patients with SZ and BD are largely unknown. The white matter network of patients with SZ (n = 45) and BD (n = 47) and that of healthy controls (HC, n = 105) were constructed. We used dynamics network control theory to quantify the dynamics metrics of the network, including controllability and synchronizability, to measure the ability to transfer between different states. Experiments show that the patients with SZ and BD showed decreasing modal controllability and synchronizability and increasing average controllability. The correlations between the average controllability and synchronizability of patients were broken, especially for those with SZ. The patients also showed alterations in brain regions with supercontroller roles and their distribution in the cognitive system. Finally, we were able to accurately discriminate and predict patients with SZ and BD. Our findings provide novel dynamic metrics evidence that patients with SZ and BD are characterized by a selective disruption of brain network controllability, potentially leading to reduced brain state transfer capacity, and offer new guidance for the clinical diagnosis of mental illness.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Different patterns of white matter microstructural alterations between psychotic and non-psychotic bipolar disorder

This study aimed to investigate alterations in white matter (WM) microstructure in patients with psychotic and non-psychotic bipolar disorder (PBD and NPBD, respectively). We used 3T-magnetic resonance imaging to examine 29 PBD, 23 NPBD, and 65 healthy control (HC) subjects. Using tract-based spatial statistics for diffusion tensor imaging data, we compared fractional anisotropy (FA) and mean diffusion (MD) pairwise among the PBD, NPBD, and HC groups. We found several WM areas of decreased FA or increased MD in the PBD and NPBD groups compared to HC. PBD showed widespread FA decreases in the corpus callosum as well as the bilateral internal capsule and fornix. However, NPBD showed local FA decreases in a part of the corpus callosum body as well as in limited regions within the left cerebral hemisphere, including the anterior and posterior corona radiata and the cingulum. In addition, both PBD and NPBD shared widespread MD increases across the posterior corona radiata, cingulum, and sagittal stratum. These findings suggest that widespread WM microstructural alterations might be a common neuroanatomical characteristic of bipolar disorder, regardless of being psychotic or non-psychotic. Particularly, PBD might involve extensive inter-and intra-hemispheric WM connectivity disruptions.

Single episode brief psychotic disorder versus bipolar disorder: A diffusion tensor imaging and executive functions study

BACKGROUND

Despite fast progress in neuroscientific approaches, the neurobiological continuum links psychotic spectrum, and affective disorder is obscure. White matter WM abnormalities found utilizing Diffusion Tensor Imaging (DTI) showing impaired communication in both disorders have been consistently demonstrated; however, direct comparisons of findings between them are scarce. This study aims to study WM abnormalities in single episode bipolar I disorder, and single episode brief psychotic disorder related to healthy control with the association of executive function.

METHODS

A cross-sectional case-control study was used to assess 60 subjects divided into 20 patients with single episode bipolar I disorder, 20 individuals with single episode brief psychotic disorder (both groups of patients were in remission), and 20 healthy controls. The present study examined the superior longitudinal fasciculus (SLF), and cingulum bundle fractional anisotropy (FA) determined from DTI images symmetrically and connected these results with cognitive functions as assessed by the trail making test (TMT) and Wisconsin card sorting test (WCST).

RESULTS

DTI data indicated that the psychotic group had a significant decrease in FA of the right SLF (p-value less than 0.001), left SLF (p-value less than 0.001), and left cingulum (p-value less than 0.001) than the bipolar I group. In terms of executive functioning, the psychotic group performed significantly worse than the bipolar I group on the TMT part B (p-value less than 0.001), the WCST (number of classifications fulfilled) (p-value less than 0.001), and perseverative errors (p-value less than 0.001).

CONCLUSION

Even after clinical remission, individuals with single episode brief psychotic disorder had more pronounced white matter impairments and executive function deficiencies than individuals with single episode bipolar I disorder.

Abnormal global-brain functional connectivity and its relationship with cognitive deficits in drug-naive first-episode adolescent-onset schizophrenia

Abnormal functional connectivity (FC) has been reported in drug-naive first-episode adolescent-onset schizophrenia (AOS) with inconsistent results due to differently selected regions of interest. The voxel-wise global-brain functional connectivity (GFC) analysis can help explore abnormal FC in an unbiased way in AOS. A total of 48 drug-naive first-episode AOS as well as 31 sex-, age- and education-matched healthy controls were collected. Data were subjected to GFC, correlation analysis and support vector machine analyses. Compared with healthy controls, the AOS group exhibited increased GFC in the right middle frontal gyrus (MFG), and decreased GFC in the right inferior temporal gyrus, left superior temporal gyrus (STG)/precentral gyrus/postcentral gyrus, right posterior cingulate cortex /precuneus and bilateral cuneus. After the Benjamini-Hochberg correction, significantly negative correlations between GFC in the bilateral cuneus and Trail-Making Test: Part A (TMT-A) scores (r=-0.285, p=0.049), between GFC in the left STG/precentral gyrus/postcentral gyrus and TMT-A scores (r=-0.384, p=0.007), and between GFC in the right MFG and the fluency scores (r=-0.335, p=0.020) in the patients. GFC in the left STG/precentral gyrus/postcentral gyrus has a satisfactory accuracy (up to 86.08%) in classifying patients from controls. AOS shows abnormal GFC in the brain areas of multiple networks, which bears cognitive significance. These findings suggest potential abnormalities in processing self-monitoring and sensory prediction, which further elucidate the pathophysiology of AOS.

A Complex Combination Therapy for a Complex Disease-Neuroimaging Evidence for the Effect of Music Therapy in Schizophrenia

Schizophrenia is a disease characterized by clinical polymorphism: a combination of diverse syndromes defined by differences in structure, course and outcome. The etiology and pathogenesis of this mental disorder is still not completely understood, in spite of the achievements in the fields of neuroscience, genetics, neuroimaging and others. Different treatment strategies have been developed for patients with schizophrenia, but the search for new pharmacological agents continues with the mission of achieving a more effective control over the disease manifestations (positive and negative symptoms), improvement of the patients' social functioning and quality of life. The accumulated clinical experience has revealed that drug treatment and the inclusion in various rehabilitation programs and social skills training shows promising results in these patients. In recent years a plethora of evidence has been compiled regarding the role of music therapy as a possible alternative in the combination treatment of patients with mental disorders, schizophrenia included. Thus, the purpose of this review is to present the reader with a more detailed and science-based account of the beneficial effect of music therapy on the general wellbeing of patients diagnosed with schizophrenia. To fulfill our goal, we will focus mainly on the evidence provided by modern neuroimaging research.

White matter impairments in patients with schizophrenia: A multisite diffusion MRI study

There is a lack of convincing and replicative findings regarding white matter abnormalities in schizophrenia. Several multisite diffusion magnetic resonance imaging (dMRI) studies have been conducted to increase statistical power and reveal subtle white matter changes. Data pooling methods are crucial in joint analysis to compensate for the use of different scanners and image acquisition parameters. A harmonization method using raw dMRI data was developed to overcome the limited generalizability of previous data pooling methods. We obtained dMRI data of 242 healthy controls and 190 patients with schizophrenia from four different study sites. After applying the harmonization method to the raw dMRI data, a two-tensor whole-brain tractography was performed, and diffusion measures were compared between the two groups. The correlation of fractional anisotropy (FA) with the positive and negative symptoms was evaluated, and the interaction effect of diagnosis-by-age, age-squared, and sex was examined. The following white matter tracts showed significant group differences in the FA: the right superior longitudinal fascicle (SLF), the left-to-right lateral orbitofrontal commissural tract, pars orbitalis (pOr-pOr) commissural tract, and pars triangularis (pTr-pTr) commissural tract. The FA of the right SLF and pTr-pTr commissural tract were significantly associated with the Positive and Negative Syndrome Scale (PANSS) positive and negative scores. No significant interaction effect was observed. These findings add to the evidence on structural brain abnormalities in schizophrenia and can aid in obtaining a better understanding of the biological foundations of schizophrenia.

Searching for Imaging Biomarkers of Psychotic Dysconnectivity

BACKGROUND

Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.

METHODS

We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.

RESULTS

Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.

CONCLUSIONS

Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Changes in negative symptoms are linked to white matter changes in superior longitudinal fasciculus in individuals at ultra-high risk for psychosis

AIM

Growing evidence suggests that subtle white matter (WM) alterations are associated with psychopathology in individuals at ultra-high risk for psychosis (UHR). However, the longitudinal relationship between symptom progression and WM changes over time remains under-explored. Here, we examine associations between changes in clinical symptoms and changes in WM over six months in a large UHR-cohort.

METHODS

110 UHR-individuals and 59 healthy controls underwent diffusion weighted imaging at baseline and after six months. Group × time effects on fractional anisotropy (FA) were tested globally and in four predefined regions of interest (ROIs) bilaterally using linear modelling with repeated measures. Correlations between the changes in clinical symptoms and FA changes in the ROIs were examined with Pearson's correlation. A partial least squares correlation-technique (PLS-C) explored multivariate associations between patterns of changes in psychopathology, regional FA and additional WM indices.

RESULTS

At baseline, UHR-individuals displayed significantly lower FA globally (p = 0.018; F = 12.274), in right superior longitudinal fasciculus (p = 0.02; Adj R² = 0.07) and in left uncinate fasciculus (p = 0.048; Adj R² = 0.058) compared to controls (corrected). We identified a group × time interaction in global FA and right superior longitudinal fasciculus, but the finding did not survive multiple comparisons. However, an increase of negative symptoms in UHR-individuals correlated with FA increase in right superior longitudinal fasciculus (p = 0.048, corrected, r = 0.357), and this finding was supported by the multivariate PLS-C.

CONCLUSION

We found a positive correlation with a moderate effect between change in negative symptoms and FA change over 6 months in right superior longitudinal fasciculus. This link appeared mainly to reflect a subgroup of UHR-individuals, which already at baseline presented as vulnerable.

Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis

OBJECTIVE

Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR).

METHODS

110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression.

RESULTS

Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ²  = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R²  = 0.055, F = 6.084, p = 0.016) and functional level (R²  = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months.

CONCLUSION

Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.

The psychosis human connectome project: An overview

Investigations within the Human Connectome Project have expanded to include studies focusing on brain disorders. This paper describes one of the investigations focused on psychotic psychopathology: The psychosis Human Connectome Project (P-HCP). The data collected as part of this project were multimodal and derived from clinical assessments of psychopathology, cognitive assessments, instrument-based motor assessments, blood specimens, and magnetic resonance imaging (MRI) data. The dataset will be made publicly available through the NIMH Data Archive. In this report we provide specific information on how the sample of participants was obtained and characterized and describe the experimental tasks and procedures used to probe neural functions involved in psychotic disorders that may also mark genetic liability for psychotic psychopathology. Our goal in this paper is to outline the data acquisition process so that researchers intending to use these publicly available data can plan their analyses. MRI data described in this paper are limited to data acquired at 3 Tesla. A companion paper describes the study's 7 Tesla image acquisition protocol in detail, which is focused on visual perceptual functions in psychotic psychopathology.

Fronto-thalamic structural and effective connectivity and delusions in schizophrenia: a combined DTI/DCM study

BACKGROUND

Schizophrenia (SZ) is a complex disorder characterized by a range of behavioral and cognitive symptoms as well as structural and functional alterations in multiple cortical and subcortical structures. SZ is associated with reduced functional network connectivity involving core regions such as the anterior cingulate cortex (ACC) and the thalamus. However, little is known whether effective coupling, the directed influence of one structure over the other, is altered during rest in the ACC-thalamus network.

METHODS

We collected resting-state fMRI and diffusion-weighted MRI data from 18 patients and 20 healthy controls. We analyzed fronto-thalamic effective connectivity using dynamic causal modeling for cross-spectral densities in a network consisting of the ACC and the left and right medio-dorsal thalamic regions. We studied structural connectivity using fractional anisotropy (FA).

RESULTS

We found decreased coupling strength from the right thalamus to the ACC and from the right thalamus to the left thalamus, as well as increased inhibitory intrinsic connectivity in the right thalamus in patients relative to controls. ACC-to-left thalamus coupling strength correlated with the Positive and Negative Syndrome Scale (PANSS) total positive syndrome score and with delusion score. Whole-brain structural analysis revealed several tracts with reduced FA in patients, with a maximum decrease in white matter tracts containing fronto-thalamic and cingulo-thalamic fibers.

CONCLUSIONS

We found altered effective and structural connectivity within the ACC-thalamus network in SZ. Our results indicate that ACC-thalamus network activity at rest is characterized by reduced thalamus-to-ACC coupling. We suggest that positive symptoms may arise as a consequence of compensatory measures to imbalanced fronto-thalamic coupling.

Motor abnormalities and basal ganglia in first-episode psychosis (FEP)

BACKGROUND

Motor abnormalities (MAs) are the primary manifestations of schizophrenia. However, the extent to which MAs are related to alterations of subcortical structures remains understudied.

METHODS

We aimed to investigate the associations of MAs and basal ganglia abnormalities in first-episode psychosis (FEP) and healthy controls. Magnetic resonance imaging was performed on 48 right-handed FEP and 23 age-, gender-, handedness-, and educational attainment-matched controls, to obtain basal ganglia shape analysis, diffusion tensor imaging techniques (fractional anisotropy and mean diffusivity), and relaxometry (R2*) to estimate iron load. A comprehensive motor battery was applied including the assessment of parkinsonism, catatonic signs, and neurological soft signs (NSS). A fully automated model-based segmentation algorithm on 1.5T MRI anatomical images and accurate corregistration of diffusion and T2* volumes and R2* was used.

RESULTS

FEP patients showed significant local atrophic changes in left globus pallidus nucleus regarding controls. Hypertrophic changes in left-side caudate were associated with higher scores in sensory integration, and in right accumbens with tremor subscale. FEP patients showed lower fractional anisotropy measures than controls but no significant differences regarding mean diffusivity and iron load of basal ganglia. However, iron load in left basal ganglia and right accumbens correlated significantly with higher extrapyramidal and motor coordination signs in FEP patients.

CONCLUSIONS

Taken together, iron load in left basal ganglia may have a role in the emergence of extrapyramidal signs and NSS of FEP patients and in consequence in the pathophysiology of psychosis.

Shared and distinct white matter abnormalities in schizophrenia and bipolar disorder

While white matter impairments play an integral part in the pathophysiology of schizophrenia and bipolar disorder, the literature on white matter abnormality differences between the two disorders is insufficient. The University of California Los Angeles Consortium for Neuropsychiatric Phenomic LA5c public dataset, including 47 patients with schizophrenia, 47 with bipolar disorder, and 115 healthy controls, was obtained via OpenNeuro. Whole-brain tractography was performed using Unscented Kalman filter-based two-tensor tractography and White Matter Query Language. Diffusion indices, including fractional anisotropy (FA), axial diffusivity, radial diffusivity (RD), and trace (TR), were used to compare subject groups. Spearman's partial correlation with a covariate of age was used for correlation with clinical variables. Both patient groups exhibited significantly higher RD in the left external capsule and TR in the right extreme capsule. Significantly lower FA in the left external capsule, right thalamo-occipital and thalamo-parietal tracts were found in the schizophrenia group in comparison with bipolar disorder and healthy control groups. Compared with healthy controls, patients with schizophrenia had significantly lower FA in the left-to-right lateral orbitofrontal commissural tract. There were possible associations of the FA and RD of the left external capsule with the anxiety-depression score of the Brief Psychiatric Rating Scale in patients with schizophrenia. The white matter alterations identified in schizophrenia and bipolar disorder may be a neurobiological basis contributing to characterization of the two disorders.

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Anomalous self-experiences are related to general cognition deficits in schizophrenia

Anomalous self-experiences (ASEs) are prevalent in schizophrenia, but its underpinnings are not completely understood. Given the likely complex substrate of the experience of the self, neurocognitive functions requiring coordinate cerebral activity may relate to ASEs. Moreover, cognitive deficits functioning may be involved in the link between self-experience disturbances and some aspects of social dysfunction in schizophrenia. We have assessed ASEs in 41 schizophrenia patients (11 first episodes) using the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE), and the general cognition using the Brief Assessment of Cognition in Schizophrenia (BACS). Besides, social cognition was assessed using two complementary tools Meyer, Salovey and Caruso Emotional Intelligence Test (MSCEIT) and GEOPTE (Grupo Español para la Optimización del Tratamiento de la Esquizofrenia). The results revealed that Self-awareness/presence and Somatization IPASE scores were inversely explained by motor speed in the BACS; Consciousness IPASE scores were inversely explained by problem solving performance in the BACS. These data reveal a significant relationship between certain domains of general cognition and anomalous self-experiences, that may be useful in further investigation on the substrates of ASEs.