Impaired interactions among white‐matter functional networks in antipsychotic‐naive first‐episode schizophrenia

Schizophrenia has been conceptualized as a disorder arising from structurally pathological alterations to white‐matter fibers in the brain. However, few studies have focused on white‐matter functional changes in schizophrenia. Considering that converging evidence suggests that white‐matter resting state functional MRI (rsfMRI) signals can effectively depict neuronal activity and psychopathological status, this study examined white‐matter network‐level interactions in antipsychotic‐naive first‐episode schizophrenia (FES) to facilitate the interpretation of the psychiatric pathological mechanisms in schizophrenia. We recruited 42 FES patients (FESs) and 38 healthy controls (HCs), all of whom underwent rsfMRI. We identified 11 white‐matter functional networks, which could be further classified into deep, middle, and superficial layers of networks. We then examined network‐level interactions among these 11 white‐matter functional networks using coefficient Granger causality analysis. We employed group comparisons on the influences among 11 networks using network‐based statistic. Excitatory influences from the middle superior corona radiate network to the superficial orbitofrontal and deep networks were disrupted in FESs compared with HCs. Additionally, an extra failure of suppression within superficial networks (including the frontoparietal network, temporofrontal network, and the orbitofrontal network) was observed in FESs. We additionally recruited an independent cohort (13 FESs and 13 HCs) from another center to examine the replicability of our findings across centers. Similar replication results further verified the white‐matter functional network interaction model of schizophrenia. The novel findings of impaired interactions among white‐matter functional networks in schizophrenia indicate that the pathophysiology of schizophrenia may also lie in white‐matter functional abnormalities.     

Individualized covariance profile of cortical morphology for auditory hallucinations in first‐episode psychosis

Neocortical phenotype of cortical surface area (CSA) and thickness (CT) are influenced by distinctive genetic factors and undergo differential developmental trajectories, which could be captured using the individualized cortical structural covariance (ISC). Disturbed patterns of neocortical development and maturation underlie the perceptual disturbance of psychosis including auditory hallucination (AH). To demonstrate the utility of selected ISC features as primal biomarker of AH in first‐episode psychosis (FEP) subjects experiencing AH (FEP‐AH), we employed herein a support vector machine (SVM). A total of 147 subjects (FEP‐AH, n = 27; FEP‐NAH, n = 24; HC, n = 96) underwent T₁‐weighted magnetic resonance imaging at 3T. The FreeSurfer software suite was used for cortical parcellation, with the CSA‐ISC and CT‐ISC then calculated. The most informative ISCs showing statistical significance (P < 0.001) across every run of leave‐one‐out group‐comparison were aligned according to the absolute value of averaged t‐statistics and were packaged into candidate feature sets for classification analysis using the SVM. An optimal feature set comprising three CSA‐ISCs, including the intraparietal sulcus, Broca's complex, and the anterior insula, distinguished FEP‐AH from FEP‐NAH subjects with 83.6% accuracy (sensitivity = 82.8%; specificity = 85.7%). Furthermore, six CT‐ISCs encompassing the executive control network and Wernicke's module classified FEP‐AH from FEP‐NAH subjects with 82.3% accuracy (sensitivity = 79.5%; specificity = 88.6%). Finally, extended sets of ISCs related to the default‐mode network distinguished FEP‐AH or FEP‐NAH from HC subjects with 89.0–93.0% accuracy (sensitivity = 88.4–93.4%; specificity = 89.0–94.1%). This study established a distinctive intermediate phenotype of biological proneness for AH in FEP using CSA‐ISCs as well as a state marker of disease progression using CT‐ISCs. Hum Brain Mapp 37:1051–1065, 2016. © 2015 Wiley Periodicals, Inc .     

Source‐based morphometry of gray matter volume in men with first‐episode schizophrenia

Objectives: There is a lot of variability between the results of studies reporting the pattern of gray matter volume changes in schizophrenia. Methodological issues may play an important role in this heterogeneity. The aim of the present study was to replicate the better performance of multivariate “source‐based morphometry” (SBM) over the mass‐univariate approach. Experimental design: Voxel‐based morphometry of Jacobian‐modulated gray matter volume images, using voxel and cluster level inference, and SBM were performed in a group of first‐episode schizophrenia patients (N = 49) and healthy controls (N = 127). Results: Using SBM we were able to find a significant reduction of gray matter volume in fronto‐temporo‐cerebellar areas whereas no significant results were obtained using voxel‐based morphometry. Conclusion: Multivariate analysis of gray matter volume seems to be a suitable method for characterization of the pattern of changes at the beginning of the illness in schizophrenia subjects. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.     

Altered single‐subject gray matter structural networks in drug‐naïve attention deficit hyperactivity disorder children

Altered topological organization of brain structural covariance networks has been observed in attention deficit hyperactivity disorder (ADHD). However, results have been inconsistent, potentially related to confounding medication effects. In addition, since structural networks are traditionally constructed at the group level, variabilities in individual structural features remain to be well characterized. Structural brain imaging with MRI was performed on 84 drug‐naïve children with ADHD and 83 age‐matched healthy controls. Single‐subject gray matter (GM) networks were obtained based on areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared using nonparametric permutation testing. Compared with healthy subjects, GM networks in ADHD patients demonstrated significantly altered topological characteristics, including higher global and local efficiency and clustering coefficient, and shorter path length. In addition, ADHD patients exhibited abnormal centrality in corticostriatal circuitry including the superior frontal gyrus, orbitofrontal gyrus, medial superior frontal gyrus, precentral gyrus, middle temporal gyrus, and pallidum (all p < .05, false discovery rate [FDR] corrected). Altered global and nodal topological efficiencies were associated with the severity of hyperactivity symptoms and the performance on the Stroop and Wisconsin Card Sorting Test tests (all p < .05, FDR corrected). ADHD combined and inattention subtypes were differentiated by nodal attributes of amygdala (p < .05, FDR corrected). Alterations in GM network topologies were observed in drug‐naïve ADHD patients, in particular in frontostriatal loops and amygdala. These alterations may contribute to impaired cognitive functioning and impulsive behavior in ADHD.     

Cortisol and ACTH levels in drug‐naive adolescents with first‐episode early onset schizophrenia

The aim of this study was to investigate serum levels of cortisol and adrenocorticotropic hormone in adolescents with first‐episode early onset schizophrenia. A total of 23 adolescent patients, who did not receive prior therapy and who were diagnosed with psychosis according to DSM‐IV, were included. Kiddie‐Schedule for Affective Disorders and Schizophrenia‐Present and Lifetime Version, Positive and Negative Symptom Scale, and Clinical Global Impression Scale were conducted with the participants. No significant differences were found between the patients and the control subjects in serum cortisol and adrenocorticotropic hormone levels (P > .05). Our study's findings do not support the hypothesis of increased hypothalamic‐pituitary‐adrenal axis activity in first‐episode early onset schizophrenia.     

Morbidity in 303 first‐episode bipolar I disorder patients

Baldessarini RJ, Salvatore P, Khalsa H‐MK, Gebre‐Medhin P, Imaz H, González‐Pinto A, Perez J, Cruz N, Maggini C, Tohen M. Morbidity in 303 first‐episode bipolar I disorder patients.
Bipolar Disord 2010: 12: 264–270. © 2010 The Authors.
Journal compilation © 2010 John Wiley & Sons A/S. Objectives: To test the hypotheses that: (i) depressive‐dysthymic‐dysphoric (D‐type) morbidity is more prevalent than manic‐hypomanic‐psychotic (M‐type) morbidity even from first episodes of bipolar I disorder (BPD‐I) and despite treatment; (ii) initial presentations predict later morbidity; (iii) morbidity varies internationally; and (iv) early and later morbidity are similar. Methods: We followed SCID‐based, DSM‐IV BPD‐I patients (n = 303) systematically and prospectively for two years to estimate the percent of weeks in specific morbid states from first lifetime major episodes. Results: Total morbidity accounted for 44% of the first two years, and D‐type exceeded M‐type illnesses by 2.1‐fold (30%/14%) among morbidities ranking: mixed states (major + minor) ≥ dysthymia ≥ mania ≥ major depression > hypomania > psychosis. In 164 cases, morbidities at 0.5–2.5 and 2.5–4.5 years were very similar. Depressive or mixed initial episodes predicted a 3.6‐fold excess of D‐type morbidity, and initial M‐type episodes predicted a 7.1‐fold excess of M‐type morbidity over two years. Morbidity in European (EU) sites was nearly half that in the U.S., and 22% greater overall among men than women. In five comparable studies, illness accounted for 54% of follow‐up time, and the ratio of D/M morbidity averaged 3.0. Conclusions: In accord with four midcourse studies, morbidity from BPD‐I onset, despite treatment by community standards, averaged 44%, was 68% D‐type morbidity, and was strongly predicted by first‐episode polarity. Lower morbidity in EU than U.S. sites may reflect differences in healthcare or social systems.     

The effects of antidepressant treatment on resting‐state functional brain networks in patients with major depressive disorder

Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first‐episode drug‐naive major depressive disorder (MDD) patients and performed resting‐state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor—escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8‐week interval. Whole‐brain connectivity was analyzed using a graph‐theory approach—functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment‐related changes. We observed significant group‐by‐time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal‐limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging‐based biomarkers for treatment evaluation in MDD. Hum Brain Mapp 36:768–778, 2015. © 2014 Wiley Periodicals, Inc.     

Spatio‐temporal dynamics of resting‐state brain networks improve single‐subject prediction of schizophrenia diagnosis

Correlation in functional MRI activity between spatially separated brain regions can fluctuate dynamically when an individual is at rest. These dynamics are typically characterized temporally by measuring fluctuations in functional connectivity between brain regions that remain fixed in space over time. Here, dynamics in functional connectivity were characterized in both time and space. Temporal dynamics were mapped with sliding‐window correlation, while spatial dynamics were characterized by enabling network regions to vary in size (shrink/grow) over time according to the functional connectivity profile of their constituent voxels. These temporal and spatial dynamics were evaluated as biomarkers to distinguish schizophrenia patients from controls, and compared to current biomarkers based on static measures of resting‐state functional connectivity. Support vector machine classifiers were trained using: (a) static, (b) dynamic in time, (c) dynamic in space, and (d) dynamic in time and space characterizations of functional connectivity within canonical resting‐state brain networks. Classifiers trained on functional connectivity dynamics mapped over both space and time predicted diagnostic status with accuracy exceeding 91%, whereas utilizing only spatial or temporal dynamics alone yielded lower classification accuracies. Static measures of functional connectivity yielded the lowest accuracy (79.5%). Compared to healthy comparison individuals, schizophrenia patients generally exhibited functional connectivity that was reduced in strength and more variable. Robustness was established with replication in an independent dataset. The utility of biomarkers based on temporal and spatial functional connectivity dynamics suggests that resting‐state dynamics are not trivially attributable to sampling variability and head motion.     

Assessment of cortical degeneration in patients with Parkinson's disease by voxel-based morphometry, cortical folding, and cortical thickness

Noninvasive brain imaging methods provide useful information on cerebral involution and degenerative processes. Here we assessed cortical degeneration in 20 nondemented patients with Parkinson's disease (PD) and 20 healthy controls using three quantitative neuroanatomical approaches: voxel‐based morphometry (VBM), cortical folding (BrainVisa), and cortical thickness (FreeSurfer). We examined the relationship between global and regional gray matter (GM) volumes, sulcal indices, and thickness measures derived from the previous methods as well as their association with cognitive performance, age, severity of motor symptoms, and disease stage. VBM analyses showed GM volume reductions in the left temporal gyrus in patients compared with controls. Cortical folding measures revealed significant decreases in the left frontal and right collateral sulci in patients. Finally, analysis of cortical thickness showed widespread cortical thinning in right lateral occipital, parietal and left temporal, frontal, and premotor regions. We found that, in patients, all global anatomical measures correlated with age, while GM volume and cortical thickness significantly correlated with disease stage. In controls, a significant association was found between global GM volume and cortical folding with age. Overall these results suggest that the three different methods provide complementary and related information on neurodegenerative changes occurring in PD, however, surface‐based measures of cortical folding and especially cortical thickness seem to be more sensitive than VBM to identify regional GM changes associated to PD. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.     

Eight‐week antidepressant treatment reduces functional connectivity in first‐episode drug‐naïve patients with major depressive disorder

Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big‐data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first‐episode drug‐naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: {"type":"clinical-trial","attrs":{"text":"NCT03294525","term_id":"NCT03294525"}}NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty‐one first‐episode drug‐naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting‐state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big‐data finding, we also conducted a cross‐sectional comparison of resting‐state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network‐Based Statistic analyses and large‐scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network‐Based Statistic analyses nor large‐scale network analyses detected significant functional connectivity differences between treatment‐naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.     

Altered cortical thickness and structural covariance networks in upper limb amputees: A graph theoretical analysis

Background

The extensive functional and structural remodeling that occurs in the brain after amputation often results in phantom limb pain (PLP). These closely related phenomena are still not fully understood.

Methods

Using magnetic resonance imaging (MRI) and graph theoretical analysis (GTA), we explored how alterations in brain cortical thickness (CTh) and structural covariance networks (SCNs) in upper limb amputees (ULAs) relate to PLP. In all, 45 ULAs and 45 healthy controls (HCs) underwent structural MRI. Regional network properties, including nodal degree, betweenness centrality (BC), and node efficiency, were analyzed with GTA. Similarly, global network properties, including global efficiency (Eglob), local efficiency (Eloc), clustering coefficient (Cp), characteristic path length (Lp), and the small-worldness index, were evaluated.

Results

Compared with HCs, ULAs had reduced CThs in the postcentral and precentral gyri contralateral to the amputated limb; this decrease in CTh was negatively correlated with PLP intensity in ULAs. ULAs showed varying degrees of change in node efficiency in regional network properties compared to HCs (p < 0.005). There were no group differences in Eglob, Eloc, Cp, and Lp properties (all p > 0.05). The real-worldness SCN of ULAs showed a small-world topology ranging from 2% to 34%, and the area under the curve of the small-worldness index in ULAs was significantly different compared to HCs (p < 0.001).

Conclusion

These results suggest that the topological organization of human CNS functional networks is altered after amputation of the upper limb, providing further support for the cortical remapping theory of PLP.     

Preprocessing strategy influences graph‐based exploration of altered functional networks in major depression

Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422‐1442, 2016. © 2016 Wiley Periodicals, Inc.     

Functional dysconnectivity of the limbic loop of frontostriatal circuits in first‐episode,treatment‐naive schizophrenia

Frontostriatal circuits dysfunction has been implicated in the etiology and psychopathology of patients with schizophrenia (SZ). However, few studies have investigated SZ‐related functional connectivity (FC) alterations in discrete frontostriatal circuits and their relationship with pathopsychology in first‐episode schizophrenia (FESZ). The goal of this study was to identify dysfunctions in discrete frontostriatal circuits that are associated with key features of FESZ. To this end, a case–control, cross‐sectional study was conducted, wherein resting‐state (RS) functional magnetic resonance (fMRI) data were collected from 37 treatment‐naïve FESZ patients and 29 healthy control (HC) subjects. Seed‐based FC analyses were performed by placing six bilateral pairs of seeds within a priori defined subdivisions of the striatum. We observed significantly decreased FC for the FESZ group relative to the HC group [p < .05, family‐wise error (FWE)‐corrected] in the limbic loop, but not in the sensorimotor or associative loops, of frontostriatal circuitry. Moreover, bilaterally decreased inferior ventral striatum/nucleus accumbens (VSi)‐dorsal anterior cingulate cortex (dACC) FC within the limbic loop correlated inversely with overall FESZ symptom severity and the disorganization factor score of PANSS. These findings provide new insight into the role of frontostriatal limbic loop hypoconnectivity in early‐stage schizophrenia pathology and suggest potential novel therapeutic targets.