Investigating structural subdivisions of the anterior cingulate cortex in schizophrenia, with implications for treatment resistance and glutamatergic levels

Elevated intrinsic cortical curvature in treatment-resistant schizophrenia: Evidence of structural deformation in functional connectivity areas and comparison with alternate indices of structure

BACKGROUND

Research suggests structural and connectivity abnormalities in patients with treatment-resistant schizophrenia (TRS) compared to first-line responders and healthy-controls. However, measures of these abnormalities are often influenced by external factors like nicotine and antipsychotics, limiting their clinical utility. Intrinsic-cortical-curvature (ICC) presents a millimetre-scale measure of brain gyrification, highly sensitive to schizophrenia differences, and associated with TRS-like traits in early stages of the disorder. Despite this evidence, ICC in TRS remains unexplored. This study investigates ICC as a marker for treatment resistance in TRS, alongside structural indices for comparison.

METHODS

We assessed ICC in anterior cingulate, dorsolateral prefrontal, temporal, and parietal cortices of 38 first-line responders, 30 clozapine-resistant TRS, 37 clozapine-responsive TRS, and 52 healthy-controls. For comparative purposes, Fold and Curvature indices were also analyzed.

RESULTS

Adjusting for age, sex, nicotine-use, and chlorpromazine equivalence, principal findings indicate ICC elevations in the left hemisphere dorsolateral prefrontal (p < 0.001, η2partial = 0.142) and temporal cortices (LH p = 0.007, η2partial = 0.060; RH p = 0.011, η2partial = 0.076) of both TRS groups, and left anterior cingulate cortex of clozapine-resistant TRS (p = 0.026, η2partial = 0.065), compared to healthy-controls. Elevations that correlated with reduced cognition (p = 0.001) and negative symptomology (p < 0.034) in clozapine-resistant TRS. Fold and Curvature indices only detected group differences in the right parietal cortex, showing interactions with age, sex, and nicotine use. ICC showed interactions with age.

CONCLUSION

ICC elevations were found among patients with TRS, and correlated with symptom severity. ICCs relative independence from sex, nicotine-use, and antipsychotics, may support ICC's potential as a viable marker for TRS, though age interactions should be considered.

Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

Disease Progression Patterns of Brain Morphology in Schizophrenia: More Progressed Stages in Treatment Resistance

BACKGROUND AND HYPOTHESIS

Given the heterogeneity and possible disease progression in schizophrenia, identifying the neurobiological subtypes and progression patterns in each patient may lead to novel biomarkers. Here, we adopted data-driven machine-learning techniques to identify the progression patterns of brain morphological changes in schizophrenia and investigate the association with treatment resistance.

STUDY DESIGN

In this cross-sectional multicenter study, we included 177 patients with schizophrenia, characterized by treatment response or resistance, with 3D T1-weighted magnetic resonance imaging. Cortical thickness and subcortical volumes calculated by FreeSurfer were converted into z scores using 73 healthy controls data. The Subtype and Stage Inference (SuStaIn) algorithm was used for unsupervised machine-learning analysis.

STUDY RESULTS

SuStaIn identified 3 different subtypes: (1) subcortical volume reduction (SC) type (73 patients), in which volume reduction of subcortical structures occurs first and moderate cortical thinning follows, (2) globus pallidus hypertrophy and cortical thinning (GP-CX) type (42 patients), in which globus pallidus hypertrophy initially occurs followed by progressive cortical thinning, and (3) cortical thinning (pure CX) type (39 patients), in which thinning of the insular and lateral temporal lobe cortices primarily happens. The remaining 23 patients were assigned to baseline stage of progression (no change). SuStaIn also found 84 stages of progression, and treatment-resistant schizophrenia showed significantly more progressed stages than treatment-responsive cases (P = .001). The GP-CX type presented earlier stages than the pure CX type (P = .009).

CONCLUSIONS

The brain morphological progressions in schizophrenia can be classified into 3 subtypes, and treatment resistance was associated with more progressed stages, which may suggest a novel biomarker.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

A systematic review of neuroimaging studies of clozapine-resistant schizophrenia

This systematic review aimed to review neuroimaging studies comparing clozapine-resistant schizophrenia patients with clozapine-responding patients, and with first-line antipsychotic responding (FLR) patients. A total of 19 studies including 6 longitudinal studies were identified. Imaging techniques comprised computerized tomography (CT, n = 3), structural magnetic resonance imaging (MRI, n = 7), magnetic resonance spectroscopy (MRS, n = 5), functional MRI (n = 1), single-photon emission computerized tomography (SPECT, n = 3) and diffusion tensor imaging (DTI, n = 1). The most consistent finding was hypo-frontality in the clozapine-resistant group compared with the clozapine-responding group with possible differences in frontal-striatal-basal ganglia circuitry as well as the GABA level between the two treatment-resistant groups. Additional statistically significant findings were reported when comparing clozapine-resistant patients with the FLR group, including lower cortical thickness and brain volume of multiple brain regions as well as lower Glx/Cr level in the dorsolateral prefrontal cortex. Both treatment-resistant groups were found to have extensive differences in neurobiological features in comparison with the FLR group. Overall results suggested treatment-resistant schizophrenia is likely to be a neurobiological distinct type of the illness. Clozapine-resistant and clozapine-responding schizophrenia are likely to have both shared and distinct neurobiological features. However, conclusions from existing studies are limited, and future multi-center collaborative studies are required with a consensus clinical definition of patient samples, multimodal imaging tools, and longitudinal study designs.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Commonalities and differences in ECT-induced gray matter volume change between depression and schizophrenia

BACKGROUND

Electroconvulsive therapy (ECT) is one of the most effective treatments for depression and schizophrenia, particularly in urgent or treatment-resistant cases. After ECT, regional gray matter volume (GMV) increases have been repeatedly reported both in depression and schizophrenia. However, the interpretation of these findings remains entangled because GMV changes do not necessarily correlate with treatment effects and may be influenced by the intervention itself. We hypothesized that the comparison of longitudinal magnetic resonance imaging data between the two diagnostic groups will provide clues to distinguish diagnosis-specific and transdiagnostic changes.

METHOD

Twenty-nine Japanese participants, including 18 inpatients with major depressive disorder and 11 with schizophrenia, underwent longitudinal voxel-based morphometry before and after ECT. We investigated GMV changes common to both diagnostic groups and those specific to each group. Moreover, we also evaluated potential associations between GMV changes and clinical improvement for each group.

RESULTS

In both diagnostic groups, GMV increased in widespread areas after ECT, sharing common regions including: anterior temporal cortex; medial frontal and anterior cingulate cortex; insula; and caudate nucleus. In addition, we found a schizophrenia-specific GMV increase in a region including the left pregenual anterior cingulate cortex, with volume increase significantly correlating with clinical improvement.

CONCLUSIONS

Transdiagnostic volume changes may represent the effects of the intervention itself and pathophysiological changes common to both groups. Conversely, diagnosis-specific volume changes are associated with treatment effects and may represent pathophysiology-specific impacts of ECT.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

Pharmacological and nonpharmacological augmentation treatments for clozapine-resistant schizophrenia: A systematic review and network meta-analysis with normalized entropy assessment

OBJECTIVE

To integrate all evidence derived from randomized controlled trials (RCTs) of both pharmacological and nonpharmacological augmentation interventions for clozapine-resistant schizophrenia (CRS).

METHODS

Six major electronic databases were systematically searched for RCTs published until July 10, 2021. The primary outcome was change in overall symptoms, and the secondary outcomes were positive and negative symptoms and acceptability. We performed random-effects network meta-analysis. Normalized entropy was calculated to examine the uncertainty of treatment ranking.

RESULTS

We identified 35 RCTs (1472 patients with 23 active augmentation treatments) with a mean daily clozapine dose of 440.80 (91.27) mg for 1168.22 (710.28) days. Network meta-analysis of overall symptoms (reported as standardized mean difference; 95 % confidence interval) with consistent results indicated that mirtazapine (-4.41; -5.61, -3.21), electroconvulsive therapy (ECT) (-4.32; -5.43, -3.21), and memantine (-2.02; -3.14, -0.91) were ranked as the best three treatments. For positive symptoms, ECT (-5.18; -5.86, -4.49) was ranked the best with less uncertainty. For negative symptoms, memantine (-3.38; -4.50, -2.26), duloxetine (-3.27; -4.25, -2.29), and mirtazapine (-1.73; -2.71, -0.74) were ranked the best three treatments with less uncertainty. All antipsychotics, N-methyl d-aspartate receptor agonists, and antiepileptics were not associated with more efficacy than placebo. Compared to placebo, only amisulpride had statistically significant lower discontinuation rate (risk ratio: 0.21; 95 % CI: 0.05, 0.93).

CONCLUSION

Add-on mirtazapine, ECT, and memantine were the most efficacious augmentation options for CRS. Data on other important outcomes such as cognitive functioning or quality of life were rarely reported, making further large-scale, well-designed RCTs necessary. (PROSPERO number, CRD42021262197.).

Rational and Translational Implications of D-Amino Acids for Treatment-Resistant Schizophrenia: From Neurobiology to the Clinics

Schizophrenia has been conceptualized as a neurodevelopmental disorder with synaptic alterations and aberrant cortical–subcortical connections. Antipsychotics are the mainstay of schizophrenia treatment and nearly all share the common feature of dopamine D2 receptor occupancy, whereas glutamatergic abnormalities are not targeted by the presently available therapies. D-amino acids, acting as N-methyl-D-aspartate receptor (NMDAR) modulators, have emerged in the last few years as a potential augmentation strategy in those cases of schizophrenia that do not respond well to antipsychotics, a condition defined as treatment-resistant schizophrenia (TRS), affecting almost 30–40% of patients, and characterized by serious cognitive deficits and functional impairment. In the present systematic review, we address with a direct and reverse translational perspective the efficacy of D-amino acids, including D-serine, D-aspartate, and D-alanine, in poor responders. The impact of these molecules on the synaptic architecture is also considered in the light of dendritic spine changes reported in schizophrenia and antipsychotics’ effect on postsynaptic density proteins. Moreover, we describe compounds targeting D-amino acid oxidase and D-aspartate oxidase enzymes. Finally, other drugs acting at NMDAR and proxy of D-amino acids function, such as D-cycloserine, sarcosine, and glycine, are considered in the light of the clinical burden of TRS, together with other emerging molecules.