White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1-5 over 1 year) in 286 subjects (aged 12-32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p < 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FAT or FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis-regardless of transition outcome-is characterized by subtle age-related white matter changes that occur in tandem with development.

Deep learning based segmentation of brain tissue from diffusion MRI.

Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in dMRI as compared with anatomical MRI. In this study, we present a deep learning method for diffusion MRI segmentation, which we refer to as DDSeg. Our proposed method learns tissue segmentation from high-quality imaging data from the Human Connectome Project (HCP), where registration of anatomical MRI to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with different acquisition protocols, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from other acquisitions with lower resolution and fewer gradient directions.

MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis.

Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.

In vivo gamma-aminobutyric acid-A/benzodiazepine receptor availability and genetic liability in asymptomatic individuals with high genetic loading of schizophrenia: A [11C]flumazenil positron emission tomography study.

OBJECTIVES: Whilst reduced signalling and gene expression related to gamma-aminobutyric acid (GABA) play a role in the presumed pathophysiology of schizophrenia, its origin is unclear. Studying asymptomatic individuals with high genetic liability to schizophrenia (AIs) would provide insights. Therefore, this study aimed to investigate the role of genetic liability in GABAergic dysfunction of schizophrenia by exploring in vivo GABA-A/benzodiazepine receptor (GABAR) availability in AIs. METHODS: A total of 10 AIs with multiple relatives diagnosed as schizophrenia and 11 healthy controls underwent [11C]flumazenil positron emission tomography and neurocognitive function tests. RESULTS: There was no significant difference in [11C]flumazenil availability based on the groups. GABAR availability in caudate nuclei had positive correlations with genetic liability of AIs. GABAR availability in caudate nuclei and verbal memory measures of AIs revealed positive correlations. Only the correlation between right caudate and short-term verbal memory survived multiple-comparison correction (p = 0.030). CONCLUSIONS: This study, for the first time, reports correlations between the genetic liability of schizophrenia and GABAR availability. Correlations between [11C]flumazenil binding in caudate of individuals with high genetic liability to schizophrenia suggests that the GABAergic dysfunction may arise from shared genetic factors and also that it may be responsible for cognitive impairment of AIs.

Reduced cortical thickness in subjects at clinical high risk for psychosis and clinical attributes.

OBJECTIVES: Although neuroanatomical abnormalities in subjects at clinical high risk for psychosis have been considered a putative biomarker of psychosis, relevance of cortical thickness alterations remains contested due to discrepant findings. Inconsistencies persist in Asian clinical high risk studies, despite their advantageous settings well-controlled for confounds. Attributes of cortical thickness alterations in clinical high risk subjects warrant further examination. METHODS: We examined cortical thickness at the whole-brain level in 74 clinical high risk subjects and 34 demographically matched healthy controls recruited from Seoul Youth Clinic, South Korea. Clinical symptoms were assessed using the Scale of Prodromal Symptoms, and their associations with cortical thickness were explored using partial correlation analysis. RESULTS: Compared to healthy control, clinical high risk exhibited significant cortical thinning in bilateral prefrontal cortex and inferior parietal lobule clusters. Reduced thickness in the left prefrontal cortex cluster was associated with more severe Scale of Prodromal Symptoms general symptoms scores and the right inferior parietal lobule cluster with Scale of Prodromal Symptoms disorganization symptoms. CONCLUSIONS: Thickness deficits found in the present clinical high risk sample demonstrated a degree of consistency with those reported in the previous Seoul Youth Clinic study. While inconsistencies reported between the present and previous Seoul Youth Clinic samples may reflect markedly decreased rate of converters, consistencies may be relevant to clinical attributes beyond transition, such as the prevalence of comorbidities. Particular recruitment strategies employed for sample selections should also be considered for findings in Asian clinical high risk samples. Our results suggest potential utility of cortical thickness alterations in clinical high risk subjects beyond the frame of transition.

Higher serotonin transporter availability in early-onset obsessive-compulsive disorder patients undergoing escitalopram treatment: A [11 C]DASB PET study.

OBJECTIVE: Early-onset obsessive-compulsive disorder (EOCD) and late-onset obsessive-compulsive disorder (LOCD) are distinct subtypes of obsessive-compulsive disorder (OCD). OCD patients are treated with serotonin reuptake inhibitors, but the difference in serotonin transporter (SERT) availability between medicated EOCD and LOCD is unexplored yet. METHODS: Six EOCD and 6 LOCD patients were enrolled. They underwent serial [11 C]DASB positron emission tomography scans during maintenance therapy with escitalopram, and their plasma concentration of escitalopram was measured simultaneously with the scan. Then, the drug-free binding potential of SERT was calculated by pharmacokinetic-pharmacodynamic modelling. RESULTS: In comparison with LOCD patients, SERT availability was significantly higher in the putamen of EOCD patients (U = 4, p = .026), but not in the caudate nucleus (U = 14, p = .589), thalamus (U = 16, p = .818), and dorsal raphe nucleus (U = 7, p = .093). Binding potential of putamen showed a negative correlation (r = -.580, p = .048) with age of onset of the disease, but not with the Yale-Brown Obsessive Compulsive Scale scores. CONCLUSIONS: These findings indicate that the earlier the age of onset of OCD, the less serotonergic pathology there is and that this difference remains even after long-term serotonin reuptake inhibitor treatment. Clinically, it might suggest that nonserotonergic treatments would be a better option for EOCD patients.

DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.

Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approaches compute the parcellation from anatomical MRI (T1- or T2-weighted) data, using tools such as FreeSurfer or CAT12, and then register it to the diffusion space. However, the registration is challenging due to image distortions and low resolution of dMRI data, often resulting in mislabeling in the derived brain parcellation. Furthermore, these approaches are not applicable when anatomical MRI data is unavailable. As an alternative we developed the Deep Diffusion Parcellation (DDParcel), a deep learning method for fast and accurate parcellation of brain anatomical regions directly from dMRI data. The input to DDParcel are dMRI parameter maps and the output are labels for 101 anatomical regions corresponding to the FreeSurfer Desikan-Killiany (DK) parcellation. A multi-level fusion network leverages complementary information in the different input maps, at three network levels: input, intermediate layer, and output. DDParcel learns the registration of diffusion features to anatomical MRI from the high-quality Human Connectome Project data. Then, to predict brain parcellation for a new subject, the DDParcel network no longer requires anatomical MRI data but only the dMRI data. Comparing DDParcel's parcellation with T1w-based parcellation shows higher test-retest reproducibility and a higher regional homogeneity, while requiring much less computational time. Generalizability is demonstrated on a range of populations and dMRI acquisition protocols. Utility of DDParcel's parcellation is demonstrated on tractography analysis for fiber tract identification.

Opposing white matter microstructure abnormalities in 22q11.2 deletion and duplication carriers.

Deletions and duplications at the 22q11.2 locus are associated with significant neurodevelopmental and psychiatric morbidity. Previous diffusion-weighted magnetic resonance imaging (MRI) studies in 22q11.2 deletion carriers (22q-del) found nonspecific white matter (WM) abnormalities, characterized by higher fractional anisotropy. Here, utilizing novel imaging and processing methods that allow separation of signal contribution from different tissue properties, we investigate whether higher anisotropy is driven by (1) extracellular changes, (2) selective degeneration of secondary fibers, or (3) volumetric differences. We further, for the first time, investigate WM microstructure in 22q11.2 duplication carriers (22q-dup). Multi-shell diffusion-weighted images were acquired from 26 22q-del, 19 22q-dup, and 18 healthy individuals (HC). Images were fitted with the free-water model to estimate anisotropy following extracellular free-water elimination and with the novel BedpostX model to estimate fractional volumes of primary and secondary fiber populations. Outcome measures were compared between groups, with and without correction for WM and cerebrospinal fluid (CSF) volumes. In 22q-del, anisotropy following free-water elimination remained significantly higher compared with controls. BedpostX did not identify selective secondary fiber degeneration. Higher anisotropy diminished when correcting for the higher CSF and lower WM volumes. In contrast, 22q-dup had lower anisotropy and greater extracellular space than HC, not influenced by macrostructural volumes. Our findings demonstrate opposing effects of reciprocal 22q11.2 copy-number variation on WM, which may arise from distinct pathologies. In 22q-del, microstructural abnormalities may be secondary to enlarged CSF space and more densely packed WM. In 22q-dup, we see evidence for demyelination similar to what is commonly observed in neuropsychiatric disorders.

Mapping thalamocortical functional connectivity with large-scale brain networks in patients with first-episode psychosis.

Abnormal thalamocortical networks involving specific thalamic nuclei have been implicated in schizophrenia pathophysiology. While comparable topography of anatomical and functional connectivity abnormalities has been reported in patients across illness stages, previous functional studies have been confined to anatomical pathways of thalamocortical networks. To address this issue, we incorporated large-scale brain network dynamics into examining thalamocortical functional connectivity. Forty patients with first-episode psychosis and forty healthy controls underwent T1-weighted and resting-state functional magnetic resonance imaging. Independent component analysis of voxelwise thalamic functional connectivity maps parcellated the cortex into thalamus-related networks, and thalamic subdivisions associated with these networks were delineated. Functional connectivity of (1) networks with the thalamus and (2) thalamic subdivision seeds were examined. In patients, functional connectivity of the salience network with the thalamus was decreased and localized to the ventrolateral (VL) and ventroposterior (VP) thalamus, while that of a network comprising the cerebellum, temporal and parietal regions was increased and localized to the mediodorsal (MD) thalamus. In patients, thalamic subdivision encompassing the VL and VP thalamus demonstrated hypoconnectivity and that encompassing the MD and pulvinar regions demonstrated hyperconnectivity. Our results extend the implications of disrupted thalamocortical networks involving specific thalamic nuclei to dysfunctional large-scale brain network dynamics in schizophrenia pathophysiology.

White matter microstructure and its relation to clinical features of obsessive-compulsive disorder: findings from the ENIGMA OCD Working Group.

Microstructural alterations in cortico-subcortical connections are thought to be present in obsessive-compulsive disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. Here we investigated microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. We analyzed diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA OCD Working Group, in a cross-sectional case-control magnetic resonance study. We extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohen's d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. Adult OCD patients showed significant FA reduction in the sagittal stratum (d = -0.21, z = -3.21, p = 0.001) and posterior thalamic radiation (d = -0.26, z = -4.57, p < 0.0001). In the sagittal stratum, lower FA was associated with a younger age of onset (z = 2.71, p = 0.006), longer duration of illness (z = -2.086, p = 0.036), and a higher percentage of medicated patients in the cohorts studied (z = -1.98, p = 0.047). No significant association with symptom severity was found. Pediatric OCD patients did not show any detectable microstructural abnormalities compared to controls. Our findings of microstructural alterations in projection and association fibers to posterior brain regions in OCD are consistent with models emphasizing deficits in connectivity as an important feature of this disorder.

Glucagon-Like Peptide-1 Receptor Agonist Differentially Affects Brain Activation in Response to Visual Food Cues in Lean and Obese Individuals with Type 2 Diabetes Mellitus.

BACKGROUND: To investigate the effects of a glucagon-like peptide-1 receptor agonist on functional brain activation in lean and obese individuals with type 2 diabetes mellitus (T2DM) in response to visual food cues. METHODS: In a randomized, single-blinded, crossover study, 15 lean and 14 obese individuals with T2DM were administered lixisenatide or normal saline subcutaneously with a 1-week washout period. We evaluated brain activation in response to pictures of high-calorie food, low-calorie food, and nonfood using functional magnetic resonance imaging and measured appetite and caloric intake in participants who were given access to an ad libitum buffet. RESULTS: Obese individuals with T2DM showed significantly greater activation of the hypothalamus, pineal gland, parietal cortex (high-calorie food vs. low-calorie food, P<0.05), orbitofrontal cortex (high-calorie food vs. nonfood, P<0.05), and visual cortex (food vs. nonfood, P<0.05) than lean individuals with T2DM. Lixisenatide injection significantly reduced the functional activation of the fusiform gyrus and lateral ventricle in obese individuals with T2DM compared with that in lean individuals with T2DM (nonfood vs. high-calorie food, P<0.05). In addition, in individuals who decreased their caloric intake after lixisenatide injection, there were significant interaction effects between group and treatment in the posterior cingulate, medial frontal cortex (high-calorie food vs. low-calorie food, P<0.05), hypothalamus, orbitofrontal cortex, and temporal lobe (food vs. nonfood, P<0.05). CONCLUSION: Brain responses to visual food cues were different in lean and obese individuals with T2DM. In addition, acute administration of lixisenatide differentially affected functional brain activation in these individuals, especially in those who decreased their caloric intake after lixisenatide injection.

Enhanced Attentional Network by Short-Term Intensive Meditation.

While recent studies have suggested behavioral effects of short-term meditation on the executive attentional functions, functional changes in the neural correlates of attentional networks after short-term meditation have been unspecified. Here, we conducted a randomized control trial to investigate the effects of a 4-day intensive meditation on the neural correlates of three attentional functions: alerting, orienting, and executive attention. Twenty-three participants in meditation practice and 14 participants in a relaxation retreat group performed attention network test (ANT) during functional magnetic resonance imaging both before and immediately after intervention. The meditation group showed significantly improved behavioral performance in the executive control network in ANT after the intervention. Moreover, neural activities in the executive control network, namely, the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (DLPFC), were also significantly increased during the ANT after meditation. Interestingly, neural activity in the right ACC was significantly predicted by behavioral conflict levels in each individual in the meditation group, indicating significant effects of the program on the executive control network. Moreover, brain regions associated with the alerting and orienting networks also showed enhanced activity during the ANT after the meditation. Our study provides novel evidence on the enhancement of the attentional networks at the neural level via short-term meditation. We also suggest that short-term meditation may be beneficial to individuals at high risk of cognitive deficits by improving neural mechanisms of attention.

Aberrant temporal behavior of mismatch negativity generators in schizophrenia patients and subjects at clinical high risk for psychosis.

OBJECTIVE: Although disconnection syndrome has been considered a core pathophysiologic mechanism of schizophrenia, little is known about the temporal behavior of mismatch negativity (MMN) generators in individuals with schizophrenia or clinical high risk (CHR) for psychosis. METHODS: MMN was assessed in 29 schizophrenia patients, 40 CHR subjects, and 47 healthy controls (HCs). Individual realistic head models and the minimum L2 norm algorithm were used to generate a current source density (CSD) model of MMN. The strength and time course of MMN CSD activity were calculated separately for the frontal and temporal cortices and were compared across brain regions and groups. RESULTS: Schizophrenia patients and CHR subjects displayed lower MMN CSD strength than HCs in both the temporal and frontal cortices. We found a significant time delay in MMN generator activity in the frontal cortex relative to that in the temporal cortex in HCs. However, the sequential temporo-frontal activities of MMN generators were disrupted in both the schizophrenia and CHR groups. CONCLUSIONS: Impairments and altered temporal behavior of MMN multiple generators were observed even in individuals at risk for psychosis. SIGNIFICANCE: These findings suggest that aberrant MMN generator activity might be helpful in revealing the pathophysiology of schizophrenia.