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

PennLINC/Fixel_NMF_development: v1.0.0

Preprocessing and data analysis scripts for fixel dat

Q&A with Ted Satterthwaite and Joëlle Bagautdinova

We at Cell Reports discuss the work, interests, and mentoring experiences of Theodore Satterthwaite (TS) and his graduate student and co-author, Joëlle Bagautdinova (JB). They share with us their recent findings highlighting the relationship between the development of cognitive function and white matter and also talk about the challenges and technical advances in cognitive neuroscience and neuroimaging

Excitatory/Inhibitory Imbalance Underlies Hippocampal Atrophy in Individuals With 22q11.2 Deletion Syndrome With Psychotic Symptoms

Background: Abnormal neurotransmitter levels have been reported in individuals at high risk for schizophrenia, leading to a shift in the excitatory/inhibitory balance. However, it is unclear whether these alterations predate the onset of clinically relevant symptoms. Our aim was to explore in vivo measures of excitatory/inhibitory balance in 22q11.2 deletion carriers, a population at genetic risk for psychosis. Methods: Glx (glutamate+glutamine) and GABA+ (gamma-aminobutyric acid with macromolecules and homocarnosine) concentrations were estimated in the anterior cingulate cortex, superior temporal cortex, and hippocampus using the Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS) sequence and the Gannet toolbox in 52 deletion carriers and 42 control participants. T1-weighted images were acquired longitudinally and processed with FreeSurfer version 6 to extract hippocampal volume. Subgroup analyses were conducted in deletion carriers with psychotic symptoms. Results: While no differences were found in the anterior cingulate cortex, deletion carriers had higher levels of Glx in the hippocampus and superior temporal cortex and lower levels of GABA+ in the hippocampus than control participants. We additionally found a higher Glx concentration in the hippocampus of deletion carriers with psychotic symptoms. Finally, more pronounced hippocampal atrophy was significantly associated with increased Glx levels in deletion carriers. Conclusions: We provide evidence for an excitatory/inhibitory imbalance in temporal brain structures of deletion carriers, with a further hippocampal Glx increase in individuals with psychotic symptoms that was associated with hippocampal atrophy. These results are in line with theories proposing abnormally enhanced glutamate levels as a mechanistic explanation for hippocampal atrophy via excitotoxicity. Our results highlight a central role of glutamate in the hippocampus of individuals at genetic risk for schizophrenia.</p

Altered cortical thickness development in 22q11.2 deletion syndrome and association with psychotic symptoms

Schizophrenia has been extensively associated with reduced cortical thickness (CT), and its neurodevelopmental origin is increasingly acknowledged. However, the exact timing and extent of alterations occurring in preclinical phases remain unclear. With a high prevalence of psychosis, 22q11.2 deletion syndrome (22q11DS) is a neurogenetic disorder that represents a unique opportunity to examine brain maturation in high-risk individuals. In this study, we quantified trajectories of CT maturation in 22q11DS and examined the association of CT development with the emergence of psychotic symptoms. Longitudinal structural MRI data with 1-6 time points were collected from 324 participants aged 5-35 years (N = 148 22q11DS, N = 176 controls), resulting in a total of 636 scans (N = 334 22q11DS, N = 302 controls). Mixed model regression analyses were used to compare CT trajectories between participants with 22q11DS and controls. Further, CT trajectories were compared between participants with 22q11DS who developed (N = 61, 146 scans), or remained exempt of (N = 47; 98 scans) positive psychotic symptoms during development. Compared to controls, participants with 22q11DS showed widespread increased CT, focal reductions in the posterior cingulate gyrus and superior temporal gyrus (STG), and accelerated cortical thinning during adolescence, mainly in frontotemporal regions. Within 22q11DS, individuals who developed psychotic symptoms showed exacerbated cortical thinning in the right STG. Together, these findings suggest that genetic predisposition for psychosis is associated with increased CT starting from childhood and altered maturational trajectories of CT during adolescence, affecting predominantly frontotemporal regions. In addition, accelerated thinning in the STG may represent an early biomarker associated with the emergence of psychotic symptoms

Sleep abnormalities in different clinical stages of psychosis:A systematic review and meta-analysis

Importance: Abnormal sleep is frequent in psychosis; however, sleep abnormalities in different stages (ie, clinical high risk for psychosis [CHR-P], early psychosis [EP], and chronic psychosis [CP]) have not been characterized. Objective: To identify sleep abnormalities across psychosis stages. Data sources: Web of Science and PubMed were searched between inception and June 15, 2022. Studies written in English were included. Study selection: Sleep disturbance prevalence studies and case-control studies reporting sleep quality, sleep architecture, or sleep electroencephalography oscillations in CHR-P, EP, or CP. Data extraction and synthesis: This systematic review and meta-analysis followed Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Stage-specific and pooled random-effects meta-analyses were conducted, along with the assessment of heterogeneity, study quality, and meta-regressions (clinical stage, sex, age, medication status, and psychotic symptoms). Main outcomes and measures: Sleep disturbance prevalence, self-reported sleep quality, sleep architecture (total sleep time, sleep latency, sleep efficiency, nonrapid eye movement, rapid eye movement stages, and number of arousals), and sleep electroencephalography oscillations (spindle density, amplitude, and duration, and slow wave density). Results: Fifty-nine studies with up to 6710 patients (n = 5135 for prevalence) and 977 controls were included. Sleep disturbance prevalence in pooled cases was 50% (95% CI, 40%-61%) and it was similar in each psychosis stage. Sleep quality was worse in pooled cases vs controls (standardized mean difference [SMD], 1.00 [95% CI, 0.70-1.30]). Sleep architecture alterations included higher sleep onset latency (SMD [95% CI]: pooled cases, 0.96 [0.62-1.30]; EP, 0.72 [0.52-0.92]; CP, 1.36 [0.66-2.05]), higher wake after sleep onset (SMD [95% CI]: pooled cases, 0.5 [0.29-0.71]; EP, 0.62 [0.34-0.89]; CP, 0.51 [0.09-0.93]), higher number of arousals (SMD [95% CI]: pooled cases, 0.45 [0.07-0.83]; CP, 0.81 [0.30-1.32]), higher stage 1 sleep (SMD [95% CI]: pooled cases, 0.23 [0.06-0.40]; EP, 0.34 [0.15-0.53]), lower sleep efficiency (SMD [95% CI]: pooled cases, -0.75 [-0.98 to -0.52]; EP, -0.90 [-1.20 to -0.60]; CP, -0.73 [-1.14 to -0.33]), and lower rapid eye movement density (SMD [95% CI]: pooled cases, 0.37 [0.14-0.60]; CP, 0.4 [0.19-0.77]). Spindle parameter deficits included density (SMD [95% CI]: pooled cases, -1.06 [-1.50 to -0.63]; EP, -0.80 [-1.22 to -0.39]; CP, -1.39 [-2.05 to -0.74]; amplitude: pooled cases, -1.08 [-1.33 to -0.82]; EP, -0.86 [-1.24 to -0.47]; CP, -1.25 [-1.58 to -0.91]; and duration: pooled cases: -1.2 [-1.69 to -0.73]; EP, -0.71 [-1.08 to -0.34]; CP, -1.74 [-2.10 to -1.38]). Individuals with CP had more frequent arousals vs CHR-P (z = 2.24, P = .02) and reduced spindle duration vs EP (z = -3.91, P < .001). Conclusions and relevance: In this systematic review and meta-analysis, sleep disturbances were found to be prevalent throughout the course of psychosis, and different psychosis stages showed both shared and distinct abnormalities in sleep quality, architecture, and spindles. These findings suggest that sleep should become a core clinical target and research domain from at-risk to early and chronic stages of psychosis

ModelArray: An R package for statistical analysis of fixel-wise data

ABSTRACT: Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data