Iron Deposition and Distribution Across the Hippocampus Is Associated with Pattern Separation and Pattern Completion in Older Adults at Risk for Alzheimer's Disease.

Elevated iron deposition in the brain has been observed in older adult humans and persons with Alzheimer's disease (AD), and has been associated with lower cognitive performance. We investigated the impact of iron deposition, and its topographical distribution across hippocampal subfields and segments (anterior, posterior) measured along its longitudinal axis, on episodic memory in a sample of cognitively unimpaired older adults at elevated familial risk for AD (N = 172, 120 females, 52 males; mean age = 68.8 ± 5.4 years). MRI-based quantitative susceptibility maps were acquired to derive estimates of hippocampal iron deposition. The Mnemonic Similarity Task was used to measure pattern separation and pattern completion, two hippocampally mediated episodic memory processes. Greater hippocampal iron load was associated with lower pattern separation and higher pattern completion scores, both indicators of poorer episodic memory. Examination of iron levels within hippocampal subfields across its long axis revealed topographic specificity. Among the subfields and segments investigated here, iron deposition in the posterior hippocampal CA1 was the most robustly and negatively associated with the fidelity memory representations. This association remained after controlling for hippocampal volume and was observed in the context of normal performance on standard neuropsychological memory measures. These findings reveal that the impact of iron load on episodic memory performance is not uniform across the hippocampus. Both iron deposition levels as well as its spatial distribution, must be taken into account when examining the relationship between hippocampal iron and episodic memory in older adults at elevated risk for AD.

Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome.

Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.

Perception of social inclusion/exclusion and response inhibition in adolescents with past suicide attempt: a multidomain task-based fMRI study.

The occurrence of suicidal behaviors increases during adolescence. Hypersensitivity to negative social signals and deficits in cognitive control are putative mechanisms of suicidal behaviors, which necessitate confirmation in youths. Multidomain functional neuroimaging could enhance the identification of patients at suicidal risk beyond standard clinical measures. Three groups of adolescents (N = 96; 78% females, age = 11.6-18.1) were included: patients with depressive disorders and previous suicide attempts (SA, n = 29); patient controls with depressive disorders but without any suicide attempt history (PC, n = 35); and healthy controls (HC, n = 32). We scanned participants with 3T-MRI during social inclusion/exclusion (Cyberball Game) and response inhibition (Go-NoGo) tasks. Neural activation was indexed by the blood-oxygenation-level dependent (BOLD) of the hemodynamic response during three conditions in the Cyberball Game ("Control condition", "Social Inclusion", and "Social Exclusion"), and two conditions in Go-NoGo task ("Go" and "NoGo" blocks). ANCOVA-style analysis identified group effects across three whole-brain contrasts: 1) NoGo vs. Go, 2) Social inclusion vs. control condition, 3) Social exclusion vs. control condition. We found that SA had lower activation in the left insula during social inclusion vs. control condition compared to PC and HC. Moreover, SA compared to PC had higher activity in the right middle prefrontal gyrus during social exclusion vs. control condition, and in bilateral precentral gyri during NoGo vs. Go conditions. Task-related behavioral and self-report measures (Self-reported emotional reactivity in the Cyberball Game, response times and number of errors in the Go-NoGo Task) did not discriminate groups. In conclusion, adolescent suicidal behaviors are likely associated with neural alterations related to the processing of social perception and response inhibition. Further research, involving prospective designs and diverse cohorts of patients, is necessary to explore the potential of neuroimaging as a tool in understanding the emergence and progression of suicidal behaviors.

Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders.

BACKGROUND: Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders. METHODS: The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration. RESULTS: Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006). CONCLUSIONS: Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Immune Alterations in the Intrauterine Environment Shape Offspring Brain Development in a Sex-Specific Manner.

Exposure to immune dysregulation in utero or in early life has been shown to increase risk for neuropsychiatric illness. The sources of inflammation can be varied, including acute exposures due to maternal infection or acute stress, or persistent exposures due to chronic stress, obesity, malnutrition, or autoimmune diseases. These exposures may cause subtle alteration in brain development, structure, and function that can become progressively magnified across the life span, potentially increasing the likelihood of developing a neuropsychiatric conditions. There is some evidence that males are more susceptible to early-life inflammatory challenges than females. In this review, we discuss the various sources of in utero or early-life immune alteration and the known effects on fetal development with a sex-specific lens. To do so, we leveraged neuroimaging, behavioral, cellular, and neurochemical findings. Gaining clarity about how the intrauterine environment affects offspring development is critically important for informing preventive and early intervention measures that may buffer against the effects of these early-life risk factors.

A standardized image processing and data quality platform for rodent fMRI.

Functional magnetic resonance imaging in rodents holds great potential for advancing our understanding of brain networks. Unlike the human community, there remains no standardized resource in rodents for image processing, analysis and quality control, posing significant reproducibility limitations. Our software platform, Rodent Automated Bold Improvement of EPI Sequences, is a pipeline designed to address these limitations for preprocessing, quality control, and confound correction, along with best practices for reproducibility and transparency. We demonstrate the robustness of the preprocessing workflow by validating performance across multiple acquisition sites and both mouse and rat data. Building upon a thorough investigation into data quality metrics across acquisition sites, we introduce guidelines for the quality control of network analysis and offer recommendations for addressing issues. Taken together, this software platform will allow the emerging community to adopt reproducible practices and foster progress in translational neuroscience.

White matter microstructure in transmasculine and cisgender adolescents: A multiparametric and multivariate study.

Adolescence is a sensitive developmental period for neural sex/gender differentiation. The present study used multiparametric mapping to better characterize adolescent white matter (WM) microstructure. WM microstructure was investigated using diffusion tensor indices (fractional anisotropy; mean, radial, and axial diffusivity [AD]) and quantitative T1 relaxometry (T1) in hormone therapy naïve adolescent cisgender girls, cisgender boys, and transgender boys (i.e., assigned female at birth and diagnosed with gender dysphoria). Diffusion indices were first analyzed for group differences using tract-based spatial statistics, which revealed a group difference in AD. Thus, two multiparametric and multivariate analyses assessed AD in conjunction with T1 relaxation time, and with respect to developmental proxy variables (i.e., age, serum estradiol, pubertal development, sexual attraction) thought to be relevant to adolescent brain development. The multivariate analyses showed a shared pattern between AD and T1 such that higher AD was associated with longer T1, and AD and T1 strongly related to all five developmental variables in cisgender boys (10 significant correlations, r range: 0.21-0.73). There were fewer significant correlations between the brain and developmental variables in cisgender girls (three correlations, r range: -0.54-0.54) and transgender boys (two correlations, r range: -0.59-0.77). Specifically, AD related to direction of sexual attraction (i.e., gynephilia, androphilia) in all groups, and T1 related to estradiol inversely in cisgender boys compared with transgender boys. These brain patterns may be indicative of less myelination and tissue density in cisgender boys, which corroborates other reports of protracted WM development in cisgender boys. Further, these findings highlight the importance of considering developmental trajectory when assessing the subtleties of neural structure associated with variations in sex, gender, and sexual attraction.

Aging-Dependent Loss of Connectivity in Alzheimer's Model Mice with Rescue by mGluR5 Modulator.

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD ( App NL-G-F /hMapt ), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis.

Elevated hippocampal perfusion has been observed in people at clinical high risk for psychosis (CHR-P). Preclinical evidence suggests that hippocampal hyperactivity is central to the pathophysiology of psychosis, and that peripubertal treatment with diazepam can prevent the development of psychosis-relevant phenotypes. The present experimental medicine study examined whether diazepam can normalize hippocampal perfusion in CHR-P individuals. Using a randomized, double-blind, placebo-controlled, crossover design, 24 CHR-P individuals were assessed with magnetic resonance imaging (MRI) on two occasions, once following a single oral dose of diazepam (5 mg) and once following placebo. Regional cerebral blood flow (rCBF) was measured using 3D pseudo-continuous arterial spin labeling and sampled in native space using participant-specific hippocampus and subfield masks (CA1, subiculum, CA4/dentate gyrus). Twenty-two healthy controls (HC) were scanned using the same MRI acquisition sequence, but without administration of diazepam or placebo. Mixed-design ANCOVAs and linear mixed-effects models were used to examine the effects of group (CHR-P placebo/diazepam vs. HC) and condition (CHR-P diazepam vs. placebo) on rCBF in the hippocampus as a whole and by subfield. Under the placebo condition, CHR-P individuals (mean [±SD] age: 24.1 [±4.8] years, 15 F) showed significantly elevated rCBF compared to HC (mean [±SD] age: 26.5 [±5.1] years, 11 F) in the hippocampus (F(1,41) = 24.7, pFDR < 0.001) and across its subfields (all pFDR < 0.001). Following diazepam, rCBF in the hippocampus (and subfields, all pFDR < 0.001) was significantly reduced (t(69) = -5.1, pFDR < 0.001) and normalized to HC levels (F(1,41) = 0.4, pFDR = 0.204). In conclusion, diazepam normalized hippocampal hyperperfusion in CHR-P individuals, consistent with evidence implicating medial temporal GABAergic dysfunction in increased vulnerability for psychosis.

WarpDrive: Improving spatial normalization using manual refinements.

Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.

The impact of quality control on cortical morphometry comparisons in autism.

Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.