Abstract
The coronavirus disease-19 (COVID-19) global pandemic has already had an unprecedented impact on populations around the world, and is anticipated to have a disproportionate burden on people ...with schizophrenia and related disorders. We discuss the implications of the COVID-19 global pandemic with respect to: (1) increased risk of infection and poor outcomes among people with schizophrenia, (2) anticipated adverse mental health consequences for people with schizophrenia, (3) considerations for mental health service delivery in inpatient and outpatient settings, and (4) potential impact on clinical research in schizophrenia. Recommendations emphasize rapid implementation of measures to both decrease the risk of COVID-19 transmission and maintain continuity of clinical care and research to preserve safety of both people with schizophrenia and the public.
In patients with schizophrenia neuroimaging studies have revealed global differences with some brain regions showing focal abnormalities. Examining neurocircuitry, diffusion-weighted imaging studies ...have identified altered structural integrity of white matter in frontal and temporal brain regions and tracts such as the cingulum bundles, uncinate fasciculi, internal capsules and corpus callosum associated with the illness. Furthermore, structural co-variance analyses have revealed altered structural relationships among regional morphology in the thalamus, frontal, temporal and parietal cortices in schizophrenia patients. The distributed nature of these abnormalities in schizophrenia suggests that multiple brain circuits are impaired, a neural feature that may be better addressed with network level analyses. However, even with the advent of these newer analyses, a large amount of variability in findings remains, likely partially due to the considerable heterogeneity present in this disorder.
Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment planning for Alzheimer's disease (AD). Individual ...prognosis is complicated by many factors including the definition of the prediction objective itself. In this work, we present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories using multimodal and longitudinal data. We perform primary analyses on three cohorts from Alzheimer's Disease Neuroimaging Initiative (ADNI), and a replication analysis using subjects from Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-13) at nine timepoints spanned over six years based on a hierarchical clustering approach. Subsequently we predict these trajectory classes for a given subject using magnetic resonance (MR) imaging, genetic, and clinical variables from two timepoints (baseline + follow-up). For prediction, we present a longitudinal Siamese neural-network (LSN) with novel architectural modules for combining multimodal data from two timepoints. The trajectory modeling yields two (stable and decline) and three (stable, slow-decline, fast-decline) trajectory classes for MMSE and ADAS-13 assessments, respectively. For the predictive tasks, LSN offers highly accurate performance with 0.900 accuracy and 0.968 AUC for binary MMSE task and 0.760 accuracy for 3-way ADAS-13 task on ADNI datasets, as well as, 0.724 accuracy and 0.883 AUC for binary MMSE task on replication AIBL dataset.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Βeta‐amyloid (Aβ) is a neurotoxic protein that deposits early in the pathogenesis of preclinical Alzheimer's disease. We aimed to identify network connectivity that may alter the negative effect of ...Aβ on cognition. Following assessment of memory performance, resting‐state fMRI, and mean cortical PET‐Aβ, a total of 364 older adults (286 with clinical dementia rating CDR‐0, 59 with CDR‐0.5 and 19 with CDR‐1, mean age: 74.0 ± 6.4 years) from the OASIS‐3 sample were included in the analysis. Across all participants, a partial least squares regression showed that lower connectivity between posterior medial default mode and frontoparietal networks, higher within‐default mode, and higher visual–motor connectivity predict better episodic memory. These connectivities partially mediate the effect of Aβ on episodic memory. These results suggest that connectivity strength between the precuneus cortex and the superior frontal gyri may alter the negative effect of Aβ on episodic memory. In contrast, education was associated with different functional connectivity patterns. In conclusion, functional characteristics of specific brain networks may help identify amyloid‐positive individuals with a higher likelihood of memory decline, with implications for AD clinical trials.
Zhukovsky et al. show that connectivity of the posterior medial default mode network with frontoparietal areas mediates the relationship between amyloid‐beta levels and episodic memory. In contrast, education was associated with different functional connectivity patterns.
Abstract Schizophrenia is a highly heritable disorder. Thus, the combination of genetics and brain imaging may be a useful strategy to investigate the effects of risk genes on anatomical ...connectivity, and for gene discovery, i.e. discovering the genetic correlates of white matter phenotypes. Following a database search, I review evidence for heritability of white matter phenotypes. I also review candidate gene investigations, examining association of putative risk variants with white matter phenotypes, as well as the recent flurry of research exploring relationships of genome-wide significant risk loci with white matter phenotypes. Finally, I review multivariate and polygene approaches, which constitute a new wave of imaging-genetics research, including large collaborative initiatives aiming to discover new genes that may predict aspects of white matter microstructure. The literature supports the heritability of white matter phenotypes. Loci in genes intimately implicated in oligodendrocyte and myelin development, growth and maintenance, and neurotrophic systems are associated with white matter microstructure. GWAS variants have not yet sufficiently been explored using DTI-based evaluation of white matter to draw conclusions, although micro-RNA 137 is promising due to its potential regulation of other GWAS schizophrenia genes. Many imaging-genetic studies only include healthy participants, which, while helping control for certain confounds, cannot address questions related to disease heterogeneity or symptom expression, and thus more studies should include participants with schizophrenia. With sufficiently large sample sizes, the future of this field lies in polygene strategies aimed at risk prediction and heterogeneity dissection of schizophrenia that can translate to personalized interventions.
Schizophrenia and bipolar disorder (BD) may be disorders of accelerated aging. Direct comparison of healthy aging populations with schizophrenia and BD patients across the adult lifespan may help ...inform this theory. In total, 225 individuals (91 healthy controls, 81 schizophrenia, 53 euthymic BD) underwent 3T T1-weighted magnetic resonance imaging, diffusion tensor imaging, and cognitive testing. We analyzed associations among age, diagnosis, and cognition with cortical thickness and fractional anisotropy (FA) using general linear models. We then assessed "brain age" using a random forest algorithm, which was also assessed in an independent sample (n = 147). Participants with schizophrenia had lower cortical thickness and FA compared with the other two groups, most prominently in fronto-temporal circuitry. These brain changes were more evident in younger participants than in older ones, yet were associated with cognitive performance independent of diagnosis. Predicted age was 8 years greater than chronological age in individuals with schizophrenia in the first sample and 6 years greater in the second sample. Predicted and chronological age were not different in BD. Differences in brain circuitry are present from illness onset most prominently in schizophrenia and to a lesser extent in BD. These results support a non-progressive "early hit" hypothesis/etiology of illness in the major psychoses. Brain age differences support the hypothesized early aging mechanism in schizophrenia but not in BD.
Abstract Background Post-mortem studies have demonstrated considerable dendritic pathologies among persons with schizophrenia and to some extent among those with bipolar-I disorder(BD-I). Modeling ...gray matter(GM) microstructural properties is now possible with a recently proposed diffusion-weighted MRI modeling technique: Neurite-Orientation Dispersion and Density Imaging(NODDI). This technique may bridge the gap between neuroimaging and histopathological findings. Methods We performed an extended series of multi-shell diffusion-weighted imaging and other structural imaging series using a 3T MRI. Participants scanned included individuals with schizophrenia(n=36), BD-I(n=29), and healthy controls(HC;n=35). GM-based spatial statistics was used to compare NODDI-driven microstructural measures(orientation-dispersion-index and neurite-density-indexNDI) among groups, and assess their relationship with neurocognitive performance. We also investigated the accuracy of these measures in the prediction of group membership, and whether combining them with cortical thickness and white matter fractional anisotropy further improved accuracy. Results The GM-NDI was significantly lower in temporal pole, anterior parahippocampal gyrus, and hippocampus of the schizophrenia patients than the HC. The GM-NDI of BD-I patients did not differ significantly from either schizophrenia patients or HC, and was intermediate between the two groups in the post-hoc analysis. Irrespective of diagnosis, higher performance in spatial working memory was significantly associated with higher GM-NDI mainly in the fronto-temporal areas. Addition of GM-NDI to cortical thickness resulted in higher accuracy to predict group membership. Conclusions GM-NDI captures brain differences in the major psychoses not accessible with other structural-MRI methods. Given the strong association of GM-NDI with disease state and neurocognitive performance, its potential utility for biological subtyping should be further explored.
Cognitive deficits are a core feature of schizophrenia. Among these deficits, working memory impairment is considered a central cognitive impairment in schizophrenia. The prefrontal cortex, a region ...critical for working memory performance, has been demonstrated as a critical liability region in schizophrenia. As yet, there are no standardized treatment options for working memory deficits in schizophrenia. In this review, we summarize the neuronal basis for working memory impairment in schizophrenia, including dysfunction in prefrontal signaling pathways (e.g., γ-aminobutyric acid transmission) and neural network synchrony (e.g., gamma/theta oscillations). We discuss therapeutic strategies for working memory dysfunction such as pharmacological agents, cognitive remediation therapy, and repetitive transcranial magnetic stimulation. Despite the drawbacks of current approaches, the advances in neurobiological and translational treatment strategies suggest that clinical application of these methods will occur in the near future.
Advances in image segmentation of magnetic resonance images (MRI) have demonstrated that multi-atlas approaches improve segmentation over regular atlas-based approaches. These approaches often rely ...on a large number of manually segmented atlases (e.g. 30–80) that take significant time and expertise to produce. We present an algorithm, MAGeT-Brain (Multiple Automatically Generated Templates), for the automatic segmentation of the hippocampus that minimises the number of atlases needed whilst still achieving similar agreement to multi-atlas approaches. Thus, our method acts as a reliable multi-atlas approach when using special or hard-to-define atlases that are laborious to construct.
MAGeT-Brain works by propagating atlas segmentations to a template library, formed from a subset of target images, via transformations estimated by nonlinear image registration. The resulting segmentations are then propagated to each target image and fused using a label fusion method.
We conduct two separate Monte Carlo cross-validation experiments comparing MAGeT-Brain and basic multi-atlas whole hippocampal segmentation using differing atlas and template library sizes, and registration and label fusion methods. The first experiment is a 10-fold validation (per parameter setting) over 60 subjects taken from the Alzheimer's Disease Neuroimaging Database (ADNI), and the second is a five-fold validation over 81 subjects having had a first episode of psychosis. In both cases, automated segmentations are compared with manual segmentations following the Pruessner-protocol. Using the best settings found from these experiments, we segment 246 images of the ADNI1:Complete 1Yr 1.5T dataset and compare these with segmentations from existing automated and semi-automated methods: FSL FIRST, FreeSurfer, MAPER, and SNT. Finally, we conduct a leave-one-out cross-validation of hippocampal subfield segmentation in standard 3T T1-weighted images, using five high-resolution manually segmented atlases (Winterburn et al., 2013).
In the ADNI cross-validation, using 9 atlases MAGeT-Brain achieves a mean Dice's Similarity Coefficient (DSC) score of 0.869 with respect to manual whole hippocampus segmentations, and also exhibits significantly lower variability in DSC scores than multi-atlas segmentation. In the younger, psychosis dataset, MAGeT-Brain achieves a mean DSC score of 0.892 and produces volumes which agree with manual segmentation volumes better than those produced by the FreeSurfer and FSL FIRST methods (mean difference in volume: 80mm3, 1600mm3, and 800mm3, respectively). Similarly, in the ADNI1:Complete 1Yr 1.5T dataset, MAGeT-Brain produces hippocampal segmentations well correlated (r>0.85) with SNT semi-automated reference volumes within disease categories, and shows a conservative bias and a mean difference in volume of 250mm3 across the entire dataset, compared with FreeSurfer and FSL FIRST which both overestimate volume differences by 2600mm3 and 2800mm3 on average, respectively. Finally, MAGeT-Brain segments the CA1, CA4/DG and subiculum subfields on standard 3T T1-weighted resolution images with DSC overlap scores of 0.56, 0.65, and 0.58, respectively, relative to manual segmentations.
We demonstrate that MAGeT-Brain produces consistent whole hippocampal segmentations using only 9 atlases, or fewer, with various hippocampal definitions, disease populations, and image acquisition types. Additionally, we show that MAGeT-Brain identifies hippocampal subfields in standard 3T T1-weighted images with overlap scores comparable to competing methods.
•We propose an automated MR image hippocampus (and subfield) segmentation method.•Our method is optimised for use with a small number (<10) of training images.•Consistent, accurate identification of the whole hippocampus and subfields•Validated on healthy, Alzheimer's disease, and first episode psychosis subjects•Source code and high-resolution training subfield atlases available online
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