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Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine., (© 2021 European Sleep Research Society.)

Background: Altered brain connectivity is implicated in the development and clinical burden of schizophrenia. Relative to matched controls, schizophrenia patients show (1) a global and regional reduction in the integrity of the brain's white matter (WM), assessed using diffusion tensor imaging (DTI) fractional anisotropy (FA), and (2) accelerated age-related decline in FA values. In the largest mega-analysis to date, we tested if differences in the trajectories of WM tract development influenced patient-control differences in FA. We also assessed if specific tracts showed exacerbated decline with aging., Methods: Three cohorts of schizophrenia patients (total n = 177) and controls (total n = 249; age = 18-61 years) were ascertained with three 3T Siemens MRI scanners. Whole-brain and regional FA values were extracted using ENIGMA-DTI protocols. Statistics were evaluated using mega- and meta-analyses to detect effects of diagnosis and age-by-diagnosis interactions., Results: In mega-analysis of whole-brain averaged FA, schizophrenia patients had lower FA (P = 10 -11 ) and faster age-related decline in FA (P = 0.02) compared with controls. Tract-specific heterochronicity measures, that is, abnormal rates of adolescent maturation and aging explained approximately 50% of the regional variance effects of diagnosis and age-by-diagnosis interaction in patients. Interactive, three-dimensional visualization of the results is available at www.enigma-viewer.org., Conclusion: WM tracts that mature later in life appeared more sensitive to the pathophysiology of schizophrenia and were more susceptible to faster age-related decline in FA values. Hum Brain Mapp 37:4673-4688, 2016. © 2016 Wiley Periodicals, Inc., Competing Interests: Authors declare no conflict of interest., (© 2016 Wiley Periodicals, Inc.)

Reading is a multisensory function that relies on arbitrary associations between auditory speech sounds and symbols from a second modality. Studies of bimodal phonetic perception have mostly investigated the integration of visual letters and speech sounds. Blind readers perform an analogous task by using tactile Braille letters instead of visual letters. The neural underpinnings of audiotactile phonetic processing have not been studied before. We used functional magnetic resonance imaging to reveal the neural correlates of audiotactile phonetic processing in 16 early-blind Braille readers. Braille letters and corresponding speech sounds were presented in unimodal, and congruent/incongruent bimodal configurations. We also used a behavioral task to measure the speed of blind readers in identifying letters presented via tactile and/or auditory modalities. Reaction times for tactile stimuli were faster. The reaction times for bimodal stimuli were equal to those for the slower auditory-only stimuli. fMRI analyses revealed the convergence of unimodal auditory and unimodal tactile responses in areas of the right precentral gyrus and bilateral crus I of the cerebellum. The left and right planum temporale fulfilled the 'max criterion' for bimodal integration, but activities of these areas were not sensitive to the phonetical congruency between sounds and Braille letters. Nevertheless, congruency effects were found in regions of frontal lobe and cerebellum. Our findings suggest that, unlike sighted readers who are assumed to have amodal phonetic representations, blind readers probably process letters and sounds separately. We discuss that this distinction might be due to mal-development of multisensory neural circuits in early blinds or it might be due to inherent differences between Braille and print reading mechanisms.

Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study., (Copyright © 2015. Published by Elsevier Inc.)

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Objective: Abstinence-based therapy (ABT) and methadone maintenance therapy (MMT) are common methods of treatment in heroin dependence as both suppress subjective feeling of drug craving. However, it is not clear whether the neural basis of craving suppression is similar in both types of treatments. In this study, we compared brain activation during pictorial presentation of heroin-related cues in ABT and MMT groups to understand the neural basis of drug craving in these groups., Methods: Three groups of subjects (successful ABT and MMT clients and healthy control) underwent functional magnetic resonance imaging, while heroin-related cues and neutral cues were presented to them. In addition, subjective cue-elicited craving has been measured using drug drive questionnaire before and after imaging., Result: Self-report of craving was not different between ABT and MMT groups before and after scanning. Anterior cingulate cortex and inferior frontal gyrus showed higher activations in ABT than in healthy control. Inferior frontal gyrus and superior temporal gyrus showed higher activity in ABT than in MMT. Lingual gyrus and cerebellum showed higher activity in MMT than in healthy control., Conclusions: Heroin avoidance may be achieved by MMT or ABT; however, the neural mechanism underlying these therapeutic methods differs.