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OBJECTIVE: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia—signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume—could be replicated in an independent schizophrenia sample, and investigated whether expression of these signatures can be detected at the population level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. METHODS: This cross-sectional study used an independent schizophrenia-control sample (N=347; ages 16–57 years) for replication of imaging signatures, and then examined two independent population-level data sets: typically developing youths and youths with psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort (N=359; ages 16–23 years) and adults in the UK Biobank study (N=836; ages 44–50 years). The authors quantified signature expression using support-vector machine learning and compared cognition, psychopathology, and polygenic risk between signatures. RESULTS: Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youths with psychosis spectrum symptoms than in typically developing youths, whereas signature 2 frequency was similar in the two groups. In both youths and adults, signature 1 was associated with worse cognitive performance than signature 2. Compared with adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. CONCLUSIONS: The authors successfully replicated two neuroanatomical signatures of schizophrenia and describe their prevalence in population-based samples of youths and adults. They further demonstrated distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal volume’ subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership (‘None’), and mixed SG1 + SG2 subgroups (‘Mixed’). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of ‘lower brain volume’ in SG1 and ‘higher striatal volume’ (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p

Opioid drugs have long served as important agents for pain management in the clinic, but misuse of opioids, especially of illicit ultra-potent synthetic opioids like fentanyl, is driving the growing opioid epidemic. Like the natural opioid morphine, fentanyl targets μ-opioid receptor signaling in the central nervous system. However, the pharmacology of fentanyl on the opioid receptors differs from morphine, and clinical symptoms of fentanyl overdose like opioid-induced respiratory depression (OIRD) are more severe and less well-managed by reversal agents like naloxone. Hypoventilation due to OIRD stimulate peripheral chemoreceptors like the carotid body (CB). Previous reports suggest that morphine has minor effects on CB sensory output or the hypoxic ventilatory response mediated by the CB in animal models. Denervation of the CB significantly attenuates the hypoxic ventilatory response of unaesthetized rats after morphine treatment, suggesting that CB sensory activity persists in the presence of morphine to defend against OIRD. However, the effect of fentanyl on CB sensory responses has been little explored. Our goal was to determine how fentanyl regulates CB activity at the level of glomus cells, neuroendocrine sensory cells in the CB that signal to afferent nerves that project to the brainstem to increase ventilation. Sensory stimuli like hypoxia induce calcium transients in glomus cells across all species studied, including human. By two-photon imaging, we performed calcium imaging of mouse glomus cells expressing the genetically encoded calcium indicator GCaMP3 in intact CBs. Whereas morphine suppresses the activity of neurons in the central nervous system, application of fentanyl at levels sufficient to induce OIRD in mice acutely increased calcium transients in CB glomus cells. This increase in intracellular calcium was suppressed by naloxone, indicating that the stimulatory effect of fentanyl was mediated through opioid receptor signaling. In addition, we tested if glomus cells could respond to sensory stimuli in the presence of fentanyl. We found that the combination of fentanyl and hypoxia (PO2~25 mmHg) increased calcium responses over either fentanyl or hypoxia alone. These results suggest that CB stimulation through sensory pathways could potentially be targeted to increase ventilation in OIRD. Experimental groups contained 22-73 cells from least two preparations from different animals, and p This work is funded by a UCSF Innovation Ventures Catalyst Grant and NIH Grant R41DA056270. AJC is a co-founder of Olfa Thera, Inc. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

ImportanceAutism spectrum disorder (ASD) is associated with significant clinical, neuroanatomical, and genetic heterogeneity that limits precision diagnostics and treatment.ObjectiveTo assess distinct neuroanatomical dimensions of ASD using novel semisupervised machine learning methods and to test whether the dimensions can serve as endophenotypes also in non-ASD populations.Design, Setting, and ParticipantsThis cross-sectional study used imaging data from the publicly available Autism Brain Imaging Data Exchange (ABIDE) repositories as the discovery cohort. The ABIDE sample included individuals diagnosed with ASD aged between 16 and 64 years and age- and sex-match typically developing individuals. Validation cohorts included individuals with schizophrenia from the Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging (PHENOM) consortium and individuals from the UK Biobank to represent the general population. The multisite discovery cohort included 16 internationally distributed imaging sites. Analyses were performed between March 2021 and March 2022.Main Outcomes and MeasuresThe trained semisupervised heterogeneity through discriminative analysis models were tested for reproducibility using extensive cross-validations. It was then applied to individuals from the PHENOM and the UK Biobank. It was hypothesized that neuroanatomical dimensions of ASD would display distinct clinical and genetic profiles and would be prominent also in non-ASD populations.ResultsHeterogeneity through discriminative analysis models trained on T1-weighted brain magnetic resonance images of 307 individuals with ASD (mean [SD] age, 25.4 [9.8] years; 273 [88.9%] male) and 362 typically developing control individuals (mean [SD] age, 25.8 [8.9] years; 309 [85.4%] male) revealed that a 3-dimensional scheme was optimal to capture the ASD neuroanatomy. The first dimension (A1: aginglike) was associated with smaller brain volume, lower cognitive function, and aging-related genetic variants (FOXO3; Z = 4.65; P = 1.62 × 10−6). The second dimension (A2: schizophrenialike) was characterized by enlarged subcortical volumes, antipsychotic medication use (Cohen d = 0.65; false discovery rate–adjusted P = .048), partially overlapping genetic, neuroanatomical characteristics to schizophrenia (n = 307), and significant genetic heritability estimates in the general population (n = 14 786; mean [SD] h2, 0.71 [0.04]; P −4). The third dimension (A3: typical ASD) was distinguished by enlarged cortical volumes, high nonverbal cognitive performance, and biological pathways implicating brain development and abnormal apoptosis (mean [SD] β, 0.83 [0.02]; P = 4.22 × 10−6).Conclusions and RelevanceThis cross-sectional study discovered 3-dimensional endophenotypic representation that may elucidate the heterogeneous neurobiological underpinnings of ASD to support precision diagnostics. The significant correspondence between A2 and schizophrenia indicates a possibility of identifying common biological mechanisms across the 2 mental health diagnoses.

IMPORTANCE: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. OBJECTIVE: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. DESIGN, SETTING, AND PARTICIPANTS: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. MAIN OUTCOMES AND MEASURES: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. RESULTS: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×10(8)) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant–based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f(2) = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f(2) = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer’s Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). CONCLUSIONS AND RELEVANCE: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.

The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume, and signature 2 with increased striatal volume, could be replicated in an independent schizophrenia sample, and investigate whether expression of these signatures can be detected at the population-level and how they relate to cognition, psychosis spectrum symptoms, and schizophrenia genetic risk. This cross-sectional study used an independent schizophrenia-control sample (n=347; age 16-57 years) for replication of imaging signatures, and then examined two independent population-level datasets: Philadelphia Neurodevelopmental Cohort [PNC; n=359 typically developing (TD) and psychosis-spectrum symptoms (PS) youth] and UK Biobank (UKBB; n=836; age 44-50 years) adults. We quantified signature expression using support-vector machine learning, and compared cognition, psychopathology, and polygenic risk between signatures. Two neuroanatomical signatures of schizophrenia were replicated. Signature 1 but not signature 2 was significantly more common in youth with PS than TD youth, whereas signature 2 frequency was similar. In both youth and adults, signature 1 had worse cognitive performance than signature 2. Compared to adults with neither signature, adults expressing signature 1 had elevated schizophrenia polygenic risk scores, but this was not seen for signature 2. We successfully replicate two neuroanatomical signatures of schizophrenia, and describe their prevalence in population-based samples of youth and adults. We further demonstrate distinct relationships of these signatures with psychosis symptoms, cognition, and genetic risk, potentially reflecting underlying neurobiological vulnerability.

Background In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. Purpose To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. Study type Retrospective. Population Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. Field strength/sequence Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. Assessment StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. Statistical tests Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. Results Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. Data conclusion While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization. Level of evidence 4 TECHNICAL EFFICACY: Stage 1.

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies, potentially preventing spurious findings and improving reliability of results.

PURPOSE: Hypertension is a risk factor for cognitive impairment; however, the mechanisms leading to cognitive changes remain unclear. In this cross-sectional study, we evaluate the impact of white matter lesion (WML) burden on brain functional connectivity (FC) and cognition in a large cohort of hypertensive patients from the Systolic Blood Pressure Intervention Trial (SPRINT) at baseline. METHODS: Functional networks were identified from baseline resting state functional MRI scans of 660 SPRINT participants using independent component analysis. WML volumes were calculated from structural MRI. Correlation analyses were carried out between mean FC of each functional network and global WML as well as WML within atlas-defined white matter regions. For networks of interest, voxel-wise-adjusted correlation analyses between FC and regional WML volume were performed. Multiple variable linear regression models were built for cognitive test performance as a function of network FC, followed by mediation analysis. RESULTS: Mean FC of the default mode network (DMN) was negatively correlated with global WML volume, and regional WML volume within the precuneus. Voxel-wise correlation analyses revealed that regional WML was negatively correlated with FC of the DMN’s left lateral temporal region. FC in this region of the DMN was positively correlated to performance on the Montreal Cognitive Assessment and demonstrated significant mediation effects. Additional networks also demonstrated global and regional WML correlations; however, they did not demonstrate an association with cognition. CONCLUSION: In hypertensive patients, greater WML volume is associated with lower FC of the DMN, which in turn is related to poorer cognitive test performance. TRIAL REGISTRATION: NCT01206062

Background: Anemia and red cell distribution width (RDW) have been linked to poor cognitive performance, pending studies of underlying mechanisms. Objective: We examined cross-sectional relationships of initial RDW status (v1), RDW change (δ), and anemia with brain structural magnetic resonance imaging (sMRI) markers, including global and cortical brain and hippocampal and white matter lesion (WML) volumes, 5–6 years later. Methods: Data were used from three prospective visits within the Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) study with complete v1 (2004–2009) and v2 (2009–2013) exposures and ancillary sMRI data at vscan (2011–2015, n = 213, mean v1 to vscan time: 5.7 years). Multivariable-adjusted linear regression models were conducted, overall, by sex, by race, and within non-anemics, correcting for multiple testing with q-values. Results: In minimally adjusted models (socio-demographics and follow-up time), anemiav1 and RDWv1 were consistently associated with smaller bilateral hippocampal volumes overall, and among females (q

Automatic segmentation of brain anatomy has been a key processing step in quantitative neuroimaging analyses. An extensive body of literature has relied on Freesurfer segmentations. Yet, in recent years, the multi-atlas segmentation framework has consistently obtained results with superior accuracy in various evaluations. We compared brain anatomy segmentations from Freesurfer, which uses a single probabilistic atlas strategy, against segmentations from Multi-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters and locally optimal atlas selection (MUSE), one of the leading ensemble-based methods that calculates a consensus segmentation through fusion of anatomical labels from multiple atlases and registrations. The focus of our evaluation was twofold. First, using manual ground-truth hippocampus segmentations, we found that Freesurfer segmentations showed a bias towards over-segmentation of larger hippocampi, and under-segmentation in older age. This bias was more pronounced in Freesurfer-v5.3, which has been used in multiple previous studies of aging, while the effect was mitigated in more recent Freesurfer-v6.0, albeit still present. Second, we evaluated inter-scanner segmentation stability using same day scan pairs from ADNI acquired on 1.5T and 3T scanners. We also found that MUSE obtains more consistent segmentations across scanners compared to Freesurfer, particularly in the deep structures.

Introduction Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). Methods Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. Results WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. Discussion A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

Developmental Coordination Disorder (DCD) is as a neurodevelopmental condition characterized by poor motor proficiency, which impacts academic performance and activities of daily living. Several studies have determined that children with DCD activate different regions of the brain when performing motor skills in comparison to typically developing (TD) children. However, none have used Functional Near-Infrared Spectroscopy (fNIRS) to explore cortical activation in this population. With that, the goal of this preliminary study was to investigate cortical activation using fNIRS in six children with DCD and six TD children between ages of 8 and 12 years. Three fine-motor tasks were performed: Finger Tapping (FT), Curve Tracing (CT), and Paragraph Writing (PW). Tasks were presented in counterbalanced order and had a baseline of 30s. Cortical activity elicited during performance of the FT, CT, and PW tasks was measured by fNIRS, and activation areas within each group were statistically compared. Results indicated that participant groups used different focal activation areas as well as different neural networks to perform the tasks. These distinct patterns were also task-specific, with differences in the right Pre-Motor Cortex (Pre-MC) and Supplementary Motor Area (SMA) for CT, and the right Dorsolateral Prefrontal Cortex (DLPFC) and the right Pre-MC for the PW task. These results add to the body of research exploring neurological alterations in children with DCD, and establish the feasibility of using fNIRS technology with this population.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed to discover patterns associated with disease rather than normal anatomical variation. Structural MRI and clinical measures in established schizophrenia (n = 307) and healthy controls (n = 364) were analysed across three sites of PHENOM (Psychosis Heterogeneity Evaluated via Dimensional Neuroimaging) consortium. Regional volumetric measures of grey matter, white matter, and CSF were used to identify distinct and reproducible neuroanatomical subtypes of schizophrenia. Two distinct neuroanatomical subtypes were found. Subtype 1 showed widespread lower grey matter volumes, most prominent in thalamus, nucleus accumbens, medial temporal, medial prefrontal/frontal and insular cortices. Subtype 2 showed increased volume in the basal ganglia and internal capsule, and otherwise normal brain volumes. Grey matter volume correlated negatively with illness duration in Subtype 1 (r = −0.201, P = 0.016) but not in Subtype 2 (r = −0.045, P = 0.652), potentially indicating different underlying neuropathological processes. The subtypes did not differ in age (t = −1.603, df = 305, P = 0.109), sex (chi-square = 0.013, df = 1, P = 0.910), illness duration (t = −0.167, df = 277, P = 0.868), antipsychotic dose (t = −0.439, df = 210, P = 0.521), age of illness onset (t = −1.355, df = 277, P = 0.177), positive symptoms (t = 0.249, df = 289, P = 0.803), negative symptoms (t = 0.151, df = 289, P = 0.879), or antipsychotic type (chi-square = 6.670, df = 3, P = 0.083). Subtype 1 had lower educational attainment than Subtype 2 (chi-square = 6.389, df = 2, P = 0.041). In conclusion, we discovered two distinct and highly reproducible neuroanatomical subtypes. Subtype 1 displayed widespread volume reduction correlating with illness duration, and worse premorbid functioning. Subtype 2 had normal and stable anatomy, except for larger basal ganglia and internal capsule, not explained by antipsychotic dose. These subtypes challenge the notion that brain volume loss is a general feature of schizophrenia and suggest differential aetiologies. They can facilitate strategies for clinical trial enrichment and stratification, and precision diagnostics.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

A lossless symmetric key encryption scheme is presented, which makes use of the Discrete Haar Wavelet Transform and the Arnold Cat Map. In the frequency domain, scaling, negation and coefficient swapping are done to increase robustness of the cipher image. In order to ensure better security and reliability, Arnold Cat Map is used for pixel scrambling. Performance analysis is performed on the encrypted image using security and statistical measures. The encrypted image is found to be highly random and decorrelated and will thus be resistant to attacks.