Discovering the gene-brain-behavior link in autism via generative machine learning.

Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variability is a challenge. We demonstrate a novel technique, 3D transport-based morphometry (TBM), to extract the structural brain changes linked to genetic copy number variation (CNV) at the 16p11.2 region. We identified two distinct endophenotypes. In data from the Simons Variation in Individuals Project, detection of these endophenotypes enabled 89 to 95% test accuracy in predicting 16p11.2 CNV from brain images alone. Then, TBM enabled direct visualization of the endophenotypes driving accurate prediction, revealing dose-dependent brain changes among deletion and duplication carriers. These endophenotypes are sensitive to articulation disorders and explain a portion of the intelligence quotient variability. Genetic stratification combined with TBM could reveal new brain endophenotypes in many neurodevelopmental disorders, accelerating precision medicine, and understanding of human neurodiversity.

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach.

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Mapping the individual human cortex using multidimensional MRI and unsupervised learning.

Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion-relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion-relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion-relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.

Investigating impact of cardiorespiratory fitness in reducing brain tissue loss caused by ageing.

Mitigating the loss of brain tissue due to age is a major problem for an ageing population. Improving cardiorespiratory fitness has been suggested as a possible strategy, but the influenceon brain morphology has not been fully characterized. To investigate the dependent shifts in brain tissue distribution as a function of cardiorespiratory fitness, we used a 3D transport-based morphometry approach. In this study of 172 inactive older adults aged 58-81 (66.5 ± 5.7) years, cardiorespiratory fitness was determined by V O 2 peak (ml/kg/min) during graded exercise and brain morphology was assessed through structural magnetic resonance imaging. After correcting for covariates including age (in the fitness model), gender and level of education, we compared dependent tissue shifts with age to those due to V O 2   peak . We found a significant association between cardiorespiratory fitness and brain tissue distribution (white matter, r = 0.30, P = 0.003; grey matter, r = 0.40, P < 0.001) facilitated by direct visualization of the brain tissue shifts due to cardiorespiratory fitness through inverse transformation-a key capability of 3D transport-based morphometry. A strong statistical correlation was found between brain tissue changes related to ageing and those associated with lower cardiorespiratory fitness (white matter, r = 0.62, P < 0.001; grey matter, r = 0.74, P < 0.001). In both cases, frontotemporal regions shifted the most while basal ganglia shifted the least. Our results highlight the importance of cardiorespiratory fitness in maintaining brain health later in life. Furthermore, this work demonstrates 3D transport-based morphometry as a novel neuroinformatic technology that may aid assessment of therapeutic approaches for brain ageing and neurodegenerative diseases.

How will artificial intelligence change medical training?

Artificial intelligence is changing medicine and it will relieve physicians from the burden of rote knowledge. Here, I discuss how this might affect medical training, drawing from the example of how automation in aviation redefined the role of the pilot.

Enabling early detection of osteoarthritis from presymptomatic cartilage texture maps via transport-based learning.

Many diseases have no visual cues in the early stages, eluding image-based detection. Today, osteoarthritis (OA) is detected after bone damage has occurred, at an irreversible stage of the disease. Currently no reliable method exists for OA detection at a reversible stage. We present an approach that enables sensitive OA detection in presymptomatic individuals. Our approach combines optimal mass transport theory with statistical pattern recognition. Eighty-six healthy individuals were selected from the Osteoarthritis Initiative, with no symptoms or visual signs of disease on imaging. On 3-y follow-up, a subset of these individuals had progressed to symptomatic OA. We trained a classifier to differentiate progressors and nonprogressors on baseline cartilage texture maps, which achieved a robust test accuracy of 78% in detecting future symptomatic OA progression 3 y prior to symptoms. This work demonstrates that OA detection may be possible at a potentially reversible stage. A key contribution of our work is direct visualization of the cartilage phenotype defining predictive ability as our technique is generative. We observe early biochemical patterns of fissuring in cartilage that define future onset of OA. In the future, coupling presymptomatic OA detection with emergent clinical therapies could modify the outcome of a disease that costs the United States healthcare system $16.5 billion annually. Furthermore, our technique is broadly applicable to earlier image-based detection of many diseases currently diagnosed at advanced stages today.

Machine intelligence in healthcare-perspectives on trustworthiness, explainability, usability, and transparency.

Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.

Discovery and visualization of structural biomarkers from MRI using transport-based morphometry.

Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI). Unfortunately, current computer-assisted approaches that examine pre-specified features, whether anatomically-defined (i.e. thalamic volume, cortical thickness) or based on pixelwise comparison (i.e. deformation-based methods), are prone to missing a vast array of physical changes that are not well-encapsulated by these metrics. In this paper, we have developed a technique for automated pattern analysis that can fully determine the relationship between brain structure and observable phenotype without requiring any a priori features. Our technique, called transport-based morphometry (TBM), is an image transformation that maps brain images losslessly to a domain where they become much more separable. The new approach is validated on structural brain images of healthy older adult subjects where even linear models for discrimination, regression, and blind source separation enable TBM to independently discover the characteristic changes of aging and highlight potential mechanisms by which aerobic fitness may mediate brain health later in life. TBM is a generative approach that can provide visualization of physically meaningful shifts in tissue distribution through inverse transformation. The proposed framework is a powerful technique that can potentially elucidate genotype-structural-behavioral associations in myriad diseases.

The influence of surgical correction on white matter microstructural integrity in rabbits with familial coronal suture craniosynostosis.

OBJECT Craniosynostosis is a condition in which one or more of the calvarial sutures fuses prematurely. In addition to the cosmetic ramifications attributable to premature suture fusion, aberrations in neurophysiological parameters are seen, which may result in more significant damage. This work examines the microstructural integrity of white matter, using diffusion tensor imaging (DTI) in a homogeneous strain of rabbits with simple, familial coronal suture synostosis before and after surgical correction. METHODS After diagnosis, rabbits were assigned to different groups: wild-type (WT), rabbits with early-onset complete fusion of the coronal suture (BC), and rabbits that had undergone surgical correction with suturectomy (BC-SU) at 10 days of age. Fixed rabbit heads were imaged at 12, 25, or 42 days of life using a 4.7-T, 40-cm bore Avance scanner with a 7.2-cm radiofrequency coil. For DTI, a 3D spin echo sequence was used with a diffusion gradient (b = 2000 sec/mm(2)) applied in 6 directions. RESULTS As age increased from 12 to 42 days, the DTI differences between WT and BC groups became more pronounced (p < 0.05, 1-way ANOVA), especially in the corpus callosum, cingulum, and fimbriae. Suturectomy resulted in rabbits with no significant differences compared with WT animals, as assessed by DTI of white matter tracts. Also, it was possible to predict to which group an animal belonged (WT, BC, and BC-SU) with high accuracy based on imaging data alone using a linear support vector machine classifier. The ability to predict to which group the animal belonged improved as the age of the animal increased (71% accurate at 12 days and 100% accurate at 42 days). CONCLUSIONS Craniosynostosis results in characteristic changes of major white matter tracts, with differences becoming more apparent as the age of the rabbits increases. Early suturectomy (at 10 days of life) appears to mitigate these differences.

Rapid improvement in post-infectious gastroparesis symptoms with mirtazapine.

We report the case of a 34-year-old woman with severe post-infectious gastroparesis who was transferred from an outside medical facility for a second opinion regarding management. This patient had no prior history of gastrointestinal symptoms. However, in the aftermath of a viral illness, she developed two months of intractable nausea, vomiting, and oral intake intolerance that resulted in numerous hospitalizations for dehydration and electrolyte disturbances. A solid-phase gastric emptying scan had confirmed delayed emptying, confirming gastroparesis. Unfortunately, conventional pro-kinetic agents and numerous anti-emetic drugs provided little or no relief of the patient's symptoms. At our institution, the patient experienced a cessation of vomiting, reported a significant reduction in nausea, and tolerated oral intake shortly after taking mirtazapine. Based on mirtazapine's primary action as a serotonin (5-HT) 1a receptor agonist, we infer that this receptor system mediated the clinical improvement through a combination of peripheral and central neural mechanisms. This report highlights the potential utility of 5-HT1a agonists in the management of nausea and vomiting. We conclude that mirtazapine may be effective in treating symptoms associated with non-diabetic gastroparesis that are refractory to conventional therapies.

Angioglioma misdiagnosed as encephalomalacia on magnetic resonance imaging for over a decade.

We report a case of intractable seizures secondary to an angioglioma that was misdiagnosed as post-traumatic encephalomalacia for over a decade, with a discussion of the radiological findings and a review of the literature.

Assessment of lung volume collapsibility in chronic obstructive lung disease patients using CT.

OBJECTIVE: To investigate the collapsibility of the lung and individual lobes in patients with COPD during inspiration/expiration and assess the association of whole lung and lobar volume changes with pulmonary function tests (PFTs) and disease severity. METHODS: PFT measures used were RV/TLC%, FEV1% predicted, FVC, FEV1/FVC%, DLco% predicted and GOLD category. A total of 360 paired inspiratory and expiratory CT examinations acquired in 180 subjects were analysed. Automated computerised algorithms were used to compute individual lobe and total lung volumes. Lung volume collapsibility was assessed quantitatively using the simple difference between CT computed inspiration (I) and expiration (E) volumes (I-E), and a relative measure of volume changes, (I-E)/I. RESULTS: Mean absolute collapsibility (I-E) decreased in all lung lobes with increasing disease severity defined by GOLD classification. Relative collapsibility (I-E)/I showed a similar trend. Upper lobes had lower volume collapsibility across all GOLD categories and lower lobes collectively had the largest volume collapsibility. Whole lung and left lower lobe collapsibility measures tended to have the highest correlations with PFT measures. Collapsibility of lung lobes and whole lung was also negatively correlated with the degree of air trapping between expiration and inspiration, as measured by mean lung density. All measured associations were statistically significant (P < 0.01). CONCLUSION: Severity of COPD appears associated with increased collapsibility in the upper lobes, but change (decline) in collapsibility is faster in the lower lobes. KEY POINTS: • Inspiratory and expiratory computed tomography allows assessment of lung collapsibility • Lobe volume collapsibility is significantly correlated with measures of lung function. • As COPD severity increases, collapsibility of individual lung lobes decreases. • Upper lobes exhibit more severe disease, while lower lobes decline faster.

Optimal threshold in CT quantification of emphysema.

OBJECTIVES: To determine the optimal threshold by quantitatively assessing the extent of emphysema at the level of the entire lung and at the level of individual lobes using a large, diverse dataset of computed tomography (CT) examinations. METHODS: This study comprises 573 chest CT examinations acquired from subjects with different levels of airway obstruction (222 none, 83 mild, 141 moderate, 63 severe and 64 very severe). The extent of emphysema was quantified using the percentage of the low attenuation area (LAA%) divided by the total lung or lobe volume(s). The correlations between the extent of emphysema, and pulmonary functions and the five-category classification were assessed using Pearson and Spearman's correlation coefficients, respectively. When quantifying emphysema using a density mask, a wide range of thresholds from -850 to -1,000 HU were used. RESULTS: The highest correlations of LAA% with the five-category classification and PFT measures ranged from -925 to -965 HU for each individual lobe and the entire lung. However, the differences between the highest correlations and those obtained at -950 HU are relatively small. CONCLUSION: Although there are variations in the optimal cut-off thresholds for individual lobes, the single threshold of -950 HU is still an acceptable threshold for density-based emphysema quantification.