The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Identifying healthy individuals with Alzheimer neuroimaging phenotypes in the UK Biobank

Identifying prediagnostic neurodegenerative disease is a critical issue in neurodegenerative disease research, and Alzheimer's disease (AD) in particular, to identify populations suitable for preventive and early disease modifying trials. Evidence from genetic studies suggest the neurodegeneration of Alzheimer's disease measured by brain atrophy starts many years before diagnosis, but it is unclear whether these changes can be detected in sporadic disease. To address this challenge we train a Bayesian machine learning neural network model to generate a neuroimaging phenotype and AD-score representing the probability of AD using structural MRI data in the Alzheimer's Disease Neuroimaging Cohort (cut-off 0.5, AUC 0.92, PPV 0.90, NPV 0.93). We go on to validate the model in an independent real world dataset of the National Alzheimer's Coordinating Centre (AUC 0.74, PPV 0.65, NPV 0.80), and demonstrate correlation of the AD-score with cognitive scores in those with an AD-score above 0.5. We then apply the model to a healthy population in the UK Biobank study to identify a cohort at risk for Alzheimer's disease. This cohort have a cognitive profile in keeping with Alzheimer's disease, with strong evidence for poorer fluid intelligence, and with some evidence of poorer performance on tests of numeric memory, reaction time, working memory and prospective memory. We found some evidence in the AD-score positive cohort for modifiable risk factors of hypertension and smoking. This approach demonstrates the feasibility of using AI methods to identify a potentially prediagnostic population at high risk for developing sporadic Alzheimer's disease.

Correcting B0 inhomogeneity-induced distortions in whole-body diffusion MRI of bone

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p < 10–20). On average (± SD), participants’ largest error reduction was 11.8 mm (± 3.6). Mean (95% CI) displacement of bone lesions was 6.0 mm (95% CI 5.0–7.2); maximum displacement was 17.1 mm. Corrected diffusion images were more similar to structural MRI, as evidenced by consistent increases in mutual information (Wilcoxon signed-rank p < 10–12). These findings support the use of distortion correction techniques to improve localization of bone on DWI.

Unsupervised high-frequency smartphone-based cognitive assessments are reliable, valid, and feasible in older adults at risk for Alzheimer disease.

Smartphones have the potential for capturing subtle changes in cognition that characterize preclinical Alzheimer disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on principles from ecological momentary assessment (EMA) and administers brief tests of associative memory, processing speed, and working memory up to 4 times per day over 7 consecutive days. ARC was designed to be administered unsupervised using participants’ personal devices in their everyday environments. We evaluated the reliability and validity of ARC in a sample of 268 cognitively normal older adults (ages 65-97) and 22 individuals with very mild dementia (ages 61-88). Participants completed at least one 7-day cycle of ARC testing and conventional cognitive assessments; most also completed cerebrospinal fluid, amyloid and tau PET, and structural MRI studies. First, results indicated that ARC tasks were reliable as between person reliability across the 7-day cycle and test-retest reliabilities at 6-month and 1-year follow-ups all exceeded 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures (r = 0.53 between composite scores). Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, the intensive 7-day cycle indicated that ARC was feasible (86.50% approached chose to enroll), well tolerated (80.42% adherence, 4.83% dropout), and was rated favorably by older adult participants. Overall, the results suggest that ARC is reliable and valid and represents a feasible tool for assessing cognitive changes associated with the earliest stages of AD.

Transcriptomic Signatures Associated With Gray Matter Volume Changes in Patients With Functional Constipation

Functional constipation, which belongs to the functional gastrointestinal disorder (FGID), is a common disease and significantly impacts daily life. FGID patients have been progressively proven with functional and structural alterations in various brain regions, but whether and how functional constipation affects the brain gray matter volume (GMV) remains unclear; besides, which genes are associated with the GMV changes in functional constipation is largely unknown. On account of the structural MRI image from the 30 functional constipation patients and 30 healthy controls (HCs), GMV analysis showed that functional constipation patients had significantly decreased GMV in the right orbital prefrontal cortex (OFC), left precentral gyrus (PreG), and bilateral thalamus (THA). Correlation analysis showed that the self-rating depressive scale, patient assessment of constipation quality of life (PAC-QOL), and Wexner constipation scores were negatively correlated with GMV of the OFC and negative correlations between PAC-QOL score and GMV of the bilateral THA. Based on the Allen Human Brain Atlas, a cross-sample spatial correlation was conducted and found that 18 genes’ expression values showed robust correlations with GMV changes in functional constipation patients. These outcomes highlight our recognition of the transcriptional features related to GMV changes in functional constipation and could be regarded as candidates to detect biological mechanisms of abnormality in functional constipation patients.

Comparison Analysis of GLCM and PCA on Parkinson's Disease Using Structural MRI

Parkinson disease (PD) is a neurological disorder where the dopaminergic neurons experience deterioration. It is caused from the death of the dopamine neurons present in the substantia nigra i.e., the mid part of the brain. The symptoms of this disease emerge slowly, the onset of the earlier stages shows some non-motor symptoms and with time motor symptoms can also be gauged. Parkinson is incurable but can be treated to improve the condition of the sufferer. No definite method for diagnosing PD has been concluded yet. However, researchers have suggested their own framework out of which MRI gave better results and is also a non-invasive method. In this study, the MRI images are used for extracting the features. For performing the feature extraction techniques Gray Level Co-occurrence Matrix and Principal Component Analysis are performed and are analysed. Feature extraction reduces the dimensionality of data. It aims to reduce the feature of data by generating new features from the original one.

Mesocorticolimbic system abnormalities in chronic cluster headache patients: a neural signature?

BACKGROUND: Converging evidence suggests that anatomical and functional mesocorticolimbic abnormalities support the chronicization of pain disorders. METHODS: We mapped structural and functional alterations of the mesocorticolimbic system in a sample of chronic cluster headache (cCH) patients (n = 28) in comparison to age and sex-matched healthy individuals (n=28) employing structural MRI and resting-state functional MRI (rs-fMRI). RESULTS: Univariate logistic regression models showed that several of the examined structures/areas (i.e., the bilateral nucleus accumbens, ventral diencephalon, hippocampus, and frontal pole, and the right amygdala) differentiated cCH patients from healthy individuals (p<0.05, uncorrected). Specifically, all the significant structures/areas had increased volumes in cCH patients compared to healthy individuals. The examination of the groups suffering from left and right-sided cranial attacks showed a lateralization effect: ipsilateral to the pain ventral diencephalic regions and contralateral to the pain nucleus accumbens discriminated cCH patients from healthy individuals. The rs-fMRI data analyses showed that cCH patients compared to CTRL individuals present robust reduced functional connectivity in the right frontal pole-right amygdala pathway (p<0.05, FDR-corrected). CONCLUSION: Our results showed that cCH patients present anatomical and functional maladaptation of the mesocorticolimbic system, with functional data indicating a possible prefrontal areas' failure to modulate the mesolimbic structures. These results were opposite to what we hypothesized based on the previous literature on chronic pain conditions. Future studies should assess whether the observed mesocorticolimbic abnormalities are due to the neuroprotective effects of the assumed medications, or to the frequent comorbidity of CH with neuropsychiatric disorders or if they are a genuine neural signature of CH and/or cCH condition.

Plasma EFEMP1 Is Associated with Brain Aging and Dementia: The Framingham Heart Study

Background: Epidermal growth factor containing fibulin extracellular matrix protein-1 (EFEMP1) has been associated with increased white matter hyperintensities (WMH) burden and disorders of premature aging and may have a shared pathophysiological role in the development of WMH and dementia. Objective: To determine the association between plasma EFEMP1 levels and MRI markers of vascular brain injury and incident all-cause and Alzheimer’s disease (AD) dementia. Methods: We measured plasma EFEMP1 levels in 1597 [53% women, mean age 68.7 (SD 5.7) years] dementia-free Framingham Offspring cohort participants between 1998–2001 and subsequently followed them for incident dementia. Secondary outcomes included stroke, structural MRI brain measures and neurocognitive test performance. Results: During a median 11.8 [Q1, Q3 : 7.1, 13.3] year follow-up, 131 participants developed dementia. The highest quintile of plasma EFEMP1, compared to the bottom four quintiles, was associated with an increased risk of time to incident all-cause dementia (HR 1.77, 95% CI 1.18–2.64) and AD dementia (HR 1.76, 95% CI 1.11–2.81) but not with markers of vascular brain injury (WMH, covert brain infarcts or stroke). Higher circulating EFEMP1 concentrations were also cross-sectionally associated with lower total brain (β±SE, –0.28±0.11, p = 0.01) and hippocampal volumes (–0.006±0.003, p = 0.04) and impaired abstract reasoning (Similarities test, –0.18±0.08, p = 0.018 per standard deviation increment in EFEMP1). Conclusion: Elevated circulating EFEMP1 is associated with an increased risk of all-cause and AD dementia, smaller hippocampal and total brain volumes, and poorer cognitive performance. EFEMP1 may play an important biological role in the development of AD dementia. Further studies to validate these findings are warranted.

Depressive symptoms following traumatic brain injury are associated with resting-state functional connectivity

Background To determine whether depressive symptoms in traumatic brain injury (TBI) patients were associated with altered resting-state functional connectivity (rs-fc) or voxel-based morphology in brain regions involved in emotional regulation and associated with depression. Methods In the present study, we examined 79 patients (57 males; age range = 17–70 years, M ± s.d. = 38 ± 16.13; BDI-II, M ± s.d. = 9.84 ± 8.67) with TBI. We used structural MRI and resting-state fMRI to examine whether there was a relationship between depression, as measured with the Beck Depression Inventory (BDI-II), and the voxel-based morphology or functional connectivity in regions previously identified as involved in emotional regulation in patients following TBI. Patients were at least 4 months post-TBI (M ± s.d. = 15.13 ± 11.67 months) and the severity of the injury included mild to severe cases [Glasgow Coma Scale (GCS), M ± s.d. = 6.87 ± 3.31]. Results Our results showed that BDI-II scores were unrelated to voxel-based morphology in the examined regions. We found a positive association between depression scores and rs-fc between limbic regions and cognitive control regions. Conversely, there was a negative association between depression scores and rs-fc between limbic and frontal regions involved in emotion regulation. Conclusion These findings lead to a better understanding of the exact mechanisms that contribute to depression following TBI and better inform treatment decisions.

Chaos Analysis of the Brain Topology in Grey Matter Images for the Recognition of Psychosis

Structural MRI studies in first-episode psychosis (FEP) and in clinical high risk (CHR) patients have consistently shown volumetric abnormalities in frontal, temporal, and cingulate cortex areas. The aim of the present study was to employ chaos analysis in the identification of people with psychosis. Structural MRI were acquired from 73 CHR, 77 FEP and 44 healthy controls (HC). Chaos analysis of the grey matter distribution was performed: first, the distances of each voxel from the center of mass in the grey matter image was calculated. Next, the distances multiplied by the voxel intensity was represented as a spatial-series, which then was analyzed by extracting the Largest-Lyapunov-Exponent (lambda). The lambda brain map depicts how the grey matter topology changes. The classification of a subject’s clinical status was finally predicted by a) comparing the lambda brain maps, which resulted in statistically significant differences in FEP and CHR compared to HC; and b) matching the lambda series with the Morlet wavelet, which resulted in 100% accuracy in distinguishing between FEP and CHR. The proposed framework using spatial-series extraction enhances the classification decision for FEP, CHR and HC subjects, verifies diagnosis-relevant features and may potentially contribute to the identification of structural biomarkers for psychosis.