A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes

Large databases of high-resolution structural MR images are being assembled to quantitatively examine the relationships between brain anatomy, disease progression, treatment regimens, and genetic influences upon brain structure. Quantifying brain structures in such large databases cannot be practically accomplished by expert neuroanatomists using hand-tracing. Rather, this research will depend upon automated methods that reliably and accurately segment and quantify dozens of brain regions. At present, there is little guidance available to help clinical research groups in choosing such tools. Thus, our goal was to compare the performance of two popular and fully automated tools, FSL/FIRST and FreeSurfer, to expert hand tracing in the measurement of the hippocampus and amygdala. Volumes derived from each automated measurement were compared to hand tracing for percent volume overlap, percent volume difference, across-sample correlation, and 3-D group-level shape analysis. In addition, sample size estimates for conducting between-group studies were computed for a range of effect sizes. Compared to hand tracing, hippocampal measurements with FreeSurfer exhibited greater volume overlap, smaller volume difference, and higher correlation than FIRST, and sample size estimates with FreeSurfer were closer to hand tracing. Amygdala measurement with FreeSurfer was also more highly correlated to hand tracing than FIRST, but exhibited a greater volume difference than FIRST. Both techniques had comparable volume overlap and similar sample size estimates. Compared to hand tracing, a 3-D shape analysis of the hippocampus showed FreeSurfer was more accurate than FIRST, particularly in the head and tail. However, FIRST more accurately represented the amygdala shape than FreeSurfer, which inflated its anterior and posterior surfaces

Screen usage relates to neuroanatomy underlying reward processing.

Today’s world is inundated with technology and our use of screens. It is possible that screen usage might affect the structural development of brain systems underlying motivation, reward, and addiction. Two hundred and thirty-two 10-year-old individuals’ structural MRI and behavioral data from a publicly accessible database were analyzed to find relations between the cortical and subcortical regions of the reward circuits of the brain and the usage of social media, texting, television, YouTube and other video applications, video games, and video chat applications. Both cortical and subcortical results yielded significant relationships with variables of screen time usage. Most significantly, subcortical brain regions known to be involved in the reward system were structurally affected by duration of screen usage. These results implicate brain changes beyond the explicit structural changes in response to the ubiquitous use of screens within our society and warrant the further study of how this affects our reward system and attention

Correlation between peripheral levels of brain-derived neurotrophic factor and hippocampal volume in children and adolescents with bipolar disorder

Pediatric bipolar disorder (PBD) is a serious mental disorder that affects the development and emotional growth of affected patients. The brain derived neurotrophic factor (BDNF) is recognized as one of the possible markers of the framework and its evolution. Abnormalities in BDNF signaling in the hippocampus could explain the cognitive decline seen in patients with TB. Our aim with this study was to evaluate possible changes in hippocampal volume in children and adolescents with BD and associate them to serum BDNF. Subjects included 30 patients aged seven to seventeen years from the ProCAB (Program for Children and Adolescents with Bipolar Disorder). We observed mean right and left hippocampal volumes of 41910.55 and 41747.96 mm3 , respectively. No statistically significant correlations between peripheral BDNF levels and hippocampal volumes were found. We believe that the lack of correlation observed in this study is due to the short time of evolution of BD in children and adolescents. Besides studies with larger sample sizes to confirm the present findings and longitudinal assessments, addressing brain development versus a control group and including drug-naive patients in different mood states may help clarify the role of BDNF in the brain changes consequent upon BD

Whole brain resting state functional connectivity abnormalities in schizophrenia

Background Schizophrenia has been associated with disturbances in brain connectivity; however the exact nature of these disturbances is not fully understood. Measuring temporal correlations between the functional MRI time courses of spatially disparate brain regions obtained during rest has recently emerged as a popular paradigm for estimating brain connectivity. Previous resting state studies in schizophrenia explored connections related to particular clinical or cognitive symptoms (connectivity within a-priori selected networks), or connections restricted to functional networks obtained from resting state analysis. Relatively little has been done to understand global brain connectivity in schizophrenia. Methods Eighteen patients with chronic schizophrenia and 18 healthy volunteers underwent a resting state fMRI scan on a 3 T magnet. Whole brain temporal correlations have been estimated using resting-state fMRI data and free surfer cortical parcellations. A multivariate classification method was then used to indentify brain connections that distinguish schizophrenia patients from healthy controls. Results The classification procedure achieved a prediction accuracy of 75% in differentiating between groups on the basis of their functional connectivity. Relative to controls, schizophrenia patients exhibited co-existing patterns of increased connectivity between parietal and frontal regions, and decreased connectivity between parietal and temporal regions, and between the temporal cortices bilaterally. The decreased parieto-temporal connectivity was associated with the severity of patients' positive symptoms, while increased fronto-parietal connectivity was associated with patients' negative and general symptoms. Discussion Our analysis revealed two co-existing patterns of functional connectivity abnormalities in schizophrenia, each related to different clinical profiles. Such results provide further evidence that abnormalities in brain connectivity, characteristic of schizophrenia, are directly related to the clinical features of the disorder.National Alliance for Medical Image Computing (U.S.) (Grant U54 EB005149)National Institutes of Health (U.S.) (R01 M074794)Medical Research Council of Australia (Overseas-Based Biomedical Traning Fellowship 520627

Robust Machine Learning-Based Correction on Automatic Segmentation of the Cerebellum and Brainstem.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by <0.01. These results suggest that the combination of automated segmentation and corrective learning provides a valuable method for accurate and efficient segmentation of the cerebellum and brainstem, particularly in large-scale neuroimaging studies, and potentially for segmenting other neural regions as well

MAO A VNTR polymorphism and amygdala volume in healthy subjects

The X-linked Monoamine Oxidase A (MAO A) gene presents a well known functional polymorphism consisting of a variable number of tandem repeats (VNTR) (long and short variants) previously associated with altered neural function of the amygdala. Using automatic subcortical segmentation (Freesurfer), we investigated whether amygdala volume could be influenced by this genotype. We studied 109 healthy subjects (age range 18-80 years; 59 male and 50 female), 74 carrying the MAO A High-activity allele and 35 the MAO A Low-activity allele. No significant effect of the MAO A polymorphism or interaction effect between polymorphism × gender was found on amygdalar volume. Thus, our findings suggest that the reported impact of the MAO A polymorphism on amygdala function is not coupled with consistent volumetric changes in healthy subjects. Future studies are needed to investigate whether the association between volume of the amygdala and the MAO A VNTR polymorphism is influenced by social/psychological variables, such as impulsivity, trauma history and cigarette smoking behaviour, not taken into account in this work