Corpus callosum shape changes in early Alzheimer's disease: an MRI study using the OASIS brain database

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism

BACKGROUND

Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood.

METHODS

Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors.

RESULTS

Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels.

CONCLUSIONS

This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach

Successively predicting whether mild cognitive impairment patients will progress to Alzheimer's disease is of significant clinical relevance. This ability may provide information that can be leveraged by emerging intervention approaches and thus mitigate some of the negative effects of the disease. Neuroimaging biomarkers have gained some attention in recent years and may be useful in predicting the conversion of mild cognitive impairment to Alzheimer's disease. We implemented a novel multi-modal approach that allowed us to evaluate the potential of different imaging modalities, both alone and in different degrees of combinations, in predicting the conversion to Alzheimer's disease of mild cognitive impairment patients. We applied this approach to the imaging data from the Alzheimer's Disease Neuroimaging Initiative that is a multi-modal imaging dataset comprised of MRI, Fluorodeoxyglucose PET, Florbetapir PET and diffusion tensor imaging. We included a total of 480 mild cognitive impairment patients that were split into two groups: converted and stable. Imaging data were segmented into atlas-based regions of interest, from which relevant features were extracted for the different imaging modalities and used to construct machine-learning models to classify mild cognitive impairment patients into converted or stable, using each of the different imaging modalities independently. The models were then combined, using a simple weight fusion ensemble strategy, to evaluate the complementarity of different imaging modalities and their contribution to the prediction accuracy of the models. The single-modality findings revealed that the model, utilizing features extracted from Florbetapir PET, demonstrated the highest performance with a balanced accuracy of 83.51%. Concerning multi-modality models, not all combinations enhanced mild cognitive impairment conversion prediction. Notably, the combination of MRI with Fluorodeoxyglucose PET emerged as the most promising, exhibiting an overall improvement in predictive capabilities, achieving a balanced accuracy of 78.43%. This indicates synergy and complementarity between the two imaging modalities in predicting mild cognitive impairment conversion. These findings suggest that β-amyloid accumulation provides robust predictive capabilities, while the combination of multiple imaging modalities has the potential to surpass certain single-modality approaches. Exploring modality-specific biomarkers, we identified the brainstem as a sensitive biomarker for both MRI and Fluorodeoxyglucose PET modalities, implicating its involvement in early Alzheimer's pathology. Notably, the corpus callosum and adjacent cortical regions emerged as potential biomarkers, warranting further study into their role in the early stages of Alzheimer's disease.

Structural irregularities in MR corpus callosal images and their association with cerebrospinal fluid biomarkers in Mild Cognitive Impairments

In this study, irregularity measures from MR images of corpus callosal brain structures in healthy and Mild Cognitive Impairment (MCI) conditions are extracted and their association with Cerebrospinal Fluid (CSF) biomarkers are analyzed. For this, MR images of healthy controls, Early MCI (EMCI) and Late MCI (LMCI) subjects are considered from a public database. The considered images are preprocessed and corpus callosal structure is segmented. Structural irregularity measures are extracted from the segmented regions using Fourier analysis. Statistical tests are performed to identify the significant features which can characterize the MCI stages. Association of these measures with CSF amyloid beta and tau concentrations are further investigated. Results demonstrate that Fourier spectral analysis is able to characterize the non-periodic variations in the corpus callosal structures of healthy, EMCI and LMCI MR images. The callosal irregularity measures increase as the disease progresses from healthy to LMCI. Phosphorylated tau concentrations in CSF demonstrate a positive correlation with irregularity measures across the diagnostic groups. Significant association of callosal measures and amyloid beta levels are found to be absent in MCI stages. As corpus callosal structural irregularities due to early MCI condition and their association with CSF markers remain uncharacterized in the literature, this study seems to be clinically significant for the timely intervention of pre-symptomatic MCI stages.

Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry

The Corpus Callosum (CC) is an important structure that includes the majority of fibers connecting the two brain hemispheres. Several neurodegenerative diseases may alter CC size and morphology leading to its atrophy and malfunction which may play a role in the pathological manifestations found in these diseases. The purpose of the current study is to determine any possible changes in CC size in patients suffering from Alzheimer’s disease. The Study also investigated the effect of acetylcholinesterase inhibitors (AChEIs) on the size of CC and its association with improvement in the Alzheimer disease severity scores. Midsagittal size of CC were recorded prospectively from 439 routine T1-weighted MRI brain images in normal individuals. The internal skull surface was measured to calculate CC/ internal skull surface ratio. Two groups of patients were studied: 300 (150 male / 150 female) were healthy subjects and 130 (55 males / 75 females) had Alzheimer disease. Out of the 130 Alzheimer disease pateints, 70 patients were treated with Donepezil or Rivastigmine or both. The size of the CC was measured based on T1-weighted MRI images after the treatment to investigate any possible improvement in CC size. The mean surface area of CC in controls was 6.53±1.105 cm². There was no significant difference between males and females (P < 0.627), and CC/ internal skull surface ratio was 4.41±0.77%. Patients with mild or severe Alzheimer disease showed a significant reduction in CC size compared to healthy controls. Treating mild Alzheimer patients with either Donepezil or Rivastigmine exerts a comparable therapeutic effect in improving the CC size. There was more improvement in the size of CC in patients with severe Alzheimer disease by using combined therapy of Donepezil and Rivastigmine than using single a medication. we measured the mean size of the various portions of the corpus callosum in normal individuals and Alzheimer patients before and after taking Donepezil and Rivastigmine. Alzheimer patients have pronounced reduction in CC which is corrected after taking Donepezil and Rivastigmine leading to remarkable improvement in Alzheimer disease severity scores.

Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhood: a longitudinal MRI analysis

The human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region-dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.

Alteration of the corpus callosum in patients with Alzheimer's disease: Deep learning-based assessment

BACKGROUND

Several studies have reported changes in the corpus callosum (CC) in Alzheimer's disease. However, the involved region differed according to the study population and study group. Using deep learning technology, we ensured accurate analysis of the CC in Alzheimer's disease.

METHODS

We used the Open Access Series of Imaging Studies (OASIS) dataset to investigate changes in the CC. The individuals were divided into three groups using the Clinical Dementia Rating (CDR); 94 normal controls (NC) were not demented (NC group, CDR = 0), 56 individuals had very mild dementia (VMD group, CDR = 0.5), and 17 individuals were defined as having mild and moderate dementia (MD group, CDR = 1 or 2). Deep learning technology using a convolutional neural network organized in a U-net architecture was used to segment the CC in the midsagittal plane. Total CC length and regional magnetic resonance imaging (MRI) measurements of the CC were made.

RESULTS

The total CC length was negatively associated with cognitive function. (beta = -0.139, p = 0.022) Among MRI measurements of the CC, the height of the anterior third (beta = 0.038, p <0.0001) and width of the body (beta = 0.077, p = 0.001) and the height (beta = 0.065, p = 0.001) and area of the splenium (beta = 0.059, p = 0.027) were associated with cognitive function. To distinguish MD from NC and VMD, the receiver operating characteristic analyses of these MRI measurements showed areas under the curves of 0.65-0.74. (total CC length = 0.705, height of the anterior third = 0.735, width of the body = 0.714, height of the splenium = 0.703, area of the splenium = 0.649).

CONCLUSIONS

Among MRI measurements, total CC length, the height of the anterior third and width of the body, and the height and area of the splenium were associated with cognitive decline. They had fair diagnostic validity in distinguishing MD from NC and VMD.

Adult Neural Stem Cell Migration Is Impaired in a Mouse Model of Alzheimer's Disease

Neurogenesis in the adult brain takes place in two neurogenic niches: the ventricular-subventricular zone (V-SVZ) and the subgranular zone. After differentiation, neural precursor cells (neuroblasts) have to move to an adequate position, a process known as neuronal migration. Some studies show that in Alzheimer’s disease, the adult neurogenesis is impaired. Our main aim was to investigate some proteins involved both in the physiopathology of Alzheimer’s disease and in the neuronal migration process using the APP/PS1 Alzheimer’s mouse model. Progenitor migrating cells are accumulated in the V-SVZ of the APP/PS1 mice. Furthermore, we find an increase of Cdh1 levels and a decrease of Cdk5/p35 and cyclin B1, indicating that these cells have an alteration of the cell cycle, which triggers a senescence state. We find less cells in the rostral migratory stream and less mature neurons in the olfactory bulbs from APP/PS1 mice, leading to an impaired odour discriminatory ability compared with WT mice. Alzheimer’s disease mice present a deficit in cell migration from V-SVZ due to a senescent phenotype. Therefore, these results can contribute to a new approach of Alzheimer’s based on senolytic compounds or pro-neurogenic factors.

Proteogenomics Reveals Orthologous Alternatively Spliced Proteoforms in the Same Human and Mouse Brain Regions with Differential Abundance in an Alzheimer's Disease Mouse Model

Alternative splicing (AS) may increase the number of proteoforms produced by a gene. Alzheimer's disease (AD) is a neurodegenerative disease with well-characterized AS proteoforms. In this study, we used a proteogenomics strategy to build a customized protein sequence database and identify orthologous AS proteoforms between humans and mice on publicly available shotgun proteomics (MS/MS) data of the corpus callosum (CC) and olfactory bulb (OB). Identical proteotypic peptides of six orthologous AS proteoforms were found in both species: PKM1 (gene PKM/Pkm), STXBP1a (gene STXBP1/Stxbp1), Isoform 3 (gene HNRNPK/Hnrnpk), LCRMP-1 (gene CRMP1/Crmp1), SP3 (gene CADM1/Cadm1), and PKCβII (gene PRKCB/Prkcb). These AS variants were also detected at the transcript level by publicly available RNA-Seq data and experimentally validated by RT-qPCR. Additionally, PKM1 and STXBP1a were detected at higher abundances in a publicly available MS/MS dataset of the AD mouse model APP/PS1 than its wild type. These data corroborate other reports, which suggest that PKM1 and STXBP1a AS proteoforms might play a role in amyloid-like aggregate formation. To the best of our knowledge, this report is the first to describe PKM1 and STXBP1a overexpression in the OB of an AD mouse model. We hope that our strategy may be of use in future human neurodegenerative studies using mouse models.

Morphological alterations of the corpus callosum in antipsychotic-naive first-episode schizophrenia before and 1-year after treatment

OBJECTIVE

The corpus callosum (CC) is known to be altered in patients with schizophrenia. However, its morphologic characteristics are less well studied in treatment-naive first-episode schizophrenia patients, as is the effect of antipsychotic treatment on this structure.

METHODS

T-1 weighted MRI scans were obtained from 160 antipsychotic-naïve first-episode schizophrenia patients (AN-FES) and 155 healthy controls (HCs) before treatment initiation. Among the patients, forty-four were available for follow-up studies after one year of antipsychotic treatment, and were divided into good-outcome (n = 31) and poor-outcome subgroups (n = 13) based on whether there was a 50% reduction in Positive and Negative Symptom Scale (PANSS) total scores from baseline. A computer algorithm was applied to automatically identify the mid-sagittal plane (MSP) and obtain morphological measurement parameters of the CC.

RESULTS

Compared with HCs, AN-FES patients showed a significant reduction of thickness in the posterior midbody of the CC. This deficit was correlated with severity of negative symptoms. After one year of antipsychotic treatment, there was no significant change in CC morphological measurements in schizophrenia patients, nor was there a significant difference of CC morphological measurements between good-outcome and poor-outcome subgroups at baseline or at 1-year follow-up.

CONCLUSION

Thickness of the posterior midbody of the CC is reduced in the early course of schizophrenia before treatment. This alteration was not affected by antipsychotic treatment and was unrelated to treatment outcome at 1-year.

Free-water diffusion tensor imaging improves the accuracy and sensitivity of white matter analysis in Alzheimer's disease

Magnetic resonance imaging (MRI) based diffusion tensor imaging (DTI) can assess white matter (WM) integrity through several metrics, such as fractional anisotropy (FA), axial/radial diffusivities (AxD/RD), and mode of anisotropy (MA). Standard DTI is susceptible to the effects of extracellular free water (FW), which can be removed using an advanced free-water DTI (FW-DTI) model. The purpose of this study was to compare standard and FW-DTI metrics in the context of Alzheimer’s disease (AD). Data were obtained from the Open Access Series of Imaging Studies (OASIS-3) database and included both healthy controls (HC) and mild-to-moderate AD. With both standard and FW-DTI, decreased FA was found in AD, mainly in the corpus callosum and fornix, consistent with neurodegenerative mechanisms. Widespread higher AxD and RD were observed with standard DTI; however, the FW index, indicative of AD-associated neurodegeneration, was significantly elevated in these regions in AD, highlighting the potential impact of free water contributions on standard DTI in neurodegenerative pathologies. Using FW-DTI, improved consistency was observed in FA, AxD, and RD, and the complementary FW index was higher in the AD group as expected. With both standard and FW-DTI, higher values of MA coupled with higher values of FA in AD were found in the anterior thalamic radiation and cortico-spinal tract, most likely arising from a loss of crossing fibers. In conclusion, FW-DTI better reflects the underlying pathology of AD and improves the accuracy of DTI metrics related to WM integrity in Alzheimer’s disease.

High-Dimensional Spatial Quantile Function-on-Scalar Regression

This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.

Deep Learning Corpus Callosum Segmentation as a Neurodegenerative Marker in Multiple Sclerosis

BACKGROUND AND PURPOSE

Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.

METHODS

In a prospective study of 553 MS patients with 704 acquisitions, 200 unique 2D T₂ -weighted MRI scans were delineated to develop, train, and validate DeepnCCA. Comparative FreeSurfer segmentations were obtained in 504 3D T₁ -weighted scans. Both FreeSurfer and DeepnCCA outputs were correlated with clinical disability. Using principal component analysis of the DeepnCCA output, the morphological changes were explored in relation to clinical disease burden.

RESULTS

DeepnCCA and manual segmentations had high similarity (Dice coefficients 98.1 ± .11%, 89.3 ± .76%, for intracranial and corpus callosum area, respectively through 10-fold cross-validation). DeepnCCA had numerically stronger correlations with cognitive and physical disability as compared to FreeSurfer: Expanded disability status scale (EDSS) ±6 months (r = -.22 P = .002; r = -.17, P = .013), future EDSS (r = -.26, P<.001; r = -.17, P = .012), and future symbol digit modalities test (r = .26, P = .001; r = .24, P = .003). The corpus callosum became thinner with increasing cognitive and physical disability. Increasing physical disability, additionally, significantly correlated with a more angled corpus callosum.

CONCLUSIONS

DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.

Brain MRI after critical care admission: A longitudinal imaging study

PURPOSE

To investigate the association between episodes of critical care hospitalizations and delirium with structural brain changes in older adults.

MATERIALS AND METHODS

We included Mayo Clinic Study of Aging participants ≥60 years old at the time of study enrollment (October 29, 2004, through September 11, 2017) with available brain MRI and 'amyloid' positron emission tomography (PET) scans. We tested the hypothesis that a) intensive care unit (ICU) admission is associated with greater cortical thinning and atrophy in entorhinal cortex, inferior temporal cortex, middle temporal cortex, and fusiform cortex (Alzheimer''s disease-signature regions); b) atrophy in hippocampus and corpus callosum; c) delirium accelerates these changes; and d) ICU admission is not associated with increased deposition of cortical amyloid.

RESULTS

ICU admission was associated with cortical thinning in temporal, frontal, and parietal cortices, and decreases in hippocampal/corpus callosum volumes, but not Alzheimer''s disease-signature regions. For hippocampal volume, and 10 of 14 cortical thickness measurements, the change following ICU admission was significantly more pronounced for those who experienced delirium. ICU admission was not associated with an increased amyloid burden.

CONCLUSIONS

Critical care hospitalization is associated with accelerated brain atrophy in selected brain regions, without increases in amyloid deposition, suggesting a pathogenesis based on neurodegeneration unrelated to Alzheimer''s pathway.

Regional segmentation strategy for DTI analysis of human corpus callosum indicates motor function deficit in mild cognitive impairment

BACKGROUND

The corpus callosum is the largest white matter tract in the human brain, involved in inter-hemispheric transfer and integration of lateralised visual, sensory-motor, language, and cognitive information. Microstructural alterations are implicated in ageing as well as various neurological conditions.

NEW METHOD

Cross-sectional diffusion-weighted images of 107 healthy adults were used to create a linear regression model of the ageing corpus callosum and its sub-regions to evaluate the impact of analysis by sub-region, and to test for deviations from healthy ageing parameters in 28 subjects with mild cognitive impairment (MCI). Alterations in diffusion properties including fractional anisotropy, mean, radial and axial diffusivities were investigated as a function of age.

RESULTS

Changes in DTI parameters showed age-dependent regional differences, likely arising from axonal diameter variation across cross-sectional regions of interest in the corpus callosum. Patterns suggestive of degeneration with healthy ageing were observed in all regions. Diffusion parameters in sub-regions projecting to pre-motor, primary, and supplementary motor areas of the brain differed for MCI versus healthy controls, and MCI subjects were more likely than healthy controls to experience a reduction in motor skills.

COMPARISON WITH EXISTING METHODS

Statistical analyses of the corpus callosum by five manually-defined sub-regions, instead of a single manually-defined region of interest, revealed region-specific changes in microstructure in healthy ageing and MCI, and accounted for clinically-evaluated differences in motor skills between cohorts.

CONCLUSION

This method will support future studies of corpus callosum, enabling identification and measurement of white matter changes that are undetectable with the single ROI approach.

Influence of multiple hypothesis testing on reproducibility in neuroimaging research: A simulation study and Python-based software

BACKGROUND

Reproducibility of research findings has been recently questioned in many fields of science, including psychology and neurosciences. One factor influencing reproducibility is the simultaneous testing of multiple hypotheses, which entails false positive findings unless the analyzed p-values are carefully corrected. While this multiple testing problem is well known and studied, it continues to be both a theoretical and practical problem.

NEW METHOD

Here we assess reproducibility in simulated experiments in the context of multiple testing. We consider methods that control either the family-wise error rate (FWER) or false discovery rate (FDR), including techniques based on random field theory (RFT), cluster-mass based permutation testing, and adaptive FDR. Several classical methods are also considered. The performance of these methods is investigated under two different models.

RESULTS

We found that permutation testing is the most powerful method among the considered approaches to multiple testing, and that grouping hypotheses based on prior knowledge can improve power. We also found that emphasizing primary and follow-up studies equally produced most reproducible outcomes.

COMPARISON WITH EXISTING METHOD(S)

We have extended the use of two-group and separate-classes models for analyzing reproducibility and provide a new open-source software "MultiPy" for multiple hypothesis testing.

CONCLUSIONS

Our simulations suggest that performing strict corrections for multiple testing is not sufficient to improve reproducibility of neuroimaging experiments. The methods are freely available as a Python toolkit "MultiPy" and we aim this study to help in improving statistical data analysis practices and to assist in conducting power and reproducibility analyses for new experiments.

Effect of Hearing Ability and Mild Behavioural Impairment on MoCA and Memory Index Scores

BACKGROUND

The life-course model of modifiable risk factors for dementia now recognizes managing hearing loss and addressing social isolation.

OBJECTIVE

To investigate the contribution and inter-relationship of hearing ability and behaviour change on cognitive ability.

METHODS

We present the preliminary findings from a prospective longitudinal study of 35 non-demented participants ages 60-93, recruited from community rehabilitation and acute-care programs of Geriatric Medicine, who underwent baseline hearing, behavioural, and cognitive testing.

RESULTS

After controlling for age and hearing impairment, the left ear Dichotic Digit Test (DDT) score accounted uniquely for 20% of the variance in MoCA Memory Index (p = .016 with β = .598). Mild Behavioural Impairment (MBI) was highly prevalent, with 80% of older adults reporting at least one MBI symptom. People with hearing impairment had greater global MBI burden than people with normal hearing, especially in the domains of apathy and impulse dyscontrol; however, greater severity of hearing impairment was not associated with a higher number of neuropsychiatric symptoms (NPS).

CONCLUSIONS

Low left DDT contributed to lower memory index and greater MBI burden is associated with hearing impairment. Our findings demonstrate the value of early non-invasive hearing and behavioural assessments as part of dementia risk assessment in older adults.

Non-fasting High-Density Lipoprotein Is Associated With White Matter Microstructure in Healthy Older Adults

A growing body of evidence indicates that biomarkers of cardiovascular risk may be related to cerebral health. However, little is known about the role that non-fasting lipoproteins play in assessing age-related declines in a cerebral biomarker sensitive to vascular compromise, white matter (WM) microstructure. High-density lipoprotein cholesterol (HDL-C) is atheroprotective and low-density lipoprotein cholesterol (LDL-C) is a major atherogenic lipoprotein. This study explored the relationships between non-fasting levels of cholesterol and WM microstructure in healthy older adults. A voxelwise and region of interest approach was used to determine the relationship between cholesterol and fractional anisotropy (FA). Participants included 87 older adults between the ages of 59 and 77 (mean age = 65.5 years, SD = 3.9). Results indicated that higher HDL-C was associated with higher FA in diffuse regions of the brain when controlling for age, sex, and body mass index (BMI). HDL-C was also positively associated with FA in the corpus callosum and fornix. No relationship was observed between LDL-C and FA. Findings suggest that a modifiable lifestyle variable associated with cardiovascular health may help to preserve cerebral WM.

A Novel Approach of Diffusion Tensor Visualization Based Neuro Fuzzy Classification System for Early Detection of Alzheimer's Disease

This study examined early detection of Alzheimer's disease (AD) by diffusion tensor visualization-based methodology and neuro-fuzzy tools. Initially, we proposed a model for the early detection of AD using the measurement of apparent diffusion coefficient, fractional anisotropy, and gray matter, which can determine neurological disorder patterns and abnormalities in brain white matter. These are used as input parameters into fuzzy tools, and using fuzzy rules, we evaluate the AD score as an output variable that provides a useful platform to physicians in determining the status of the disease. In the second stage, we present an investigative study on AD and used the neuro-fuzzy classification system for pattern recognition of either AD or healthy control. The experimental results are from 20 samples (14 for training, 3 for validation, and 3 for testing) used in an artificial neural network classification system. The neural network is trained with a training algorithm and the performance of the training algorithm is obtained by executing a fuzzy expert system. Out of 20 patients, 9 are AD patients and 11 are healthy control patients. We present a neuro-fuzzy tool as a better classifier for early detection of AD and obtain a satisfactory performance with 100% accuracy.

Callosal thickness profiles for prognosticating conversion from mild cognitive impairment to Alzheimer's disease: A classification approach

INTRODUCTION

Alzheimer's disease (AD) is the most common form of dementia. Finding biomarkers to prognosticate transition from mild cognitive impairment (MCI) to AD is important to clinical medicine. Promising imaging biomarkers of AD conversion identified so far include atrophy of the cerebral cortex and subcortical gray matter nuclei.

METHODS

This study introduces thickness and bending angle of the corpus callosum as a putative white matter marker of MCI to AD conversion. The corpus callosum is computationally less demanding to segment automatically compared to more complicated structures and a subject can be processed in a few minutes. We aimed to demonstrate that callosal shape and thickness measures provide a simple, effective, and accurate prognostication tool in ADNI dataset. Using longitudinal datasets, we classified MCI subjects based on conversion to AD assessed via cognitive testing. We evaluated the classification accuracy of callosal shape features in comparison with the existing "gold standard" cortical thickness and subcortical gray matter volume measures.

RESULTS

The callosal thickness measures were less accurate in classifying conversion status by cognitive scores compared to gray matter measures for AD.

CONCLUSIONS

While this paper presented a negative result, this method may be more suitable for a disease of the white matter.

Corpus callosum shape and morphology in youth across the psychosis Spectrum

The corpus callosum is the largest white matter tract in the human brain connecting and coordinating homologous regions of the right and left hemispheres and has been strongly implicated in the pathogenesis of psychosis. We investigated corpus callosum morphology in a large community cohort of 917 individuals (aged 8-21), including 267 endorsing subsyndromal or threshold psychotic symptoms (207 on the psychosis spectrum and 60 with limited psychosis based on previously published criteria) and 650 non-psychotic volunteers. We used a highly reliable and previously published algorithm to automatically identify the midsagittal plane and to align the corpus callosum along the anterior and posterior commissures for segmentation, thereby eliminating these sources of error variance in dependent measures, which included perimeter, length, mean thickness and shape (circularity). The parcellation scheme divided the corpus callosum into 7 subregions that consisted of the rostrum, genu, rostral body, anterior midbody, posterior midbody, isthmus, and splenium. Both individuals endorsing psychotic symptoms and those with limited psychosis had significantly (p<.05) smaller area and lower thickness measures compared to healthy volunteers, but did not differ significantly from each other. Findings were relatively widespread indicating a relatively global effect not circumscribed to any particular corpus callosum subregion. These data are consistent with the hypothesis that corpus callosum abnormalities may be evident early in the course of illness and predate the onset of frank psychosis. Given that these measures can be easily obtained and are highly reliable they may assist in the identification of individuals at future risk for psychosis.

Decreased Bilateral FDG-PET Uptake and Inter-Hemispheric Connectivity in Multi-Domain Amnestic Mild Cognitive Impairment Patients: A Preliminary Study

Background: Amnestic mild cognitive impairment (aMCI) is a heterogeneous condition. Based on clinical symptoms, aMCI could be categorized into single-domain aMCI (SD-aMCI, only memory deficit) and multi-domain aMCI (MD-aMCI, one or more cognitive domain deficit). As core intrinsic functional architecture, inter-hemispheric connectivity maintains many cognitive abilities. However, few studies investigated whether SD-aMCI and MD-aMCI have different inter-hemispheric connectivity pattern. Methods: We evaluated inter-hemispheric connection pattern using fluorine-18 positron emission tomography - fluorodeoxyglucose (¹⁸F PET-FDG), resting-state functional MRI and structural T1 in 49 controls, 32 SD-aMCI, and 32 MD-aMCI patients. Specifically, we analyzed the 18^F PET-FDG (intensity normalized by cerebellar vermis) in a voxel-wise manner. Then, we estimated inter-hemispheric functional and structural connectivity by calculating the voxel-mirrored homotopic connectivity (VMHC) and corpus callosum (CC) subregions volume. Further, we correlated inter-hemispheric indices with the behavioral score and pathological biomarkers. Results: We found that MD-aMCI exhibited more several inter-hemispheric connectivity damages than SD-aMCI. Specifically, MD-aMCI displayed hypometabolism in the bilateral middle temporal gyrus (MTG), inferior parietal lobe, and left precuneus (PCu) (p < 0.001, corrected). Correspondingly, MD-aMCI showed decreased VMHC in MTG, PCu, calcarine gyrus, and postcentral gyrus, as well as smaller mid-posterior CC than the SD-aMCI and controls (p < 0.05, corrected). Contrary to MD-aMCI, there were no neuroimaging indices with significant differences between SD-aMCI and controls, except reduced hypometabolism in bilateral MTG. Within aMCI patients, hypometabolism and reduced inter-hemispheric connectivity correlated with worse executive ability. Moreover, hypometabolism indices correlated to increased amyloid deposition. Conclusion: In conclusion, patients with MD-aMCI exhibited the more severe deficit in inter-hemispheric communication than SD-aMCI. This long-range connectivity deficit may contribute to cognitive profiles and potentially serve as a biomarker to estimate disease progression of aMCI patients.

Biometry reference range of the corpus callosum in neonates: An observational study

This study aims to present the reference range of corpus callosum by ultrasound imaging in neonates and to develop a clinically feasible screening method for congenital abnormalities of corpus callosum.An observational study was conducted between January 2015 and July 2016; 2D and 3D ultrasound evaluations were conducted and virtural organ computer-aided analysis was applied in the volume calculation of corpus callosum. The following parameters were measured: thickness of the rostum, thickness of the genu, thickness of the body, thickness of the splenium, anterior-posterior distance, true length of the corpus callosum and the volume of the corpus callosum. Inter- and intraobserver agreement was also evaluated. The corrected gestational age was between 38+0 and 47+2 weeks. The least-mean-square method was used to create the growth curve for each parameter.Complete data sets were available in 317 neonates, ranging from 0 to 28 days of age. Reference values from the 1st to 99th percentiles were provided. All parameters showed a nonlinear growth trend with age. Inter- and intraobserver agreement was excellent for 2D and 3D parameters.Our results suggested that computer techniques can assist in the volume assessment of corpus callosum. The 2D and 3D ultrasound data of 7 morphologic parameters may facilitate the identification of corpus callosum anomalies based on a large population.

Callosal circularity as an early marker for Alzheimer's disease

Background

Although brain atrophy is considered to be a downstream marker of Alzheimer's disease (AD), subtle changes may allow to identify healthy subjects at risk of developing AD. As the ability to select at-risk persons is considered to be important to assess the efficacy of drugs and as MRI is a widely available imaging technique we have recently developed a reliable segmentation algorithm for the corpus callosum (CC). Callosal atrophy within AD has been hypothesized to reflect both myelin breakdown and Wallerian degeneration.

Methods

We applied our fully automated segmentation and feature extraction algorithm to two datasets: the OASIS database consisting of 316 healthy controls (HC) and 100 patients affected by either mild cognitive impairment (MCI) or Alzheimer's disease dementia (ADD) and a second database that was collected at the Memory Clinic of Hospital Network Antwerp and consists of 181 subjects, including healthy controls, subjects with subjective cognitive decline (SCD), MCI, and ADD. All subjects underwent (among others) neuropsychological testing including the Mini-Mental State Examination (MMSE). The extracted features were the callosal area (CCA), the circularity (CIR), the corpus callosum index (CCI) and the thickness profile.

Results

CIR and CCI differed significantly between most groups. Furthermore, CIR allowed us to discriminate between SCD and HC with an accuracy of 77%. The more detailed callosal thickness profile provided little added value towards the discrimination of the different AD stages. The largest effect of normal ageing on callosal thickness was found in the frontal callosal midbody.

Conclusions

To the best of our knowledge, this is the first study investigating changes in corpus callosum morphometry in normal ageing and AD by exploring both summarizing features (CCA, CIR and CCI) and the complete CC thickness profile in two independent cohorts using a completely automated algorithm. We showed that callosal circularity allows to discriminate between an important subgroup of the early AD spectrum (SCD) and age and sex matched healthy controls.

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Callosal circularity allows to discriminate between subjects with subjective cognitive decline and matched healthy controls

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Callosal circularity is smaller in subjects with AD dementia as compared to matched subjects with mild cognitive impairment

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The callosal thickness profile differs between AD and HC, but not between the different clinical AD stages

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The AD thickness profile strongly correlates with age in HCs

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Callosal circularity correlates with CSF biomarkers (T-tau and P-tau) in MCI.

Asymmetric Orientation Distribution Functions (AODFs) revealing intravoxel geometry in diffusion MRI

Characterization of anisotropy via diffusion MRI reveals fiber crossings in a substantial portion of voxels within the white-matter (WM) regions of the human brain. A considerable number of such voxels could exhibit asymmetric features such as bends and junctions. However, widely employed reconstruction methods yield symmetric Orientation Distribution Functions (ODFs) even when the underlying geometry is asymmetric. In this paper, we employ inter-voxel directional filtering approaches through a cone model to reveal more information regarding the cytoarchitectural organization within the voxel. The cone model facilitates a sharpening of the ODFs in some directions while suppressing peaks in other directions, thus yielding an Asymmetric ODF (AODF) field. We also show that a scalar measure of AODF asymmetry can be employed to obtain new contrast within the human brain. The feasibility of the technique is demonstrated on in vivo data obtained from the MGH-USC Human Connectome Project (HCP) and Parkinson's Progression Markers Initiative (PPMI) Project database. Characterizing asymmetry in neural tissue cytoarchitecture could be important for localizing and quantitatively assessing specific neuronal pathways.

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimer's disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose.

OBJECTIVE

To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm.

METHODS

We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD.

RESULTS

The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI.

CONCLUSION

The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

The Pathoconnectivity Profile of Alzheimer's Disease: A Morphometric Coalteration Network Analysis

Gray matter alterations are typical features of brain disorders. However, they do not impact on the brain randomly. Indeed, it has been suggested that neuropathological processes can selectively affect certain assemblies of neurons, which typically are at the center of crucial functional networks. Because of their topological centrality, these areas form a core set that is more likely to be affected by neuropathological processes. In order to identify and study the pattern formed by brain alterations in patients’ with Alzheimer’s disease (AD), we devised an innovative meta-analytic method for analyzing voxel-based morphometry data. This methodology enabled us to discover that in AD gray matter alterations do not occur randomly across the brain but, on the contrary, follow identifiable patterns of distribution. This alteration pattern exhibits a network-like structure composed of coaltered areas that can be defined as coatrophy network. Within the coatrophy network of AD, we were able to further identify a core subnetwork of coaltered areas that includes the left hippocampus, left and right amygdalae, right parahippocampal gyrus, and right temporal inferior gyrus. In virtue of their network centrality, these brain areas can be thought of as pathoconnectivity hubs.

Systemic and Disease-Specific Risk Factors in Vascular Dementia: Diagnosis and Prevention

In order to prevent the onset of vascular dementia (VaD) in aging individuals, it is critical to detect clinically relevant vascular and systemic pathophysiological changes to signal the onset of its preceding prodromal stages. Identifying behavioral and neurobiological markers that are highly sensitive to VaD classification vs. other dementias is likely to assist in developing novel preventive treatment strategies that could delay the onset of disruptive psychomotor symptoms, decrease hospitalizations, and increase the quality of life in clinically-high-risk aging individuals. In light of empirical diagnostic and clinical findings associated with VaD pathophysiology, the current investigation will suggest a few clinically-validated biomarker measures of prodromal VaD cognitive impairments that are correlated with vascular symptomology, and VaD endophenotypes in non-demented aging people. In prodromal VaD individuals, distinguishing VaD from other dementias (e.g., Alzheimer's disease) could facilitate specific early preventive interventions that significantly delay more severe cognitive deterioration or indirectly suppress the onset of dementia with vascular etiology. Importantly, the authors conclude that primary prevention strategies should examine aging individuals by employing comprehensive geriatric assessment approach, taking into account their medical history, and longitudinally noting their vascular, systemic, cognitive, behavioral, and clinical functional status. Secondary prevention strategies may include monitoring chronic medication as well as promoting programs that facilitate social interaction and every-day activities.

Obese Adolescents Show Reduced Cognitive Processing Speed Compared with Healthy Weight Peers

BACKGROUND

Childhood obesity and obesity-associated diabetes and metabolic syndrome (MetS) continue to rise. Obesity has been linked to structural and functional brain abnormalities, particularly in the frontal lobe.

METHODS

One hundred sixty-two adolescents (aged 19.53 ± 1.53 years) underwent medical, neurocognitive, and brain magnetic resonance imaging assessments. Participants were either healthy weight (BMI <25.0 kg/m² or BMI percentile <85%) or obese (BMI ≥30.0 kg/m² or BMI percentile ≥95%). We evaluated frontal lobe cognitive functions and the size of the corpus callosum (CC).

RESULTS

Groups differed on four measures of processing speed contained in four different cognitive tests, but not on executive function. A confirmatory factor analysis verified that the significant processing speed variables loaded on the same factor. We also found differences between the weight groups on the area of the anterior portion of the CC, but not the overall CC. Only the Controlled Oral Word Association Test (COWAT) was significantly correlated with the area of the anterior portion of the CC. In the obese group, 32.4% met criteria for MetS. No differences were found between obese participants with or without MetS and none of the MetS factors contributed consistently to cognitive performance.

CONCLUSIONS

Obese adolescents show slower cognitive processing speed while maintaining equivalent performance on executive functioning compared with their healthy weight peers. The group differences in the anterior portion of the CC, responsible for frontal lobe interhemispheric communication, may in part explain our processing speed findings. Future studies should include a longitudinal design and diffusion tensor imaging to examine the integrity of white matter.

Proteome dynamics during postnatal mouse corpus callosum development

Formation of cortical connections requires the precise coordination of numerous discrete phases. This is particularly significant with regard to the corpus callosum, whose development undergoes several dynamic stages including the crossing of axon projections, elimination of exuberant projections, and myelination of established tracts. To comprehensively characterize the molecular events in this dynamic process, we set to determine the distinct temporal expression of proteins regulating the formation of the corpus callosum and their respective developmental functions. Mass spectrometry-based proteomic profiling was performed on early postnatal mouse corpus callosi, for which limited evidence has been obtained previously, using stable isotope of labeled amino acids in mammals (SILAM). The analyzed corpus callosi had distinct proteomic profiles depending on age, indicating rapid progression of specific molecular events during this period. The proteomic profiles were then segregated into five separate clusters, each with distinct trajectories relevant to their intended developmental functions. Our analysis both confirms many previously-identified proteins in aspects of corpus callosum development, and identifies new candidates in understudied areas of development including callosal axon refinement. We present a valuable resource for identifying new proteins integral to corpus callosum development that will provide new insights into the development and diseases afflicting this structure.

Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease

In this article, we present a unified statistical pipeline for analyzing the white matter (WM) tracts morphometry and microstructural integrity, both globally and locally within the same WM tract, from diffusion tensor imaging. Morphometry is quantified globally by the volumetric measurement and locally by the vertexwise surface areas. Meanwhile, microstructural integrity is quantified globally by the mean fractional anisotropy (FA) and trace values within the specific WM tract and locally by the FA and trace values defined at each vertex of its bounding surface. The proposed pipeline consists of four steps: (1) fully automated segmentation of WM tracts in a multi-contrast multi-atlas framework; (2) generation of the smooth surface representations for the WM tracts of interest; (3) common template surface generation on which the localized morphometric and microstructural statistics are defined and a variety of statistical analyses can be conducted; (4) multiple comparison correction to determine the significance of the statistical analysis results. Detailed herein, this pipeline has been applied to the corpus callosum in Alzheimer's disease (AD) with significantly decreased FA values and increased trace values, both globally and locally, being detected in patients with AD when compared to normal aging populations. A subdivision of the corpus callosum in both hemispheres revealed that the AD pathology primarily affects the body and splenium of the corpus callosum. Validation analyses and two multiple comparison correction strategies are provided. Hum Brain Mapp 38:1875-1893, 2017. © 2017 Wiley Periodicals, Inc.

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimer's disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Reliability of measuring regional callosal atrophy in neurodegenerative diseases

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images.

After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimer's Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates.

We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97.

In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

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A completely automated segmentation of the Corpus Callosum

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Both traditional features and the thickness profile using Laplace's equation are calculated.

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Excellent reproducibility and accuracy in healthy controls

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Excellent reproducibility and accuracy in Alzheimer's Dementia and Multiple Sclerosis patients

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Excellent inter-scanner reliability enabling the pooling of multi-center data

A Novel Form of Compensation in the Tg2576 Amyloid Mouse Model of Alzheimer's Disease

One century after its first description, pathology of Alzheimer’s disease (AD) is still poorly understood. Amyloid-related dendritic atrophy and membrane alterations of susceptible brain neurons in AD, and in animal models of AD are widely recognized. However, little effort has been made to study the potential effects of combined morphological and membrane alterations on signal transfer and synaptic integration in neurons that build up affected neural networks in AD. In this study spatial reconstructions and electrophysiological measurements of layer II/III pyramidal neurons of the somatosensory cortex from wild-type (WT) and transgenic (TG) human amyloid precursor protein (hAPP) overexpressing Tg2576 mice were used to build faithful segmental cable models of these neurons. Local synaptic activities were simulated in various points of the dendritic arbors and properties of subthreshold dendritic impulse propagation and predictors of synaptic input pattern recognition ability were quantified and compared in modeled WT and TG neurons. Despite the widespread dendritic degeneration and membrane alterations in mutant mouse neurons, surprisingly little, or no change was detected in steady-state and 50 Hz sinusoidal voltage transfers, current transfers, and local and propagation delays of PSPs traveling along dendrites of TG neurons. Synaptic input pattern recognition ability was also predicted to be unaltered in TG neurons in two different soma-dendritic membrane models investigated. Our simulations predict the way how subthreshold dendritic signaling and pattern recognition are preserved in TG neurons: amyloid-related membrane alterations compensate for the pathological effects that dendritic atrophy has on subthreshold dendritic signal transfer and integration in layer II/III somatosensory neurons of this hAPP mouse model for AD. Since neither propagation of single PSPs nor integration of multiple PSPs (pattern recognition) changes in TG neurons, we conclude that AD-related neuronal hyperexcitability cannot be accounted for by altered subthreshold dendritic signaling in these neurons but hyperexcitability is related to changes in active membrane properties and network connectivity.

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy

Introduction

We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods

Longitudinal (baseline + 1-year follow-up) MRI scans of 132 MCI subjects from the Alzheimer's Disease Neuroimaging Initiative were used. A total of 54 subjects did not convert to AD over an average (±SD) follow-up of 5.46 (±1.63) years, whereas 78 converted to AD with an average conversion time of 2.56 (±1.65) years. Annual change in the corpus callosum thickness profile was calculated from the baseline and 1-year follow-up MRI. A logistic regression model with fused lasso regularization for prediction was applied to the annual changes.

Results

We found a sex difference. The accuracy of prediction was 84% in females and 61% in males. The discriminating regions of corpus callosum differed between sexes. In females, the genu, rostrum, and posterior body had predictive power, whereas the genu and splenium were relevant in males.

Discussion

Annual callosal atrophy predicts MCI-to-AD conversion in females more accurately than in males.

Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease

BACKGROUND

Corpus callosum (CC) size and shape have been previously studied in Alzheimer's disease (AD) with the majority of studies having been cross-sectional. Due to the large variance in normal CC morphology, cross-sectional studies are limited in statistical power. Determining individual rates of change requires longitudinal data. Physiological changes are particularly relevant in mild cognitive impairment (MCI), in which CC morphology has not been previously studied longitudinally.

OBJECTIVE

To study temporal rates of change in CC morphology in MCI patients over a one-year period, and to determine whether these rates differ between MCI subjects who converted to AD (MCI-C) and those who did not (MCI-NC) over an average (±SD) observation period of 5.4 (±1.6) years.

METHODS

We used a novel multi-atlas based algorithm to segment the mid-sagittal cross-sectional area of the CC in longitudinal MRI scans. Rates of change of CC circularity, total area, and five sub-areas were compared between 57 MCI-NC and 81 MCI-C subjects.

RESULTS

The CC became less circular (-0.89% per year in MCI-NC, -1.85% per year in MCI-C) with time, with faster decline in MCI-C (p = 0.0002). In females, atrophy rates were higher in MCI-C relative to MCI-NC in total CC area (p = 0.0006), genu/rostrum (p = 0.005), and splenium (0.002). In males, these rates did not differ between groups.

CONCLUSION

A greater than normal decline in CC circularity was shown to be an indicator of prodromal AD in MCI subjects. This measure is potentially useful as an imaging biomarker of disease and a therapeutic target in clinical trials.

Corpus callosum area in patients with bipolar disorder with and without psychotic features: an international multicentre study

BACKGROUND

Previous studies have reported MRI abnormalities of the corpus callosum (CC) in patients with bipolar disorder (BD), although only a few studies have directly compared callosal areas in psychotic versus nonpsychotic patients with this disorder. We sought to compare regional callosal areas in a large international multicentre sample of patients with BD and healthy controls.

METHODS

We analyzed anatomic T1 MRI data of patients with BD-I and healthy controls recruited from 4 sites (France, Germany, Ireland and the United States). We obtained the mid-sagittal areas of 7 CC subregions using an automatic CC delineation. Differences in regional callosal areas between patients and controls were compared using linear mixed models (adjusting for age, sex, handedness, brain volume, history of alcohol abuse/dependence, lithium or antipsychotic medication status, symptomatic status and site) and multiple comparisons correction. We also compared regional areas of the CC between patients with BD with and without a history of psychotic features.

RESULTS

We included 172 patients and 146 controls in our study. Patients with BD had smaller adjusted mid-sagittal CC areas than controls along the posterior body, the isthmus and the splenium of the CC. Patients with a positive history of psychotic features had greater adjusted area of the rostral CC region than those without a history of psychotic features.

LIMITATIONS

We found small to medium effect sizes, and there was no calibration technique among the sites.

CONCLUSION

Our results suggest that BD with psychosis is associated with a different pattern of interhemispheric connectivity than BD without psychosis and could be considered a relevant neuroimaging subtype of BD.

Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

PURPOSE

Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.

METHOD

First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.

RESULTS

The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.

CONCLUSION

The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.

Age and sex effects on corpus callosum morphology across the lifespan

The corpus callosum (CC) is the largest interhemispheric white matter tract in the human brain, and is characterized by pronounced differences in morphology among individuals. There are limited data, however, regarding typical development, sex differences, and the neuropsychological correlates of individual differences within CC subregions. Magnetic resonance (MR) imaging exams were collected in a large cohort (N = 305) of healthy individuals (ages 8-68). We used a highly reliable program to automatically identify the midsagittal plane and obtain CC subregion measures according to approaches described by Witelson [1989]: Brain 112:799-835 and Hampel et al. [1998]: Arch Neurol 55:193-198 and a measure of whole CC shape (i.e., circularity). CC measurement parameters, including area, perimeter, length, circularity, and CC subregion area values were generally characterized by inverted U-shaped curves across the observed age range. Peak values for CC subregions were observed between ages 32 and 45, and descriptive linear correlations were consistent with sharper area changes in development. We also observed differing age-associated changes across the lifespan between males and females in the CC subregion corresponding to the genu (Witelson's subregion 2), as well as CC circularity. Mediation analysis using path modeling indicated that genu area mediated the relationship between age and processing speed for females, and the relationship between age and visual learning and executive functioning for males. Taken together, our findings implicate sex differences in CC morphology across the lifespan that are localized to the genu, which appear to mediate neuropsychological functions.

The corpus callosum and recovery of working memory after epilepsy surgery

OBJECTIVE

For patients with medically intractable focal epilepsy, the benefit of epilepsy surgery must be weighed against the risk of cognitive decline. Clinical factors such as age and presurgical cognitive level partially predict cognitive outcome; yet, little is known about the role of cross-hemispheric white matter pathways in supporting postsurgical recovery of cognitive function. The purpose of this study is to determine whether the presurgical corpus callosum (CC) midsagittal area is associated with pre- to postsurgical change following epilepsy surgery.

METHODS

In this observational study, we retrospectively identified 24 adult patients from an epilepsy resection series who completed preoperative high-resolution T1 -weighted magnetic resonance imaging (MRI) scans, as well as pre- and postsurgical neuropsychological testing. The total area and seven subregional areas of the CC were measured on the midsagittal MRI slice using an automated method. Standardized indices of auditory-verbal working memory and delayed memory were used to probe cognitive change from pre- to postsurgery. CC total and subregional areas were regressed on memory-change scores, after controlling for overall brain volume, age, presurgical memory scores, and duration of epilepsy.

RESULTS

Patients had significantly reduced CC area relative to healthy controls. We found a positive relationship between CC area and change in working memory, but not delayed memory; specifically, the larger the CC, the greater the postsurgical improvement in working memory (β = 0.523; p = 0.009). Effects were strongest in posterior CC subregions. There was no relationship between CC area and presurgical memory scores.

SIGNIFICANCE

Findings indicate that larger CC area, measured presurgically, is related to improvement in working memory abilities following epilepsy surgery. This suggests that transcallosal pathways may play an important, yet little understood, role in postsurgical recovery of cognitive functions.

Corpus callosum shape and size changes in early Alzheimer's disease: a longitudinal MRI study using the OASIS brain database

BACKGROUND

Alzheimer's disease (AD) has been shown to be associated with shrinkage of the corpus callosum mid-sagittal cross-sectional area (CCA).

OBJECTIVE

To study temporal rates of corpus callosum atrophy not previously reported for early AD.

METHODS

We used longitudinal MRI scans to study the rates of change of CCA and circularity (CIR), a measure of its shape, in normal controls (NC, n = 75), patients with very mild AD (AD-VM, n = 51), and mild AD (AD-M, n = 21).

RESULTS

There were significant reduction rates in CCA and CIR in all three groups. While CCA reduction rates were not statistically different between groups, the CIR declined faster in AD-VM (p < 0.03) and AD-M (p < 0.0001) relative to NC, and in AD-M relative to AD-VM (p < 0.0004).

CONCLUSION

CIR declines at an accelerated rate with AD severity. Its rate of change is more closely associated with AD progression than CCA or any of its sub-regions. CIR may be a useful group biomarker for objective assessment of treatments that aim to slow AD progression.