Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study

Surface-based morphometry of the corpus callosum in young children of ages 1-5

The corpus callosum (CC) is a large white matter fiber bundle in the brain and is involved in various cognitive, sensory, and motor processes. While implicated in various developmental and psychiatric disorders, much is yet to be uncovered about the normal development of this structure, especially in young children. Additionally, while sexual dimorphism has been reported in prior literature, observations have not necessarily been consistent. In this study, we use morphometric measures including surface tensor-based morphometry (TBM) to investigate local changes in the shape of the CC in children between the ages of 12 and 60 months, in intervals of 12 months. We also analyze sex differences in each of these age groups. We observed larger significant clusters in the earlier ages between 12 v 24 m and between 48 v 60 m and localized differences in the anterior region of the body of the CC. Sex differences were most pronounced in the 12 m group. This study adds to the growing literature of work aiming to understand the developing brain and emphasizes the utility of surface TBM as a useful tool for analyzing regional differences in neuroanatomical morphometry.

FPLS-DC: functional partial least squares through distance covariance for imaging genetics

MOTIVATION

Imaging genetics integrates imaging and genetic techniques to examine how genetic variations influence the function and structure of organs like the brain or heart, providing insights into their impact on behavior and disease phenotypes. The use of organ-wide imaging endophenotypes has increasingly been used to identify potential genes associated with complex disorders. However, analyzing organ-wide imaging data alongside genetic data presents two significant challenges: high dimensionality and complex relationships. To address these challenges, we propose a novel, nonlinear inference framework designed to partially mitigate these issues.

RESULTS

We propose a functional partial least squares through distance covariance (FPLS-DC) framework for efficient genome wide analyses of imaging phenotypes. It consists of two components. The first component utilizes the FPLS-derived base functions to reduce image dimensionality while screening genetic markers. The second component maximizes the distance correlation between genetic markers and projected imaging data, which is a linear combination of the FPLS-basis functions, using simulated annealing algorithm. In addition, we proposed an iterative FPLS-DC method based on FPLS-DC framework, which effectively overcomes the influence of inter-gene correlation on inference analysis. We efficiently approximate the null distribution of test statistics using a gamma approximation. Compared to existing methods, FPLS-DC offers computational and statistical efficiency for handling large-scale imaging genetics. In real-world applications, our method successfully detected genetic variants associated with the hippocampus, demonstrating its value as a statistical toolbox for imaging genetic studies.

AVAILABILITY AND IMPLEMENTATION

The FPLS-DC method we propose opens up new research avenues and offers valuable insights for analyzing functional and high-dimensional data. In addition, it serves as a useful tool for scientific analysis in practical applications within the field of imaging genetics research. The R package FPLS-DC is available in Github: https://github.com/BIG-S2/FPLSDC.

Fine-grained features characterize hippocampal and amygdaloid change pattern in Parkinson's disease and discriminate cognitive-deficit subtype

AIMS

To extract vertex-wise features of the hippocampus and amygdala in Parkinson's disease (PD) with mild cognitive impairment (MCI) and normal cognition (NC) and further evaluate their discriminatory efficacy.

METHODS

High-resolution 3D-T1 data were collected from 68 PD-MCI, 211 PD-NC, and 100 matched healthy controls (HC). Surface geometric features were captured using surface conformal representation, and surfaces were registered to a common template using fluid registration. The statistical tests were performed to detect differences between groups. The disease-discriminatory ability of features was also tested in the ensemble classifiers.

RESULTS

The amygdala, not the hippocampus, showed significant overall differences among the groups. Compared with PD-NC, the right amygdala in MCI patients showed expansion (anterior cortical, anterior amygdaloid, and accessory basal areas) and atrophy (basolateral ventromedial area) subregions. There was notable atrophy in the right CA1 and hippocampal subiculum of PD-MCI. The accuracy of classifiers with multivariate morphometry statistics as features exceeded 85%.

CONCLUSION

PD-MCI is associated with multiscale morphological changes in the amygdala, as well as subtle atrophy in the hippocampus. These novel metrics demonstrated the potential to serve as biomarkers for PD-MCI diagnosis. Overall, these findings from this study help understand the role of subcortical structures in the neuropathological mechanisms of PD cognitive impairment.

Correlation studies of Hippocampal Morphometry and Plasma NFL Levels in Cognitively Unimpaired Subjects

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the "social brain", are related to inter-individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter-subject representational similarity analysis (IS-RSA) to assess relationships between amygdala and hippocampal morphometry (surface-based multivariate morphometry statistics, MMS), connectivity (resting-state functional connectivity, rs-FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants ( N = 24 $$ N=24 $$ ). In IS-RSA, we proposed a novel pipeline, that is, computing patching and pooling operations-based surface distance (CPP-SD), to obtain a decent representation for high-dimensional MMS data. On this basis, we found significant correlations (i.e., second-order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region-related mentalising specificity emerged from these associations: self-self and self-other mentalisation are more related to the hippocampus, while other-self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs-FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS-RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.

Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding

BACKGROUND

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the "ATN framework" of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development).

OBJECTIVE

To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements.

METHODS

With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction.

RESULTS

We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer's Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics.

CONCLUSION

The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

A Surface-Based Federated Chow Test Model for Integrating APOE Status, Tau Deposition Measure, and Hippocampal Surface Morphometry

BACKGROUND

Alzheimer's disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans.

OBJECTIVE

To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline.

METHODS

Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry.

RESULTS

We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort.

CONCLUSION

Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

Individuals with cannabis use are associated with widespread morphological alterations in the subregions of the amygdala, hippocampus, and pallidum

BACKGROUND

Cannabis is the most frequently used illicit drug worldwide. Although multiple structural MRI studies of individuals with cannabis use (CB) have been undertaken, the reports of the volume alterations in the amygdala, hippocampus, and pallidum are not consistent. This study aims to detect subregion-level morphological alterations, analyze the correlation areas with cannabis usage characteristics, and gain new insights into the neuro mechanisms of CB.

METHODS

By leveraging the novel surface-based subcortical morphometry method, 20 CB and 22 age- and sex-matched healthy controls (HC) were included to explore their volumetric and morphological differences in the three subcortical structures. Afterward, the correlation analysis between surface morphological eigenvalues and cannabis usage characteristics was performed.

RESULTS

Compared with volumetric measures, the surface-based subcortical morphometry method detected more significant global morphological deformations in the left amygdala, right hippocampus, and right pallidum (overall-p < 0.05, corrected). More obvious morphological alterations (atrophy or expansion) were observed in specific subregions (vertex-based p-value<0.05, uncorrected) of the three subcortical structures. Both positive and negative subregional correlation areas were reported by the correlation analysis.

CONCLUSIONS

The current study illuminated new pathophysiologic mechanisms in the amygdala, hippocampus, and pallidum at the subregion level, which may inform the subsequent smaller-scale CB research.

Shape analysis of subcortical structures in obsessive-compulsive disorder and the relationship with comorbid anxiety, depression, and medication use: A meta-analysis by the OCD Brain Imaging Consortium

OBJECTIVE

Neuroimaging studies of obsessive-compulsive disorder (OCD) patients have highlighted the important role of deep gray matter structures. Less work has however focused on subcortical shape in OCD patients.

METHODS

Here we pooled brain MRI scans from 412 OCD patients and 368 controls to perform a meta-analysis utilizing the ENIGMA-Shape protocol. In addition, we investigated modulating effects of medication status, comorbid anxiety or depression, and disease duration on subcortical shape.

RESULTS

There was no significant difference in shape thickness or surface area between OCD patients and healthy controls. For the subgroup analyses, OCD patients with comorbid depression or anxiety had lower thickness of the hippocampus and caudate nucleus and higher thickness of the putamen and pallidum compared to controls. OCD patients with comorbid depression had lower shape surface area in the thalamus, caudate nucleus, putamen, hippocampus, and nucleus accumbens and higher shape surface area in the pallidum. OCD patients with comorbid anxiety had lower shape surface area in the putamen and the left caudate nucleus and higher shape surface area in the pallidum and the right caudate nucleus. Further, OCD patients on medication had lower shape thickness of the putamen, thalamus, and hippocampus and higher thickness of the pallidum and caudate nucleus, as well as lower shape surface area in the hippocampus and amygdala and higher surface area in the putamen, pallidum, and caudate nucleus compared to controls. There were no significant differences between OCD patients without co-morbid anxiety and/or depression and healthy controls on shape measures. In addition, there were also no significant differences between OCD patients not using medication and healthy controls.

CONCLUSIONS

The findings here are partly consistent with prior work on brain volumes in OCD, insofar as they emphasize that alterations in subcortical brain morphology are associated with comorbidity and medication status. Further work is needed to understand the biological processes contributing to subcortical shape.

Investigating the Effect of Tau Deposition and Apoe on Hippocampal Morphometry in Alzheimer’s Disease: A Federated Chow Test Model

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. Tau tangle is the specific protein pathological hallmark of AD and plays a crucial role in leading to dementia-related structural deformations observed in magnetic resonance imaging (MRI) scans. The volume loss of hippocampus is mainly related to the development of AD. Besides, apolipoprotein E (APOE) also has significant effects on the risk of developing AD. However, few studies focus on integrating genotypes, MRI, and tau deposition to infer multimodal relationships. In this paper, we proposed a federated chow test model to study the synergistic effects of APOE and tau on hippocampal morphometry. Our experimental results demonstrate our model can detect the difference of tau deposition and hippocampal atrophy among the cohorts with different genotypes and subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal tau in the homozygote cohort. Our model will provide novel insight into the neural mechanisms about the individual impact of APOE and tau deposition on brain imaging.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

BACKGROUND

A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes.

OBJECTIVE

We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61).

METHODS

Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs.

RESULTS

The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC.

CONCLUSION

The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Federated Morphometry Feature Selection for Hippocampal Morphometry Associated Beta-Amyloid and Tau Pathology

Amyloid-β (Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer's disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. One of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research focuses in the AD pathophysiological progress. This work proposes a novel framework, Federated Morphometry Feature Selection (FMFS) model, to examine subtle aspects of hippocampal morphometry that are associated with Aβ/tau burden in the brain, measured using positron emission tomography (PET). FMFS is comprised of hippocampal surface-based feature calculation, patch-based feature selection, federated group LASSO regression, federated screening rule-based stability selection, and region of interest (ROI) identification. FMFS was tested on two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts to understand hippocampal alterations that relate to Aβ/tau depositions. Each cohort included pairs of MRI and PET for AD, mild cognitive impairment (MCI), and cognitively unimpaired (CU) subjects. Experimental results demonstrated that FMFS achieves an 89× speedup compared to other published state-of-the-art methods under five independent hypothetical institutions. In addition, the subiculum and cornu ammonis 1 (CA1 subfield) were identified as hippocampal subregions where atrophy is strongly associated with abnormal Aβ/tau. As potential biomarkers for Aβ/tau pathology, the features from the identified ROIs had greater power for predicting cognitive assessment and for survival analysis than five other imaging biomarkers. All the results indicate that FMFS is an efficient and effective tool to reveal associations between Aβ/tau burden and hippocampal morphometry.

Bladder surface dose modeling in prostate cancer radiotherapy: An analysis of motion-induced variations and the cumulative dose across the treatment

PURPOSE

To introduce a novel surface-based dose mapping method to improve quantitative bladder dosimetric assessment in prostate cancer (PC) radiotherapy.

METHODS

Based on the planning and daily pre and postfraction MRIs of 12 PC patients, bladder surface models (SMs) were generated on manually delineated contours and regionally aligned via surface-based registration. Subsequently, bladder surface dose models (SDMs) were created using face-wise dose sampling. To determine the bladder intrafractional and interfractional motion and dose variation, we performed a pose analysis between pre and postfraction bladder SMs, as well as surface mapping for fractional SMs. Discrepancies between the received dose, accumulated from daily SDMs, and the planned dose were then assessed on the corresponding SDMs. Complementary to the surface dose mapping, dose surface histogram (DSH)-based comparisons were also performed.

RESULTS

The intrafraction pose analysis revealed a significant (p < 0.05) bladder expansion, as well as an anterior/superior drift during the treatment. The intrafraction motion substantially altered dose to mid-bladder body, but not the bladder surface areas distal to or contiguous with the target. A similar pattern of dose variations was also detected by interfraction comparisons. With surface registration to the common SM, the cumulative bladder dose significantly differs from the planned dose. The discrepancy is evident in the mid-posterior range that corresponds to a mid- to high-dose region. The received DSH significantly differs from the planned DSH after permutation correction (p = 0.0122), while the overall surface-based comparison after multiple comparison correction is nonsignificant (p = 0.0800).

CONCLUSIONS

We developed a novel surface-based intra and interdose mapping framework applied to a unique daily MR dataset for image-guided radiotherapy. The framework identified significant intrafraction bladder positional changes, localized the intra and interfraction variations, and quantified planned versus received dose differences on the bladder surface. The result indicates the importance of adopting the motion-integrated bladder SDM for bladder dose management.

Predicting Tau Accumulation in Cerebral Cortex with Multivariate MRI Morphometry Measurements, Sparse Coding, and Correntropy

Biomarker-assisted diagnosis and intervention in Alzheimer's disease (AD) may be the key to prevention breakthroughs. One of the hallmarks of AD is the accumulation of tau plaques in the human brain. However, current methods to detect tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (Tau PET). In our previous work, structural MRI-based hippocampal multivariate morphometry statistics (MMS) showed superior performance as an effective neurodegenerative biomarker for preclinical AD and Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) has excellent ability to generate low-dimensional representations with strong statistical power for brain amyloid prediction. In this work, we apply this framework together with ridge regression models to predict Tau deposition in Braak12 and Braak34 brain regions separately. We evaluate our framework on 925 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Each subject has one pair consisting of a PET image and MRI scan which were collected at about the same times. Experimental results suggest that the representations from our MMS and PASCS-MP have stronger predictive power and their predicted Braak12 and Braak34 are closer to the real values compared to the measures derived from other approaches such as hippocampal surface area and volume, and shape morphometry features based on spherical harmonics (SPHARM).

Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala

BACKGROUND

The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied.

OBJECTIVE

To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up.

METHODS

Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations.

RESULTS

Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala.

CONCLUSION

These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images

An effective presymptomatic diagnosis and treatment of Alzheimer's disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention.

Predicting future cognitive decline with hyperbolic stochastic coding

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.

Improved Prediction of Imminent Progression to Clinically Significant Memory Decline Using Surface Multivariate Morphometry Statistics and Sparse Coding

BACKGROUND

Besides their other roles, brain imaging and other biomarkers of Alzheimer's disease (AD) have the potential to inform a cognitively unimpaired (CU) person's likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures.

OBJECTIVE

To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI).

METHODS

Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years ("progressors") and 80 matched adults who remained CU for at least 4 years ("nonprogressors"). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI.

RESULTS

We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume.

CONCLUSION

Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

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.

A novel pipeline leveraging surface-based features of small subcortical structures to classify individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is accompanied with widespread impairment in social-emotional functioning. Classification of ASD using sensitive morphological features derived from structural magnetic resonance imaging (MRI) of the brain may help us to better understand ASD-related mechanisms and improve related automatic diagnosis. Previous studies using T1 MRI scans in large heterogeneous ABIDE dataset with typical development (TD) controls reported poor classification accuracies (around 60%). This may because they only considered surface-based morphometry (SBM) as scalar estimates (such as cortical thickness and surface area) and ignored the neighboring intrinsic geometry information among features. In recent years, the shape-related SBM achieves great success in discovering the disease burden and progression of other brain diseases. However, when focusing on local geometry information, its high dimensionality requires careful treatment in its application to machine learning. To address the above challenges, we propose a novel pipeline for ASD classification, which mainly includes the generation of surface-based features, patch-based surface sparse coding and dictionary learning, Max-pooling and ensemble classifiers based on adaptive optimizers. The proposed pipeline may leverage the sensitivity of brain surface morphometry statistics and the efficiency of sparse coding and Max-pooling. By introducing only the surface features of bilateral hippocampus that derived from 364 male subjects with ASD and 381 age-matched TD males, this pipeline outperformed five recent MRI-based ASD classification studies with >80% accuracy in discriminating individuals with ASD from TD controls. Our results suggest shape-related SBM features may further boost the classification performance of MRI between ASD and TD.

A Deformation-Based Shape Study of the Corpus Callosum in First Episode Schizophrenia

Background: Previous first-episode schizophrenia (FES) studies have reported abnormalities in the volume and mid-sagittal size of the corpus callosum (CC), but findings have been inconsistent. Besides, the CC shape has rarely been analyzed in FES. Therefore, in this study, we investigated FES-related CC shape abnormalities using 198 participants [92 FES patients and 106 healthy controls (HCs)]. Methods: We conducted statistical shape analysis of the mid-sagittal CC curve in a large deformation diffeomorphic metric mapping framework. The CC was divided into the genu, body, and splenium (gCC, bCC, and sCC) to target the key CC sub-regions affected by the FES pathology. Gender effects have been investigated. Results: There were significant area differences between FES and HC in the entire CC and gCC but not in bCC nor sCC. In terms of the localized shape morphometrics, significant region-specific shape inward-deformations were detected in the superior portion of gCC and the anterosuperior portion of bCC in FES. These global area and local shape morphometric abnormalities were restricted to female FES but not male FES. Conclusions: gCC was significantly affected in the neuropathology of FES and this finding was specific to female FES. This study suggests that gCC may be a key sub-region that is vulnerable to the neuropathology of FES, specifically in female patients. The morphometrics of gCC may serve as novel and efficient biomarkers for screening female FES patients.

Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.

FreeSurfer ‐based segmentation of hippocampal subfields: A review of methods and applications, with a novel quality control procedure for ENIGMA studies and other collaborative efforts

Structural hippocampal abnormalities are common in many neurological and psychiatric disorders, and variation in hippocampal measures is related to cognitive performance and other complex phenotypes such as stress sensitivity. Hippocampal subregions are increasingly studied, as automated algorithms have become available for mapping and volume quantification. In the context of the Enhancing Neuro Imaging Genetics through Meta Analysis Consortium, several Disease Working Groups are using the FreeSurfer software to analyze hippocampal subregion (subfield) volumes in patients with neurological and psychiatric conditions along with data from matched controls. In this overview, we explain the algorithm's principles, summarize measurement reliability studies, and demonstrate two additional aspects (subfield autocorrelation and volume/reliability correlation) with illustrative data. We then explain the rationale for a standardized hippocampal subfield segmentation quality control (QC) procedure for improved pipeline harmonization. To guide researchers to make optimal use of the algorithm, we discuss how global size and age effects can be modeled, how QC steps can be incorporated and how subfields may be aggregated into composite volumes. This discussion is based on a synopsis of 162 published neuroimaging studies (01/2013–12/2019) that applied the FreeSurfer hippocampal subfield segmentation in a broad range of domains including cognition and healthy aging, brain development and neurodegeneration, affective disorders, psychosis, stress regulation, neurotoxicity, epilepsy, inflammatory disease, childhood adversity and posttraumatic stress disorder, and candidate and whole genome (epi‐)genetics. Finally, we highlight points where FreeSurfer‐based hippocampal subfield studies may be optimized.

Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression

Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer's disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.

Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

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A completely automated surface-based ventricular morphometry system.

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Generate a whole connected 3D ventricular shape model.

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Test-retest the system in two independent CU subject cohorts.

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Subregional ventricular abnormalities prior to clinically memory decline.

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

Morphological changes in subregions of hippocampus and amygdala in major depressive disorder patients

Despite many neuroimaging studies in the past years, the neuroanatomical substrates of major depressive disorder (MDD) subcortical structures are still not well understood. Since hippocampus and amygdala are the two vital subcortical structures that most susceptible to MDD, finding the evidence of morphological changes in their subregions may bring some new insights for MDD research. Combining structural magnetic resonance imaging (MRI) with novel morphometry analysis methods, we recruited 25 MDD patients and 28 healthy controls (HC), and investigated their volume and morphological differences in hippocampus and amygdala. Relative to volumetric method, our methods detected more significant global morphological atrophies (p<0.05). More precisely, subiculum and cornu ammonis (CA) 1 subregions of bilateral hippocampus, lateral (LA) and basolateral ventromedial (BLVM) of left amygdala and LA, BLVM, central (CE), amygdalostriatal transition area (ASTR), anterior cortical (ACO) and anterior amygdaloid area (AAA) of right amygdala were demonstrated prone to atrophy. Correlation analyses between each subject's surface eigenvalues and Hamilton Depression Scale (HAMD) were then performed. Correlation results showed that atrophy areas in hippocampus and amygdala have slight tendencies of expanding into other subregions with the development of MDD. Finally, we performed group morphometric analysis and drew the atrophy and expansion areas between MDD-Medicated group (only 19 medicated subjects in MDD group were included) and HC group, found some preliminary evidence about subregional morphological resilience of hippocampus and amygdala. These findings revealed new pathophysiologic patterns in the subregions of hippocampus and amygdala, which can help with subsequent smaller-scale MDD research.

TEMPORALLY ADAPTIVE-DYNAMIC SPARSE NETWORK FOR MODELING DISEASE PROGRESSION

Alzheimer's disease (AD) is a neurodegenerative disorder with progressive impairment of memory and cognitive functions. Sparse coding (SC) has been demonstrated to be an efficient and effective method for AD diagnosis and prognosis. However, previous SC methods usually focus on the baseline data while ignoring the consistent longitudinal features with strong sparsity pattern along the disease progression. Additionally, SC methods extract sparse features from image patches separately rather than learn with the dictionary atoms across the entire subject. To address these two concerns and comprehensively capture temporal-subject sparse features towards earlier and better discriminability of AD, we propose a novel supervised SC network termed Temporally Adaptive-Dynamic Sparse Network (TADsNet) to uncover the sequential correlation and native subject-level codes from the longitudinal brain images. Our work adaptively updates the sparse codes to impose the temporal regularized correlation and dynamically mine the dictionary atoms to make use of entire subject-level features. Experimental results on ADNI-I cohort validate the superiority of our approach.

Cortical thickness computation by solving tetrahedron-based harmonic field

Cortical thickness computation in magnetic resonance imaging (MRI) is an important method to study the brain morphological changes induced by neurodegenerative diseases. This paper presents an algorithm of thickness measurement based on a volumetric Laplacian operator (VLO), which is able to capture accurately the geometric information of brain images. The proposed algorithm is a novel three-step method: 1) The rule of parity and the shrinkage strategy are combined to detect and fix the intersection error regions between the cortical surface meshes separated by FreeSurfer software and the tetrahedral mesh is constructed which reflects the original morphological features of the cerebral cortex, 2) VLO and finite element method are combined to compute the temperature distribution in the cerebral cortex under the Dirichlet boundary conditions, and 3) the thermal gradient line is determined based on the constructed local isothermal surfaces and linear geometric interpolation results. Combined with half-face data storage structure, the cortical thickness can be computed accurately and effectively from the length of each gradient line. With the obtained thickness, we set experiments to study the group differences among groups of Alzheimer's disease (AD, N = 110), mild cognitive impairment (MCI, N = 101) and healthy control people (CTL, N = 128) by statistical analysis. The results show that the q-value associated with the group differences is 0.0458 between AD and CTL, 0.0371 between MCI and CTL, and 0.0044 between AD and MCI. Practical tests demonstrate that the algorithm of thickness measurement has high efficiency and is generic to be applied to various biological structures that have internal and external surfaces.

Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD‐related differences in subcortical regions using shape analysis. In this meta‐analysis of subcortical shape from the ENIGMA‐MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta‐analysis. Relative to CTL, patients with adolescent‐onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first‐episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD‐associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images

BACKGROUND

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

OBJECTIVE

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

METHODS

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog).

RESULTS

We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods.

CONCLUSION

Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Multi-task Dictionary Learning based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer's Disease

Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimers disease (AD). The current state-of-the-art models for many images analyzing tasks are based on Convolutional Neural Networks (CNN). However, the lack of training data is a common challenge in applying CNN to the diagnosis of AD and its prodromal stages. Another challenge for CAD applications is the controversy between the requiring of longitudinal cortical structural information for higher diagnosis/prognosis accuracy and the computing ability for processing varied imaging features. To address these two challenges, we propose a novel computer-aided AD diagnosis system CNN-Multitask Stochastic Coordinate Coding (MSCC) which integrates CNN with transfer learning strategy, a novel MSCC algorithm and our effective AD-related biomarkers-multivariate morphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC achieved superior results. The proposed system may aid in expediting the diagnosis of AD progress, facilitating earlier clinical intervention, and resulting in improved clinical outcomes.

The Effects of Air Pollution on the Brain: a Review of Studies Interfacing Environmental Epidemiology and Neuroimaging

Purpose of Review

An emerging body of evidence has raised concern regarding the potentially harmful effects of inhaled pollutants on the central nervous system during the last decade. In the general population, traffic-related air pollution (TRAP) exposure has been associated with adverse effects on cognitive, behavior, and psychomotor development in children, and with cognitive decline and higher risk of dementia in the elderly. Recently, studies have interfaced environmental epidemiology with magnetic resonance imaging to investigate in vivo the effects of TRAP on the human brain. The aim of this systematic review was to describe and synthesize the findings from these studies. The bibliographic search was carried out in PubMed with ad hoc keywords.

Recent Findings

The selected studies revealed that cerebral white matter, cortical gray matter, and basal ganglia might be the targets of TRAP. The detected brain damages could be involved in cognition changes.

Summary

The effect of TRAP on cognition appears to be biologically plausible. Interfacing environmental epidemiology and neuroimaging is an emerging field with room for improvement. Future studies, together with inputs from experimental findings, should provide more relevant and detailed knowledge about the nature of the relationship between TRAP exposure and cognitive, behavior, and psychomotor disorders observed in the general population.

Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

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Applied surface-based hippocampal morphometry on cognitively unimpaired subjects.

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Our study identified APOE-e4 dose effects on cognitively unimpaired subjects.

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Surface-based hippocampal morphometry outperformed the hippocampal volume measure.

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Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.

A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

Combined morphologic and metabolic pipeline for Positron emission tomography/computed tomography based radiotherapy response evaluation in locally advanced pancreatic adenocarcinoma

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A novel morphologic and metabolic combined pipeline for PA response evaluation.

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The derived metric outperformed traditional imaging metrics in risk stratification.

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May serve as a new image biomarker to characterize heterogeneous tumor response.

Background and purpose

Adaptive radiation planning for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment, while traditional response evaluation criteria inefficiently characterize tumors with complex morphological features or intrinsically low metabolism. To better assess treatment response of PA, we quantify and compare regional morphological and metabolic features of the 3D pre- and post-radiation therapy (RT) tumor models.

Materials and methods

Thirty-one PA patients with pre and post-RT Positron emission tomography/computed tomography (PET/CT) scans were evaluated. 3D meshes of pre- and post-RT tumors were generated and registered to establish vertex-wise correspondence. To assess tumor response, Mahalanobis distances (M_dist|_Fusion) between pre- and post-RT tumor surfaces with anatomic and metabolic fused vectors were calculated for each patient. M_dist|_Fusion was evaluated by overall survival (OS) prediction and survival risk classification. As a comparison, the same analyses were conducted on traditional imaging/physiological predictors, and distances measurements based on metabolic and morphological features only.

Results

Among all the imaging/physiological parameters, M_dist|_Fusion was shown to be the best predictor of OS (HR = 0.52, p = 0.008), while other parameters failed to reach significance. Moreover, M_dist|_Fusion outperformed traditional morphologic and metabolic measurements in patient risk stratification, either alone (HR = 11.51, p < 0.001) or combined with age (HR = 9.04, p < 0.001).

Conclusions

We introduced a PET/CT-based novel morphologic and metabolic pipeline for response evaluation in locally advanced PA. The fused M_dist|_Fusion outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction and risk stratification. The novel fusion model may serve as a new imaging-marker to more accurately characterize the heterogeneous tumor RT response.

Discriminating lung adenocarcinoma from lung squamous cell carcinoma using respiration-induced tumor shape changes

Based on 4D-CT, we aimed to characterize the pattern of morphological changes in lung tumors during respiration, and investigated its potential in non-invasively differentiating lung adenocarcinoma (AC) and squamous cell carcinoma (SCC). We applied a 3D surface analysis on 22 tumors (13 AC, 9 SCC) to investigate the tumor regional morphological fluctuations in response to respiration phases. Tumor surface vertices among ten respiratory phases were matched using surface-based registration, and the shape descriptors (ρ and detJ) were calculated and tracked across respiration stages in a regionally aligned scenario. Pair-wise group comparisons were performed between lung AC and SCC subtypes, in terms of ratios of maximal shape changes as well as correlation coefficients between tumor shape and respiratory stage indicators from the lung. AC type tumors had averaged larger surface measurements at exhale than at inhale, and these surface measurements were negatively correlated with lung volumes across respiratory stages. In contrast, SCC type tumors had averaged smaller surface measurements at exhale than at inhale, and the correlations with lung volumes were positive. The group differences in maximal shape changes as well as correlations were both statistically significant (p  <  0.05). We developed a non-invasive lung tumor sub-type detection pipeline based on respiration-induced tumor surface deformation. Significant differences in deformation patterns were detected between lung AC and SCC. The derived surface measurements may potentially serve as a new non-invasive imaging biomarker of lung cancer subtypes.

A Generic Sure Independence Screening Procedure

Extracting important features from ultra-high dimensional data is one of the primary tasks in statistical learning, information theory, precision medicine and biological discovery. Many of the sure independent screening methods developed to meet these needs are suitable for special models under some assumptions. With the availability of more data types and possible models, a model-free generic screening procedure with fewer and less restrictive assumptions is desirable. In this paper, we propose a generic nonparametric sure independence screening procedure, called BCor-SIS, on the basis of a recently developed universal dependence measure: Ball correlation. We show that the proposed procedure has strong screening consistency even when the dimensionality is an exponential order of the sample size without imposing sub-exponential moment assumptions on the data. We investigate the flexibility of this procedure by considering three commonly encountered challenging settings in biological discovery or precision medicine: iterative BCor-SIS, interaction pursuit, and survival outcomes. We use simulation studies and real data analyses to illustrate the versatility and practicability of our BCor-SIS method.

Isometry Invariant Shape Descriptors for Abnormality Detection on Brain Surfaces Affected by Alzheimer's Disease

Alzheimer's disease (AD), a progressive brain disorder, is the most common neurodegenerative disease in older adults. There is a need for brain structural magnetic resonance imaging (MRI) biomarkers to help assess AD progression and intervention effects. Prior research showed that surface based brain imaging features hold great promise as efficient AD biomarkers. However, the complex geometry of cortical surfaces poses a major challenge to defining such a feature that is sensitive in qualification, robust in analysis, and intuitive in visualization. Here we propose a novel isometry invariant shape descriptor for brain morphometry analysis. First, we calculate a global area-preserving mapping from cortical surface to the unit sphere. Based on the mapping, the Beltrami coefficient shape descriptor is calculated. An analysis of average shape descriptors reveals that our detected features are consistent with some previous AD studies where medial temporal lobe volume was identified as an important AD imaging biomarker. We further apply a novel patch-based spherical sparse coding scheme for feature dimension reduction. Later, a support vector machine (SVM) classifier is applied to discriminate 135 amyloid-beta positive persons with the clinical diagnosis of Mild Cognitive Impairment (MCI) from 248 amyloid-beta-negative normal control subjects. The 5-folder cross-validation accuracy is about 81.82\% on the dataset, outperforming some traditional, Freesurfer based, brain surface features. The results show that our shape descriptor is effective in distinguishing dementia due to AD from age-matched normal aging individuals. Our isometry invariant shape descriptors may provide a unique and intuitive way to inspect cortical surface and its morphometry changes.

HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (Aβ) in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid Aβ information. In this work, we study hippocampus morphometry on a cohort consisting of Aβ positive AD (N = 151) and matched Aβ negative cognitively unimpaired subjects (N = 271) with Aβ positivity determined via florbetapir PET. The brain images are obtained from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI). We compute our surface multivariate morphometry statistics from segmented hippocampus structure in structural MR images. With these features, we find statistically significant difference by using Hotelling's T² tests. Meanwhile, we apply a patch-based analysis of sparse coding system for binary group classification and achieve an accuracy rate of 90.48%. Our results demonstrate that our surface multivariate morphometry statistics (MMS) perform better than traditional hippocampal volume measures in classification and it may be applied as a potential biomarker for distinguishing dementia due to AD from age matched normal aging individuals.

A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology

In this paper, we present a fully-automated subcortical and ventricular shape generation pipeline that acts on structural magnetic resonance images (MRIs) of the human brain. Principally, the proposed pipeline consists of three steps: (1) automated structure segmentation using the diffeomorphic multi-atlas likelihood-fusion algorithm; (2) study-specific shape template creation based on the Delaunay triangulation; (3) deformation-based shape filtering using the large deformation diffeomorphic metric mapping for surfaces. The proposed pipeline is shown to provide high accuracy, sufficient smoothness, and accurate anatomical topology. Two datasets focused upon Huntington's disease (HD) were used for evaluating the performance of the proposed pipeline. The first of these contains a total of 16 MRI scans, each with a gold standard available, on which the proposed pipeline's outputs were observed to be highly accurate and smooth when compared with the gold standard. Visual examinations and outlier analyses on the second dataset, which contains a total of 1,445 MRI scans, revealed 100% success rates for the putamen, the thalamus, the globus pallidus, the amygdala, and the lateral ventricle in both hemispheres and rates no smaller than 97% for the bilateral hippocampus and caudate. Another independent dataset, consisting of 15 atlas images and 20 testing images, was also used to quantitatively evaluate the proposed pipeline, with high accuracy having been obtained. In short, the proposed pipeline is herein demonstrated to be effective, both quantitatively and qualitatively, using a large collection of MRI scans.

Changes in the Hippocampal Volume and Shape in Early-Onset Mild Cognitive Impairment

OBJECTIVE

The aim of this study was to examine the change in the hippocampal volume and shape in early-onset mild cognitive impairment (EO-MCI) associated with the APOE ε4 carrier state.

METHODS

This study had 50 subjects aged 55-63 years, all of whom were diagnosed with MCI at baseline via the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet. The EO-MCI patients were divided into the MCI continued (MCIcont) and Alzheimer's disease (AD) converted (ADconv) groups 2 years later. The hippocampal volume and shape were measured for all the subjects. The local shape analysis (LSA) was used to conduct based on the 2-year-interval magnetic resonance imaging scans.

RESULTS

There was a significant correlation between APOE ε4 allele and hippocampal volume atrophy. Over two years, the volume reduction in the left hippocampus was found to be faster than that in the right hippocampus, especially in the APOE ε4 carriers. LSA showed that the 2 subfields were significantly affected in the left hippocampus.

CONCLUSION

These results suggest that the possession of APOE ε4 allele may lead to greater predilection for left hippocampal atrophy in EO-MCI, and some specific subfields of the hippocampus may be more prominently involved.

Hierarchical Subcortical Sub-Regional Shape Network Analysis in Alzheimer's Disease

In this paper, by utilizing surface diffeomorphic deformations, we constructed and analyzed subcortical shape morphometric networks in 210 healthy control (HC) subjects and 175 subjects with Alzheimer's disease (AD), aiming to identify AD-induced abnormalities in the subcortical shape network. We quantitatively analyzed pertinent network attributes of the entire network and each node. Further to this, hierarchical analyses were performed; group comparisons were conducted at the structure level first and then the sub-region level. The bilateral amygdalae, hippocampi, as well as the thalamus were all divided into multiple functionally distinct sub-regions. From the structure level analysis, we found significant HC-vs-AD group differences in the average local efficiency and average global efficiency. In addition, the local nodal efficiencies between the right thalamus and all three of the right hippocampus, right amygdala, and left thalamus, as well as that between the left amygdala and left hippocampus, decreased significantly in AD. According to the sub-regional network analyses, we observed significant AD-induced local efficiency decreases between different sub-regions within the right hippocampus itself and between the subiculum of the right hippocampus and the sub-region of the right thalamus connecting to the temporal lobe, indicating a degradation of circuit between the hippocampus, thalamus, and temporal lobe. Statistical comparisons were performed using 40,000 non-parametric permutation tests, with false discovery rate correction employed for multiple comparison correction.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry

INTRODUCTION

Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.

METHODS

Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces.

RESULTS

We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted.

CONCLUSIONS

By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease

Functional data are increasingly collected in public health and medical studies to better understand many complex diseases. Besides the functional data, other clinical measures are often collected repeatedly. Investigating the association between these longitudinal data and time to a survival event is of great interest to these studies. In this article, we develop a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. The parameters of FJM are estimated in a maximum likelihood framework via expectation maximization algorithm. The proposed FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. The FJM is evaluated by a simulation study and is applied to the Alzheimer's Disease Neuroimaging Initiative study, a motivating clinical study testing whether serial brain imaging, clinical, and neuropsychological assessments can be combined to measure the progression of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.