Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change

Statistical shape analysis of corpus callosum in delusional disorder

Neurobiological foundations of delusional disorder (DD) have been studied less with neuroimaging techniques when compared to other psychotic disorders. The present study aimed to delineate the neural substrates of DD by investigating neuroanatomical characteristics of the corpus callosum (CC) with statistical shape analysis (SSA) conducted on magnetic resonance images (MRI). Twenty (female:male=1:1) DSM-5 DD patients and 20 age- and gender-matched healthy individuals were included. High-resolution 3D T1 Turbo Field Echo MRI images were scanned with a 1.5 T MR device. The landmarks that were selected to determine the shape differences in CC were identified based on previous studies. Furthermore, constructed landmarks were determined and employed to better assess regional shape differences. There was no significant difference in the CC area in the mid-sagittal images between the DD patients and controls. However, DD patients exhibited a pattern of structural CC changes in various regions. The study findings emphasizes the variable subregional nature of CC in DD patients. Future SSA studies with larger samples could shed further light on DD etiology, diagnosis, classification and treatment options.

Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.

Shape features of working memory-related deep-brain regions differentiate high and low community functioning in schizophrenia

We have previously shown that schizophrenia (SCZ) participants with high community functioning demonstrate better verbal working memory (vWM) performance relative to those with low community functioning. In the present study, we investigated whether neuroanatomical differences in regions supporting vWM also exist between schizophrenia groups that vary on community functioning. Utilizing magnetic resonance imaging, shape features of deep-brain nuclei known to be involved in vWM were calculated in samples of high functioning (HF-SCZ, n = 23) and low functioning schizophrenia participants (LF-SCZ, n = 18), as well as in a group of healthy control participants (CON, n = 45). Large deformation diffeomorphic metric mapping was employed to characterize surface anatomy of the caudate nucleus, globus pallidus, hippocampus, and thalamus. Statistical analyses involved linear mixed-effects models and vertex-wise contrast mapping to assess between-group differences in structural shape features, and Pearson correlations to evaluate relationships between shape metrics and vWM performance. We found significant between-group main effects in deep-brain surface anatomy across all structures. Post-hoc comparisons revealed HF-SCZ and LF-SCZ groups significantly differed on both caudate and hippocampal shape, however, significant correlations with vWM were only observed in hippocampal shape for both SCZ groups. Specifically, more abnormal hippocampal deformation was associated with lower vWM suggesting hippocampal shape is both a neural substrate for vWM deficits and a potential biomarker to predict or monitor the efficacy of cognitive rehabilitation. These findings add to a growing body of literature related to functional outcomes in schizophrenia by demonstrating unique shape patterns across the spectrum of community functioning in SCZ.

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Deep-brain abnormalities are present in patients regardless of functional severity.

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Caudate and hippocampal shape differ between community functioning-based groups.

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Verbal working memory relates to hippocampal shape in both patient groups.

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.

Thalamic Shape Abnormalities Differentially Relate to Cognitive Performance in Early-Onset and Adult-Onset Schizophrenia

Early-onset schizophrenia (EOS) shares many biological and clinical features with adult-onset schizophrenia (AOS), but may represent a unique subgroup with greater susceptibility for disease onset and worsened symptomatology and progression, which could potentially derive from exaggerated neurodevelopmental abnormalities. Neurobiological explanations of schizophrenia have emphasized the involvement of deep-brain structures, particularly alterations of the thalamus, which have been linked to core features of the disorder. The aim of this study was to compare thalamic shape abnormalities between EOS and AOS subjects and determine whether unique behavioral profiles related to these differences. It was hypothesized abnormal thalamic shape would be observed in anterior, mediodorsal and pulvinar regions in both schizophrenia groups relative to control subjects, but exacerbated in EOS. Magnetic resonance T1-weighted images were collected from adult individuals with EOS (n = 28), AOS (n = 33), and healthy control subjects (n = 60), as well as collection of clinical and cognitive measures. Large deformation high-dimensional brain mapping was used to obtain three-dimensional surfaces of the thalamus. General linear models were used to compare groups on surface shape features, and Pearson correlations were used to examine relationships between thalamic shape and behavioral measures. Results revealed both EOS and AOS groups demonstrated significant abnormal shape of anterior, lateral and pulvinar thalamic regions relative to CON (all p < 0.007). Relative to AOS, EOS exhibited exacerbated abnormalities in posterior lateral, mediodorsal and lateral geniculate thalamic regions (p = 0.003). Thalamic abnormalities related to worse episodic memory in EOS (p = 0.03) and worse working memory (p = 0.047) and executive functioning (p = 0003) in AOS. Overall, findings suggest thalamic abnormalities are a prominent feature in both early- and late-onset schizophrenia, but exaggerated in EOS and have different brain-behavior profiles for each. The persistence of these abnormalities in adult EOS patients suggests they may represent markers of disrupted neurodevelopment that uniquely relate to the clinical and cognitive aspects of the illness.

Surface-based abnormalities of the executive frontostriatial circuit in pediatric TBI

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Cortical thickness of the dorsolateral prefrontal cortex is reduced in pediatric TBI.

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Shape abnormalities of the caudate and mediodorsal nucleus of the thalamus are a feature of pediatric TBI.

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Surface-based abnormalities of the dorsolateral prefrontal loop do not appear to relate to executive functioning.

Childhood traumatic brain injury (TBI) is one of the most common causes of acquired disability and has significant implications for executive functions (EF), such as impaired attention, planning, and initiation that are predictive of everyday functioning. Evidence has suggested attentional features of executive functioning require behavioral flexibility that is dependent on frontostriatial circuitry. The purpose of this study was to evaluate surface-based deformation of a specific frontostriatial circuit in pediatric TBI and its role in EF. Regions of interest included: the dorsolateral prefrontal cortex (DLPFC), caudate nucleus, globus pallidus, and the mediodorsal nucleus of the thalamus (MD). T1-weighted magnetic resonance images were obtained in a sample of children ages 8–13 with complicated mild, moderate, or severe TBI (n = 32) and a group of comparison children with orthopedic injury (OI; n = 30). Brain regions were characterized using high-dimensional surface-based brain mapping procedures. Aspects of EF were assessed using select subtests from the Test of Everyday Attention for Children (TEA-Ch). General linear models tested group and hemisphere differences in DLPFC cortical thickness and subcortical shape of deep-brain regions; Pearson correlations tested relationships with EF. Main effects for group were found in both cortical thickness of the DLPFC (F_1,60 = 4.30, p = 0.042) and MD mean deformation (F_1,60 = 6.50, p = 0.01) all with lower values in the TBI group. Statistical surface maps revealed significant inward deformation on ventral-medial aspects of the caudate in TBI relative to OI, but null results in the globus pallidus. No significant relationships between EF and any region of interest were observed. Overall, findings revealed abnormalities in multiple aspects of a frontostriatial circuit in pediatric TBI, which may reflect broader pathophysiological mechanisms. Increased consideration for the role of deep-brain structures in pediatric TBI can aid in the clinical characterization of anticipated long-term developmental effects of these individuals.

Basal ganglia shape features differentiate schizoaffective disorder from schizophrenia

There is growing evidence that schizophrenia and schizoaffective disorder represent closely related syndromes that vary in severity along a neurobiological continuum. In the present study, volume and shape of the basal ganglia was examined in people with schizophrenia and schizoaffective disorder relative to healthy controls and hypothesized that unique neuroanatomical differences would be observed in each patient group. Magnetic resonance 1.5T images were obtained from schizophrenia (n = 47), schizoaffective disorder (n = 15), and from healthy control (n = 42) participants, matched for age, gender, parental socioeconomic status, and race. The caudate, putamen, and globus pallidus were characterized using high-dimensional brain mapping procedures (Csernansky et al., 2004b). Results revealed significant shape deformations between schizophrenia and schizoaffective disorder that also differed from control subjects. Relative to schizophrenia, schizoaffective subjects showed exaggerated inward deformations indicative of localized volume loss in subregions of the caudate, putamen, and globus pallidus (all p < 0.001). These shape features correlated with mental flexibility and negative symptoms in schizophrenia (all p < 0.05), but not schizoaffective disorder. To the extent that differences in important basal ganglia substructures reflect biological heterogeneity among these two psychotic illnesses, this data could prove useful in improving diagnostic precision, as well as informing the affective component of mental illness.

Outward subcortical curvature associated with sub-clinical depression symptoms in adolescents

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We related subcortical morphology to subthreshold depression (StD) in adolescents.

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StD had mostly positively associations (outward shape associated with higher StD).

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StD associated with outward hippocampal and amygdala morphology in females (N = 160).

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And outward hippocampal, thalamic, and basal ganglia morphology in males (N = 96).

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Pro-inflammatory cytokines did not mediate these relationships.

Objective

Subclinical or subthreshold depressive symptoms (StD) are frequent in adolescence and are related to suicidality and onset of depression in adulthood, however, their neurobiology is poorly understood. We examined the relationship between StD and subcortical grey matter structures in unmedicated adolescents with no history of axis I diagnosis.

Methods

277 youths from Chicago aged 14 years participated, undergoing a structural MRI scan and completing the Revised Children's Anxiety and Depression Scale (RCADS). Blood samples provided a composite of five pro-inflammatory cytokines. Regions of interest (ROI) for vertex-based surface analysis were the left and right amygdala, hippocampus, thalamus, caudate, nucleus accumbens, pallidum and putamen. Covariates were age, pubertal status, socioeconomic disadvantage and intracranial volume. Males and females were analysed separately.

Results

StD had positive associations (outward shape) with subcortical morphology in the right amygdala and left hippocampus in females, and the bilateral putamen and the left caudate, hippocampus and thalamus in males. However, we also found negative associations with StD (inward contractions) in the hippocampus in females and the caudate in males. Pro-inflammatory cytokines did not mediate the relationship between StD and outward morphology or volume.

Conclusion

This is one of the first studies to examine subcortical morphology of basal ganglia and thalamic regions related to StD in adolescents, and the first study to report mostly positive associations between StD, volume and outward morphology in youths. These findings could reflect intact neurogenesis or resilience to depression, however longitudinal research is needed to further understand the neurobiology of StD in adolescents.

Subcortical structural variations associated with low socioeconomic status in adolescents

Low socioeconomic status (SES) is associated with a higher probability of multiple exposures (e.g., neighborhood violence, poor nutrition, housing instability, air pollution, and insensitive caregiving) known to affect structural development of subcortical brain regions that subserve threat and reward processing, however, few studies have examined the relationship between SES and such subcortical structures in adolescents. We examined SES variations in volume and surface morphometry of subcortical regions. The sample comprised 256 youth in eighth grade (mean age = 13.9 years), in whom high dimensional deformation mapping of structural 3T magnetic resonance imaging scans was performed. Vertex‐wise linear regression analyses examined associations between income to poverty ratio and surfaces of the hippocampus, amygdala, thalamus, caudate, putamen, nucleus accumbens and pallidum, with the covariates age, pubertal status, and intracranial volume. Given sex differences in pubertal development and subcortical maturation at this age, the analyses were stratified by sex. Among males, who at this age average an earlier pubertal stage than females, the relationship between SES and local shape variation in subcortical regions was almost entirely positive. For females, the relationship between SES and local shape variation was negative. Racial identity was associated with SES in our sample, however supplementary analyses indicated that most of the associations between SES and subcortical structure were independent of it. Although these cross‐sectional results are not definitive, they are consistent with a scenario where low SES delays structural maturation of subcortical regions involved with threat and reward processing. Future longitudinal studies are needed to test this hypothesis.

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach

Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.

In vivo hippocampal subfield shape related to TDP-43, amyloid beta, and tau pathologies

Despite advances in the development of biomarkers for Alzheimer's disease (AD), accurate ante-mortem diagnosis remains challenging because a variety of neuropathologic disease states can coexist and contribute to the AD dementia syndrome. Here, we report a neuroimaging study correlating hippocampal deformity with regional AD and transactive response DNA-binding protein of 43 kDA pathology burden. We used hippocampal shape analysis of ante-mortem T1-weighted structural magnetic resonance imaging images of 42 participants from two longitudinal cohort studies conducted by the Rush Alzheimer's Disease Center. Surfaces were generated for the whole hippocampus and zones approximating the underlying subfields using a previously developed automated image-segmentation pipeline. Multiple linear regression models were constructed to correlate the shape with pathology measures while accounting for covariates, with relationships mapped out onto hippocampal surface locations. A significant relationship existed between higher paired helical filaments-tau burden and inward hippocampal shape deformity in zones approximating CA1 and subiculum which persisted after accounting for coexisting pathologies. No significant patterns of inward surface deformity were associated with amyloid-beta or transactive response DNA-binding protein of 43 kDA after including covariates. Our findings indicate that hippocampal shape deformity measures in surface zones approximating CA1 may represent a biomarker for postmortem AD pathology.

Systematic comparison of different techniques to measure hippocampal subfield volumes in ADNI2

Objective

Subfield-specific measurements provide superior information in the early stages of neurodegenerative diseases compared to global hippocampal measurements. The overall goal was to systematically compare the performance of five representative manual and automated T1 and T2 based subfield labeling techniques in a sub-set of the ADNI2 population.

Methods

The high resolution T2 weighted hippocampal images (T2-HighRes) and the corresponding T1 images from 106 ADNI2 subjects (41 controls, 57 MCI, 8 AD) were processed as follows. A. T1-based: 1. Freesurfer + Large-Diffeomorphic-Metric-Mapping in combination with shape analysis. 2. FreeSurfer 5.1 subfields using in-vivo atlas. B. T2-HighRes: 1. Model-based subfield segmentation using ex-vivo atlas (FreeSurfer 6.0). 2. T2-based automated multi-atlas segmentation combined with similarity-weighted voting (ASHS). 3. Manual subfield parcellation. Multiple regression analyses were used to calculate effect sizes (ES) for group, amyloid positivity in controls, and associations with cognitive/memory performance for each approach.

Results

Subfield volumetry was better than whole hippocampal volumetry for the detection of the mild atrophy differences between controls and MCI (ES: 0.27 vs 0.11). T2-HighRes approaches outperformed T1 approaches for the detection of early stage atrophy (ES: 0.27 vs.0.10), amyloid positivity (ES: 0.11 vs 0.04), and cognitive associations (ES: 0.22 vs 0.19).

Conclusions

T2-HighRes subfield approaches outperformed whole hippocampus and T1 subfield approaches. None of the different T2-HghRes methods tested had a clear advantage over the other methods. Each has strengths and weaknesses that need to be taken into account when deciding which one to use to get the best results from subfield volumetry.

Primal-dual convex optimization in large deformation diffeomorphic metric mapping: LDDMM meets robust regularizers

This paper proposes a method for primal-dual convex optimization in variational large deformation diffeomorphic metric mapping problems formulated with robust regularizers and robust image similarity metrics. The method is based on Chambolle and Pock primal-dual algorithm for solving general convex optimization problems. Diagonal preconditioning is used to ensure the convergence of the algorithm to the global minimum. We consider three robust regularizers liable to provide acceptable results in diffeomorphic registration: Huber, V-Huber and total generalized variation. The Huber norm is used in the image similarity term. The primal-dual equations are derived for the stationary and the non-stationary parameterizations of diffeomorphisms. The resulting algorithms have been implemented for running in the GPU using Cuda. For the most memory consuming methods, we have developed a multi-GPU implementation. The GPU implementations allowed us to perform an exhaustive evaluation study in NIREP and LPBA40 databases. The experiments showed that, for all the considered regularizers, the proposed method converges to diffeomorphic solutions while better preserving discontinuities at the boundaries of the objects compared to baseline diffeomorphic registration methods. In most cases, the evaluation showed a competitive performance for the robust regularizers, close to the performance of the baseline diffeomorphic registration methods.

Discrete pre-processing step effects in registration-based pipelines, a preliminary volumetric study on T1-weighted images

Pre-processing MRI scans prior to performing volumetric analyses is common practice in MRI studies. As pre-processing steps adjust the voxel intensities, the space in which the scan exists, and the amount of data in the scan, it is possible that the steps have an effect on the volumetric output. To date, studies have compared between and not within pipelines, and so the impact of each step is unknown. This study aims to quantify the effects of pre-processing steps on volumetric measures in T1-weighted scans within a single pipeline. It was our hypothesis that pre-processing steps would significantly impact ROI volume estimations. One hundred fifteen participants from the OASIS dataset were used, where each participant contributed three scans. All scans were then pre-processed using a step-wise pipeline. Bilateral hippocampus, putamen, and middle temporal gyrus volume estimations were assessed following each successive step, and all data were processed by the same pipeline 5 times. Repeated-measures analyses tested for a main effects of pipeline step, scan-rescan (for MRI scanner consistency) and repeated pipeline runs (for algorithmic consistency). A main effect of pipeline step was detected, and interestingly an interaction between pipeline step and ROI exists. No effect for either scan-rescan or repeated pipeline run was detected. We then supply a correction for noise in the data resulting from pre-processing.

Subtle hippocampal deformities in breast cancer survivors with reduced episodic memory and self-reported cognitive concerns

Cancer survivors have lingering cognitive problems, however the anatomical basis for these problems has yet to be fully elucidated. Clinical studies as well as animal models of chemotherapy have pinpointed cell and volume loss to the hippocampus, however, few studies have performed shape analysis of the hippocampus on cancer survivors. This study used high-dimensional deformation mapping analysis to test whether localized hippocampal deformation differs in breast cancer survivors who received adjuvant chemotherapy coupled with hormone blockade therapy, and if deformation was related to subjective self-reported concerns and cognitive performance. 3 T MRI images were acquired from 16 pre-menopausal breast cancer survivors and 18 healthy controls without a history of cancer. Breast cancer survivors had undergone chemotherapy within the eighteen months prior to the study, and were receiving estrogen-blockade therapy at the time of the study. Automated high-dimensional deformation mapping was used to compare localized hippocampal deformation differences between groups. Self-reported subjective concerns were assessed using Neuro-QOL Cognitive Function assessment, whereas cognitive performance was evaluated using the NIH Toolbox Cognition Battery. Relative to healthy controls, cancer survivors showed significantly more inward hippocampal deformation, worse self-reported cognitive functioning, and inferior episodic memory test score. This study is the first of its kind to examine the relationship between hippocampal deformity and cognitive impairment in cancer survivors.

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Cancer survivors demonstrated significant inward hippocampal deformation.

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Survivors self-reported worse cognitive functioning.

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Survivors performed worse than controls on a test of episodic memory.

Progressive deterioration of thalamic nuclei relates to cortical network decline in schizophrenia

Thalamic abnormalities are considered part of the complex pathophysiology of schizophrenia, particularly the involvement of specific thalamic nuclei. The goals of this study were to: introduce a novel atlas-based parcellation scheme for defining various thalamic nuclei; compare their integrity in a schizophrenia sample against healthy individuals at baseline and follow-up time points, as well as rates of change over time; examine relationships between the nuclei and abnormalities in known connected cortical regions; and finally, to determine if schizophrenia-related thalamic nuclei changes relate to cognitive functioning and clinical symptoms. Subjects were from a larger longitudinal 2-year follow-up study, schizophrenia (n=20) and healthy individuals (n=20) were group-matched for age, gender, and recent-alcohol use. We used high-dimensional brain mapping to obtain thalamic morphology, and applied a novel atlas-based method for delineating anterior, mediodorsal, and pulvinar nuclei. Results from cross sectional GLMs revealed group differences in bilateral mediodorsal and anterior nuclei, while longitudinal models revealed significant group-by-time interactions for the mediodorsal and pulvinar nuclei. Cortical correlations were the strongest for the pulvinar in frontal, temporal and parietal regions, followed by the mediodorsal nucleus in frontal regions, but none in the anterior nucleus. Thalamic measures did not correlate with cognitive and clinical scores at any time point or longitudinally. Overall, findings revealed a pattern of persistent progressive abnormalities in thalamic nuclei that relate to advancing cortical decline in schizophrenia, but not with measures of behavior.

Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson

The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.

Morphometric analysis of amygdla and hippocampus shape in impulsively aggressive and healthy control subjects

BACKGROUND

Impulsive aggressive behavior is thought to be facilitated by activation of the limbic brain, particularly the amygdala and hippocampus., Functional imaging studies suggest abnormalities in limbic brain activity during emotional information processing in impulsively aggressive subjects with Intermittent Explosive Disorder (IED). It is not known if IED is associated with altered amygdala and hippocampus volume and shape.

METHODS

We examined the volume and shape of the amygdala-hippocampal complex, using morphometric analysis of high resolution structural 3T MR scans in healthy control (HC: n = 73) subjects without history of Axis I or II psychiatric conditions and in subjects with IED (n = 67).

RESULTS

While no volume differences were observed between HC and IED subjects, a significant level of morphometric deformation, suggestive of cell loss, in both amygdala and hippocampal structures was observed bilaterally in IED subjects. Analysis of a canonical variable that used the first 10 eigenvectors from both sides of the brain revealed that these morphometric deformations in the IED subjects were not due the presence of confounding variables or to comorbidities among IED subjects.

CONCLUSIONS

These data reveal that IED is associated with a significant loss of neurons in both the amygdala and hippocampus. These changes may play a role in the functional abnormalities observed in previous fMRI studies and in the pathophysiology of impulsive aggressive behavior.

Multimodal neuroimaging computing: the workflows, methods, and platforms

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration

In this paper, we describe an instance of the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), a schizophrenia-related dataset hosted at XNAT Central, and the SchizConnect data portal used for accessing and sharing the dataset. NUSDAST was built and extended upon existing, standard schemas available for data sharing on XNAT Central (http://central.xnat.org/). With the creation of SchizConnect, we were able to link NUSDAST to other neuroimaging data sources and create a powerful, federated neuroimaging resource.

Fast approximate quadratic programming for graph matching

Quadratic assignment problems arise in a wide variety of domains, spanning operations research, graph theory, computer vision, and neuroscience, to name a few. The graph matching problem is a special case of the quadratic assignment problem, and graph matching is increasingly important as graph-valued data is becoming more prominent. With the aim of efficiently and accurately matching the large graphs common in big data, we present our graph matching algorithm, the Fast Approximate Quadratic assignment algorithm. We empirically demonstrate that our algorithm is faster and achieves a lower objective value on over 80% of the QAPLIB benchmark library, compared with the previous state-of-the-art. Applying our algorithm to our motivating example, matching C. elegans connectomes (brain-graphs), we find that it efficiently achieves performance.

Cannabis-related episodic memory deficits and hippocampal morphological differences in healthy individuals and schizophrenia subjects

Cannabis use has been associated with episodic memory (EM) impairments and abnormal hippocampus morphology among both healthy individuals and schizophrenia subjects. Considering the hippocampus' role in EM, research is needed to evaluate the relationship between cannabis-related hippocampal morphology and EM among healthy and clinical groups. We examined differences in hippocampus morphology between control and schizophrenia subjects with and without a past (not current) cannabis use disorder (CUD). Subjects group-matched on demographics included 44 healthy controls (CON), 10 subjects with a CUD history (CON-CUD), 28 schizophrenia subjects with no history of substance use disorders (SCZ), and 15 schizophrenia subjects with a CUD history (SCZ-CUD). Large-deformation, high-dimensional brain mapping with MRI produced surface-based representations of the hippocampus that were compared across all four groups and correlated with EM and CUD history. Surface maps of the hippocampus were generated to visualize morphological differences. CON-CUD and SCZ-CUD were characterized by distinct cannabis-related hippocampal shape differences and parametric deficits in EM performance. Shape differences observed in CON-CUD were associated with poorer EM performance, while shape differences observed in SCZ-CUD were associated with a longer duration of CUD and shorter duration of CUD remission. A past history of CUD may be associated with notable differences in hippocampal morphology and EM impairments among adults with and without schizophrenia. Although the results may be compatible with a causal hypothesis, we must consider that the observed cannabis-related shape differences in the hippocampus could also be explained as biomarkers of a neurobiological susceptibility to poor memory or the effects of cannabis.

Integrated cortical structural marker for Alzheimer's disease

In this article, we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild Alzheimer's disease (AD). FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka "sulcal depth"), and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes and metric distortion, which reflects white matter surface area changes. The classifier integrating all 3 types of surface measures significantly improved classification performance compared with classification based on single measures. The principal component analysis-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns.

Gauss-Newton inspired preconditioned optimization in large deformation diffeomorphic metric mapping

In this work, we propose a novel preconditioned optimization method in the paradigm of Large Deformation Diffeomorphic Metric Mapping (LDDMM). The preconditioned update scheme is formulated for the non-stationary and the stationary parameterizations of diffeomorphisms, yielding three different LDDMM methods. The preconditioning matrices are inspired in the Hessian approximation used in Gauss-Newton method. The derivatives are computed using Frechet differentials. Thus, optimization is performed in a Sobolev space, in contrast to optimization in L(2) commonly used in non-rigid registration literature. The proposed LDDMM methods have been evaluated and compared with their respective implementations of gradient descent optimization. Evaluation has been performed using real and simulated images from the Non-rigid Image Registration Evaluation Project (NIREP). The experiments conducted in this work reported that our preconditioned LDDMM methods achieved a performance similar or superior to well-established-in-literature gradient descent non-stationary LDDMM in the great majority of cases. Moreover, preconditioned optimization showed a substantial reduction in the execution time with an affordable increase of the memory usage per iteration. Additional experiments reported that optimization using Frechet differentials should be preferable to optimization using L(2) differentials.

Cannabis-related working memory deficits and associated subcortical morphological differences in healthy individuals and schizophrenia subjects

Cannabis use is associated with working memory (WM) impairments; however, the relationship between cannabis use and WM neural circuitry is unclear. We examined whether a cannabis use disorder (CUD) was associated with differences in brain morphology between control subjects with and without a CUD and between schizophrenia subjects with and without a CUD, and whether these differences related to WM and CUD history. Subjects group-matched on demographics included 44 healthy controls, 10 subjects with a CUD history, 28 schizophrenia subjects with no history of substance use disorders, and 15 schizophrenia subjects with a CUD history. Large-deformation high-dimensional brain mapping with magnetic resonance imaging was used to obtain surface-based representations of the striatum, globus pallidus, and thalamus, compared across groups, and correlated with WM and CUD history. Surface maps were generated to visualize morphological differences. There were significant cannabis-related parametric decreases in WM across groups. Similar cannabis-related shape differences were observed in the striatum, globus pallidus, and thalamus in controls and schizophrenia subjects. Cannabis-related striatal and thalamic shape differences correlated with poorer WM and younger age of CUD onset in both groups. Schizophrenia subjects demonstrated cannabis-related neuroanatomical differences that were consistent and exaggerated compared with cannabis-related differences found in controls. The cross-sectional results suggest that both CUD groups were characterized by WM deficits and subcortical neuroanatomical differences. Future longitudinal studies could help determine whether cannabis use contributes to these observed shape differences or whether they are biomarkers of a vulnerability to the effects of cannabis that predate its misuse.

Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease

BACKGROUND

The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus.

METHODS

The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change (ΔMTL). Baseline and 24 months 3D T1-weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools.

RESULTS

The average ΔMTL (mean±SEM) was 68±35mm(3) in NEC, 187±38mm(3) in MCI and 300±34mm(3) in the AD group and was significantly different (p<0.0001) between all three groups. The ΔMTL was correlated with cognitive decline.

COMPARISON WITH EXISTING METHOD(S)

Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups.

CONCLUSION

This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups.

Northwestern University Schizophrenia Data and Software Tool (NUSDAST)

The schizophrenia research community has invested substantial resources on collecting, managing and sharing large neuroimaging datasets. As part of this effort, our group has collected high resolution magnetic resonance (MR) datasets from individuals with schizophrenia, their non-psychotic siblings, healthy controls and their siblings. This effort has resulted in a growing resource, the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), an NIH-funded data sharing project to stimulate new research. This resource resides on XNAT Central, and it contains neuroimaging (MR scans, landmarks and surface maps for deep subcortical structures, and FreeSurfer cortical parcellation and measurement data), cognitive (cognitive domain scores for crystallized intelligence, working memory, episodic memory, and executive function), clinical (demographic, sibling relationship, SAPS and SANS psychopathology), and genetic (20 polymorphisms) data, collected from more than 450 subjects, most with 2-year longitudinal follow-up. A neuroimaging mapping, analysis and visualization software tool, CAWorks, is also part of this resource. Moreover, in making our existing neuroimaging data along with the associated meta-data and computational tools publically accessible, we have established a web-based information retrieval portal that allows the user to efficiently search the collection. This research-ready dataset meaningfully combines neuroimaging data with other relevant information, and it can be used to help facilitate advancing neuroimaging research. It is our hope that this effort will help to overcome some of the commonly recognized technical barriers in advancing neuroimaging research such as lack of local organization and standard descriptions.

A pilot study of basal ganglia and thalamus structure by high dimensional mapping in children with Tourette syndrome

Background: Prior brain imaging and autopsy studies have suggested that structural abnormalities of the basal ganglia (BG) nuclei may be present in Tourette Syndrome (TS). These studies have focused mainly on the volume differences of the BG structures and not their anatomical shapes.  Shape differences of various brain structures have been demonstrated in other neuropsychiatric disorders using large-deformation, high dimensional brain mapping (HDBM-LD).  A previous study of a small sample of adult TS patients demonstrated the validity of the method, but did not find significant differences compared to controls. Since TS usually begins in childhood and adult studies may show structure differences due to adaptations, we hypothesized that differences in BG and thalamus structure geometry and volume due to etiological changes in TS might be better characterized in children.

Objective: Pilot the HDBM-LD method in children and estimate effect sizes.

Methods: In this pilot study, T1-weighted MRIs were collected in 13 children with TS and 16 healthy, tic-free, control children. The groups were well matched for age.  The primary outcome measures were the first 10 eigenvectors which are derived using HDBM-LD methods and represent the majority of the geometric shape of each structure, and the volumes of each structure adjusted for whole brain volume. We also compared hemispheric right/left asymmetry and estimated effect sizes for both volume and shape differences between groups.

Results: We found no statistically significant differences between the TS subjects and controls in volume, shape, or right/left asymmetry.  Effect sizes were greater for shape analysis than for volume.

Conclusion: This study represents one of the first efforts to study the shape as opposed to the volume of the BG in TS, but power was limited by sample size. Shape analysis by the HDBM-LD method may prove more sensitive to group differences.

Hippocampal shape abnormalities of patients with childhood-onset schizophrenia and their unaffected siblings

OBJECTIVE

The hippocampus has been implicated in the pathogenesis of schizophrenia, and hippocampal volume deficits have been a consistently reported abnormality, but the subregional specificity of the deficits remains unknown. The authors explored the nature and developmental trajectory of subregional shape abnormalities of the hippocampus in patients with childhood-onset schizophrenia (COS), their healthy siblings, and healthy volunteers.

METHOD

Two hundred twenty-five anatomic brain magnetic resonance images were obtained from 103 patients with COS, 169 from their 79 healthy siblings, and 255 from 101 age- and sex-matched healthy volunteers (age range = 9-29 years). The hippocampus was segmented using FreeSurfer automated image analysis software, and hippocampal shape was evaluated by comparing subjects at more than 6,000 vertices on the left and right hippocampal surfaces. Longitudinal data were examined using mixed model regression analysis.

RESULTS

Patients with COS showed significant bilateral inward deformation in the anterior hippocampus. Healthy siblings also showed a trend for anterior inward deformation. However, the trajectory of shape change did not differ significantly between the groups. Inward deformations in the anterior hippocampus were positively related to positive symptom severity, whereas outward surface displacement was positively related to overall functioning.

CONCLUSION

This is the first and largest longitudinal three-way analysis of subregional hippocampal shape abnormalities in patients with COS and their healthy siblings compared with healthy controls. The anterior hippocampal abnormalities in COS suggest the pathophysiologic importance of this subregion in schizophrenia. The trend level and overlapping shape abnormalities in the healthy siblings suggest a more subtle, subregionally specific neuroanatomic endophenotype.

Computational medicine: translating models to clinical care

Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health.

Hippocampal shape and volume changes with antipsychotics in early stage psychotic illness

Progression of hippocampal shape and volume abnormalities has been described in psychotic disorders such as schizophrenia. However it is unclear how specific antipsychotic medications influence the development of hippocampal structure. We conducted a longitudinal, randomized, controlled, multisite, double-blind study involving 14 academic medical centers (United States 11, Canada 1, Netherlands 1, and England 1). One hundred thirty-four first-episode psychosis patients (receiving either haloperidol [HAL] or olanzapine [OLZ]) and 51 healthy controls were followed for up to 104 weeks using magnetic resonance imaging and large-deformation high-dimensional brain mapping of the hippocampus. Changes in hippocampal volume and shape metrics (i.e., percentage of negative surface vertex slopes, and surface deformation) were evaluated. Mixed-models analysis did not show a significant group-by-time interaction for hippocampal volume. However, the cumulative distribution function of hippocampal surface vertex slopes showed a notable left shift with HAL treatment compared to OLZ treatment and to controls. OLZ treatment was associated with a significantly lower percentage of "large magnitude" negative surface vertex slopes compared to HAL treatment (p = 0.004). Surface deformation maps however did not localize any hippocampal regions that differentially contracted over time with OLZ treatment, after FDR correction. These results indicate that surface analysis provides supplementary information to volumetry in detecting differential treatment effects of the hippocampus. Our results suggest that OLZ is associated with less longitudinal hippocampal surface deformation than HAL, however the hippocampal regions affected appear to be variable across patients.

Alcohol use disorders contribute to hippocampal and subcortical shape differences in schizophrenia

BACKGROUND

Alcohol abuse and dependence have been reported to exacerbate the clinical course of schizophrenia. However, the neurobiological basis of this co-morbid interaction is unknown. The aim of this study was to determine the relationship of co-morbid alcohol use disorder (AUD) with brain structure abnormalities in schizophrenia patients.

METHODS

T1-weighted magnetic resonance images were collected from schizophrenia patients without a history of any substance use disorder (SCZ_0, n=35), schizophrenia patients with a history of AUD only (SCZ_AUD, n=16), and a healthy comparison group without a history of any substance use disorder (CON, n=56). Large-deformation, high-dimensional brain mapping was used to quantify the surface shapes of the hippocampus, thalamus, striatum, and globus pallidus in these subject groups. Analysis of variance was used to test for differences in surface shape measures among the groups.

RESULTS

SCZ_AUD demonstrated the greatest severity of shape abnormalities in the hippocampus, thalamus, striatum, and globus pallidus as compared to SCZ_0 and CON. SCZ_AUD demonstrated a combination of exaggerated shape differences in regions where SCZ_0 also showed shape differences, and unique shape differences that were not observed in SCZ_0 or CON.

CONCLUSIONS

Shape differences in schizophrenia were compounded by a history of co-morbid AUD. Future research is needed to determine whether these differences are simply additive or whether they are due to an interaction between the underlying neurobiology of schizophrenia and alcoholism. The consequences of such shape differences for the clinical course of schizophrenia are not yet understood.

MRI shows more severe hippocampal atrophy and shape deformation in hippocampal sclerosis than in Alzheimer's disease

While hippocampal atrophy is a key feature of both hippocampal sclerosis (HS) and Alzheimer's disease (AD), the pathology underlying this finding differs in these two conditions. In AD, atrophy is due primarily to loss of neurons and neuronal volume as a result of neurofibrillary tangle formation. While the etiology of HS is unknown, neuron loss in the hippocampus is severe to complete. We compared hippocampal volume and deformations from premortem MRI in 43 neuropathologically diagnosed cases of HS, AD, and normal controls (NC) selected from a longitudinal study of subcortical ischemic vascular disease (IVD Program Project). HS cases (n = 11) showed loss of neurons throughout the rostral-caudal extent of the hippocampus in one or both hemispheres. AD cases (n = 24) met NIA-Reagan criteria for high likelihood of AD. Normal control cases (n = 8) were cognitively intact and showed no significant AD or hippocampal pathology. The mean hippocampal volumes were significantly lower in HS versus AD groups (P < .001). Mean shape deformations in the CA1 and subiculum differed significantly between HS versus AD, HS versus NC, and AD versus NC (P < .0001). Additional study is needed to determine whether these differences will be meaningful for clinical diagnosis of individual cases.

Statistical analysis of relative pose information of subcortical nuclei: application on ADNI data

Many brain morphometry studies have been performed in order to characterize the brain atrophy pattern of Alzheimer's disease (AD). The earliest studies focused on the volume of particular brain structures, such as hippocampus and entorhinal cortex. Even though volumetry is a powerful, robust and intuitive technique that has yielded a wealth of findings, more complex shape descriptors have been used to perform statistical shape analysis of particular brain structures. However, in shape analysis studies of brain structures the information of the relative pose between neighbor structures is typically disregarded. This work presents a framework to analyse pose information including the following approaches: similarity transformations with either pseudo-Riemannian or left-invariant Riemannian metric, and centered transformations with a bi-invariant Riemannian metric. As an illustration, an analysis of covariance (ANCOVA) and a discrimination analysis were performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) data.

Automatic morphometry in Alzheimer's disease and mild cognitive impairment

This paper presents a novel, publicly available repository of anatomically segmented brain images of healthy subjects as well as patients with mild cognitive impairment and Alzheimer's disease. The underlying magnetic resonance images have been obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. T1-weighted screening and baseline images (1.5T and 3T) have been processed with the multi-atlas based MAPER procedure, resulting in labels for 83 regions covering the whole brain in 816 subjects. Selected segmentations were subjected to visual assessment. The segmentations are self-consistent, as evidenced by strong agreement between segmentations of paired images acquired at different field strengths (Jaccard coefficient: 0.802±0.0146). Morphometric comparisons between diagnostic groups (normal; stable mild cognitive impairment; mild cognitive impairment with progression to Alzheimer's disease; Alzheimer's disease) showed highly significant group differences for individual regions, the majority of which were located in the temporal lobe. Additionally, significant effects were seen in the parietal lobe. Increased left/right asymmetry was found in posterior cortical regions. An automatically derived white-matter hypointensities index was found to be a suitable means of quantifying white-matter disease. This repository of segmentations is a potentially valuable resource to researchers working with ADNI data.

Thalamic morphology in schizophrenia and schizoaffective disorder

BACKGROUND

Biomarkers are needed that can distinguish between schizophrenia and schizoaffective disorder to inform the ongoing debate over the diagnostic boundary between these two disorders. Neuromorphometric abnormalities of the thalamus have been reported in individuals with schizophrenia and linked to core features of the disorder, but have not been similarly investigated in individuals with schizoaffective disorder. In this study, we examine whether individuals with schizoaffective disorder have a pattern of thalamic deformation that is similar or different to the pattern found in individuals with schizophrenia.

METHOD

T1-weighted magnetic resonance images were collected from individuals with schizophrenia (n = 47), individuals with schizoaffective disorder (n = 15), and controls (n = 42). Large-deformation, high-dimensional brain mapping was used to obtain three-dimensional surfaces of the thalamus. Multiple analyses of variance were used to test for group differences in volume and measures of surface shape.

RESULTS

Individuals with schizophrenia or schizoaffective disorder have similar thalamic volumes. Thalamic surface shape deformation associated with schizophrenia suggests selective involvement of the anterior and posterior thalamus, while deformations in mediodorsal and ventrolateral regions were observed in both groups. Schizoaffective disorder had distinct deformations in medial and lateral thalamic regions.

CONCLUSIONS

Abnormalities distinct to schizoaffective disorder suggest involvement of the central and ventroposterior medial thalamus which may be involved in mood circuitry, dorsolateral nucleus which is involved in recall processing, and the lateral geniculate nucleus which is involved in visual processing.

Structural pathology underlying neuroendocrine dysfunction in schizophrenia

Polydipsic hyponatremic schizophrenic (PHS) patients exhibit altered neuroendocrine activity that has been linked to their life-threatening water imbalance, as well as to impaired function and reduced volume of the anterior hippocampus. Polydipsic patients without hyponatremia (polydipsic normonatremic schizophrenics: PNS) exhibit similar, albeit less marked, changes in neuroendocrine activity and anterior hippocampal function, but not reduced anterior hippocampal volume. Indeed, reduced anterior hippocampal volume is seen in patients with normal water balance (nonpolydipsic normonatremic schizophrenics: NNS) whose neuroendocrine activity and anterior hippocampal function differ markedly from those with polydipsia. In an effort to reconcile these findings we measured hippocampal, amygdala and 3rd ventricle shapes in 26 schizophrenic patients (10 PNS, 7 PHS, 9 NNS) and 12 healthy controls matched for age and gender. Bilateral inward deformations were localized to the anterior lateral hippocampal surface (part of a neurocircuit which modulates neuroendocrine responses to psychological stimuli) in PHS and to a lesser extent in PNS, while deformations in NNS were restricted to the medial surface. Proportional deformations of the right medial amygdala, a key segment of this neurocircuit, were seen in both polydipsic groups, and correlated with the volume of the 3rd ventricle, which lies adjacent to the neuroendocrine nuclei. Finally, these structural findings were most marked in those with impaired hippocampal-mediated stress responses. These results reconcile previously conflicting data, and support the view that anterior lateral hippocampal pathology disrupts neuroendocrine function in polydipsic patients with and without hyponatremia. The relationship of these findings to the underlying mental illness remains to be established.

General multivariate linear modeling of surface shapes using SurfStat

Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper presents a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used to parameterize, smooth out, and normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using the SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.

Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method.

3D maps localize caudate nucleus atrophy in 400 Alzheimer's disease, mild cognitive impairment, and healthy elderly subjects

MRI research examining structural brain atrophy in Alzheimer's disease (AD) generally focuses on medial temporal and cortical structures, but amyloid and tau deposits also accumulate in the caudate. Here we mapped the 3D profile of caudate atrophy using a surface mapping approach in subjects with AD and mild cognitive impairment (MCI) to identify potential clinical and pathological correlates. 3D surface models of the caudate were automatically extracted from 400 baseline MRI scans (100 AD, 200 MCI, 100 healthy elderly). Compared to controls, caudate volumes were lower in MCI (2.64% left, 4.43% right) and AD (4.74% left, 8.47% right). Caudate atrophy was associated with age, sum-of-boxes and global Clinical Dementia Ratings, Delayed Logical Memory scores, MMSE decline 1 year later, and body mass index. Reduced right (but not left) volume was associated with MCI-to-AD conversion and CSF tau levels. Normal caudate asymmetry (with the right 3.9% larger than left) was lost in AD, suggesting preferential right caudate atrophy. Automated caudate maps may complement other MRI-derived measures of disease burden in AD.

Atlas generation for subcortical and ventricular structures with its applications in shape analysis

Atlas-driven morphometric analysis has received great attention for studying anatomical shape variation across clinical populations in neuroimaging research as it provides a local coordinate representation for understanding the family of anatomic observations. We present a procedure for generating atlas of subcortical and ventricular structures, including amygdala, hippocampus, caudate, putamen, globus pallidus, thalamus, and lateral ventricles, using the large deformation diffeomorphic metric atlas generation algorithm. The atlas was built based on manually labeled volumes of 41 subjects randomly selected from the database of Open Access Series of Imaging Studies (OASIS, 10 young adults, 10 middle-age adults, 10 healthy elders, and 11 patients with dementia). We show that the estimated atlas is representative of the population in terms of its metric distance to each individual subject in the population. In the application of detecting shape variations, using the estimated atlas may potentially increase statistical power in identifying group shape difference when comparing with using a single subject atlas. In shape-based classification, the metric distances between subjects and each of within-class estimated atlases construct a shape feature space, which allows for performing a variety of classification algorithms to distinguish anatomies.

APOE mRNA and protein expression in postmortem brain are modulated by an extended haplotype structure

Currently the epsilon4 allele of the apolipoprotein E gene (APOE) is the strongest genetic risk factor for late onset Alzheimer's disease (AD). However, inheritance of the APOE epsilon4 allele is not necessary or sufficient for the development of AD. Genetic evidence suggests that multiple loci in a 70 kb region surrounding APOE are associated with AD risk. Even though these loci could represent surrogate markers in linkage disequilibrium with APOE epsilon4 allele, they could also contribute biological effects independent of the APOE epsilon4 allele. Our previous study identified multiple SNPs upstream from APOE that are associated with cerebrospinal fluid apoE levels, suggesting that a haplotype structure proximal to APOE can influence apoE expression. In this study, we examined apoE expression in human post-mortem brain (PMB), and constructed chromosome-phase-separated haplotypes of the APOE proximal region to evaluate their effect on PMB apoE expression. ApoE protein expression was found to differ among AD brain regions and to differ between AD and control hippocampus. In addition, an extended APOE proximal haplotype structure, spanning from the TOMM40 gene to the APOE promoter, may modulate apoE expression in a brain region-specific manner and may influence AD disease status. In conclusion, this haplotype-phenotype analysis of apoE expression in PMB suggests that either; (1) the cis-regulation of APOE expression levels extends far upstream of the APOE promoter or (2) an APOE epsilon4 allele independent mechanism involving the TOMM40 gene plays a role in the risk of AD.

Donepezil treatment and changes in hippocampal structure in very mild Alzheimer disease

OBJECTIVE

To compare longitudinal changes in the hippocampal structure in subjects with very mild dementia of the Alzheimer type (DAT) treated with donepezil hydrochloride, untreated subjects with very mild DAT, and controls without dementia.

DESIGN

MPRAGE sequences were collected approximately 2 years apart on two 1.5-T magnetic resonance imaging systems, yielding 2 cohorts. Large-deformation high-dimensional brain mapping was used to compute deformation of hippocampal subfields.

SETTING

A dementia clinic at Washington University School of Medicine.

PATIENTS OR OTHER PARTICIPANTS

Subjects came from 2 sources: 18 untreated subjects with DAT and 26 controls were drawn from a previous longitudinal study; 18 treated subjects with DAT were studied prospectively, and 44 controls were drawn from a longitudinal study from the same period. Intervention Patients were prescribed donepezil by their physician.

MAIN OUTCOME MEASURES

Hippocampal volume loss and surface deformation.

RESULTS

There was no significant cohort effect at baseline; therefore, the 2 groups of control subjects were combined. The potential confounding effect of cohort/scanner was dealt with by including it as a covariate in statistical tests. There was no significant group effect in the rate of change of hippocampal volume or subfield deformation. Further exploration showed that compared with the untreated subjects with DAT, the treated subjects with DAT did not differ in the rate of change in any of the hippocampal measures. They also did not differ from the controls, while the untreated subjects with DAT differed from the controls in the rates of change of hippocampal volume and CA1 and subiculum subfield deformations.

CONCLUSIONS

Treatment with donepezil did not alter the progression of hippocampal deformation in subjects with DAT in this study. Small sample size may have contributed to this outcome.

Hippocampal volume deficits and shape deformities in young biological relatives of schizophrenia probands

Hippocampal volume decrement may be one of the changes that most closely pre-date schizophrenia onset. Studying hippocampal developmental morphology in adolescent or young adult biological relatives of schizophrenia probands has the potential to further our understanding of the neurodevelopmental etiology of schizophrenia and to discover biomarkers that may aid its early identification. We utilized an artificial neural network segmentation algorithm to automatically define and reliably measure MRI hippocampus volumes. We compared 46 young, nonpsychotic biological relatives of probands against 46 healthy controls without family history of schizophrenia and 46 schizophrenia probands (age range=13 to 28 years). We further contrasted hippocampal shape differences using spherical harmonic functions and assessed how obstetric complications (a trigger for aberrant in utero neurodevelopment) may contribute to hippocampal abnormalities. Similar to schizophrenia probands, unaffected biological relatives of probands had significantly smaller hippocampus volumes than controls; which correspond to inward displacements in shape deformities principally in the anterior hippocampal subregions. Examination of hippocampus volume-age relationships indicate that hippocampus volume normally decreases with age during late adolescence through early adulthood. In contrast, relatives of probands did not show these age-expected changes. Deviant hippocampus volume-age relationships suggest aberrant hippocampal neurodevelopment among biological relatives. Relatives with a history of obstetric complications had significantly smaller left and right hippocampi than relatives without obstetrics complications, including a dose relationship such that greater number of birth complications correlated with smaller hippocampus. Similar hippocampal volume deficits-obstetric complications relationships were observed among schizophrenia probands. Hippocampal abnormalities in schizophrenia are likely to be mediated by different neurobiological mechanisms, including factors associated with obstetric complications which occur during early neurodevelopment. Other brain maturational anomalies affecting the hippocampus in schizophrenia may manifest closer to illness onset in adolescence/early adulthood.

Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand-labeled expert segmentations, we created binary maps of the hippocampus for three age- and sex-matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface-based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini-mental state exam (MMSE) scores, and global and sum-of-boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD.

Collaborative computational anatomy: an MRI morphometry study of the human brain via diffeomorphic metric mapping

This article describes a large multi-institutional analysis of the shape and structure of the human hippocampus in the aging brain as measured via MRI. The study was conducted on a population of 101 subjects including nondemented control subjects (n = 57) and subjects clinically diagnosed with Alzheimer's Disease (AD, n = 38) or semantic dementia (n = 6) with imaging data collected at Washington University in St. Louis, hippocampal structure annotated at the Massachusetts General Hospital, and anatomical shapes embedded into a metric shape space using large deformation diffeomorphic metric mapping (LDDMM) at the Johns Hopkins University. A global classifier was constructed for discriminating cohorts of nondemented and demented subjects based on linear discriminant analysis of dimensions derived from metric distances between anatomical shapes, demonstrating class conditional structure differences measured via LDDMM metric shape (P < 0.01). Localized analysis of the control and AD subjects only on the coordinates of the population template demonstrates shape changes in the subiculum and the CA1 subfield in AD (P < 0.05). Such large scale collaborative analysis of anatomical shapes has the potential to enhance the understanding of neurodevelopmental and neuropsychiatric disorders.

Fully-automated, multi-stage hippocampus mapping in very mild Alzheimer disease

Landmark-based high-dimensional diffeomorphic maps of the hippocampus (although accurate) is highly-dependent on rater's anatomic knowledge of the hippocampus in the magnetic resonance images. It is therefore vulnerable to rater drift and errors if substantial amount of effort is not spent on quality assurance, training, and re-training. A fully-automated, FreeSurfer-initialized large-deformation diffeomorphic metric mapping procedure of small brain substructures, including the hippocampus, has been previously developed and validated in small samples. In this report, we demonstrate that this fully-automated pipeline can be used in place of the landmark-based procedure in a large-sample clinical study to produce similar statistical outcomes. Some direct comparisons of the two procedures are also presented.