Hippocampal subfield surface deformity in nonsemantic primary progressive aphasia

Christensen, Adam; Alpert, Kathryn I.; Rogalskı, Emily; Cobia, Derin; Rao, Julia; Beg, Mirza Faisal; Weıntraub, Sandra; Mesulam, Marek Marsel; Wang, Lei

doi:10.1016/j.dadm.2014.11.013

Cited by 16 publications

(18 citation statements)

References 57 publications

(106 reference statements)

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“…Several investigators have reported that the shapes of brain structures can provide an additional dimension (structural morphology) when quantifying alterations in cognitive ability in AD/MCI patients ( Qiu et al, 2008 ; Tang et al, 2015 , 2016 ). However, hippocampal shape abnormalities ( Apostolova et al, 2006 ; Christensen et al, 2015 ) and texture features ( Li et al, 2010 ; Zhang J. et al, 2012 ) have been reported to be associated with individual memory performance. Hence, the present study provides additional clues regarding the morphological alterations that occur in the hippocampus in AD.…”

Section: Discussionmentioning

confidence: 99%

Radiomic Features of Hippocampal Subregions in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Feng¹,

Wang²,

Zhao³

et al. 2018

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is characterized by progressive dementia, especially in episodic memory, and amnestic mild cognitive impairment (aMCI) is associated with a high risk of developing AD. Hippocampal atrophy/shape changes are believed to be the most robust magnetic resonance imaging (MRI) markers for AD and aMCI. Radiomics, a method of texture analysis, can quantitatively examine a large set of features and has previously been successfully applied to evaluate imaging biomarkers for AD. To test whether radiomic features in the hippocampus can be employed for early classification of AD and aMCI, 1692 features from the caudal and head parts of the bilateral hippocampus were extracted from 38 AD patients, 33 aMCI patients and 45 normal controls (NCs). One way analysis of variance (ANOVA) showed that 111 features exhibited statistically significant group differences (P < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects (P < 0.01). The support vector machine (SVM) model demonstrated that radiomic features allowed us to distinguish AD from NC with an accuracy of 86.75% (specificity = 88.89% and sensitivity = 84.21%) and an area under curve (AUC) of 0.93. In conclusion, these findings provide evidence showing that radiomic features are beneficial in detecting early cognitive decline, and SVM classification analysis provides encouraging evidence for using hippocampal radiomic features as a potential biomarker for clinical applications in AD.

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Section: Discussionmentioning

confidence: 99%

Radiomic Features of Hippocampal Subregions in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Feng¹,

Wang²,

Zhao³

et al. 2018

Front. Aging Neurosci.

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“…Yushkevich et al [15, 16] proposed a multi-atlas segmentation algorithm using a library of manually labeled T1 and T2 scans, whose output was refined by a machine learning bias correction strategy. Wang et al [17, 18] employed a surface-based atlas approach. Our group, in previous work, used a probabilistic atlas to produce segmentations with a Bayesian inference algorithm within a generative framework.…”

Section: Introductionmentioning

confidence: 99%

Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases

et al. 2016

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The hippocampal formation is a complex, heterogeneous structure that consists of a number of distinct, interacting subregions. Atrophy of these subregions is implied in a variety of neurodegenerative diseases, most prominently in Alzheimer’s disease (AD). Thanks to the increasing resolution of MR images and computational atlases, automatic segmentation of hippocampal subregions is becoming feasible in MRI scans. Here we introduce a generative model for dedicated longitudinal segmentation that relies on subject-specific atlases. The segmentations of the scans at the different time points are jointly computed using Bayesian inference. All time points are treated the same to avoid processing bias. We evaluate this approach using over 4,700 scans from two publicly available datasets (ADNI and MIRIAD). In test-retest reliability experiments, the proposed method yielded significantly lower volume differences and significantly higher Dice overlaps than the cross-sectional approach for nearly every subregion (average across subregions: 4.5% vs. 6.5%, Dice overlap: 81.8% vs. 75.4%). The longitudinal algorithm also demonstrated increased sensitivity to group differences: in MIRIAD (69 subjects: 46 with AD and 23 controls), it found differences in atrophy rates between AD and controls that the cross sectional method could not detect in a number of subregions: right parasubiculum, left and right presubiculum, right subiculum, left dentate gyrus, left CA4, left HATA and right tail. In ADNI (836 subjects: 369 with AD, 215 with early cognitive impairment – eMCI – and 252 controls), all methods found significant differences between AD and controls, but the proposed longitudinal algorithm detected differences between controls and eMCI and differences between eMCI and AD that the cross sectional method could not find: left presubiculum, right subiculum, left and right parasubiculum, left and right HATA. Moreover, many of the differences that the cross-sectional method already found were detected with higher significance. The presented algorithm will be made available as part of the open-source neuroimaging package FreeSurfer.

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“…In contrast, other studies found no differences in hippocampal volume in PPA compared to healthy controls (van de Pol, et al, 2006), and found no relationship between medial temporal volume and memory performance across subtypes and related syndromes (Irish, et al, 2015; Mansoor, et al, 2015). Another study including a mix of agrammatic and logopenic non-semantic PPA patients found that even in the absence of overt deficits in memory when compared to controls, subtle hippocampal shape surface deformities were related to memory performance (Christensen, et al, 2015). Future longitudinal studies will show whether the memory perturbations in PPA are exclusively caused by damage within the language network or whether they also relate to the eventual spread of pathology to the hippocampal complex.…”

Section: Discussionmentioning

confidence: 99%

Selective verbal recognition memory impairments are associated with atrophy of the language network in non-semantic variants of primary progressive aphasia

et al. 2017

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Primary progressive aphasia (PPA) is clinically defined by an initial loss of language function and preservation of other cognitive abilities, including episodic memory. While PPA primarily affects the left-lateralized perisylvian language network, some clinical neuropsychological tests suggest concurrent initial memory loss. The goal of this study was to test recognition memory of objects and words in the visual and auditory modality to separate language-processing impairments from retentive memory in PPA. Individuals with non-semantic PPA had longer reaction times and higher false alarms for auditory word stimuli compared to visual object stimuli. Moreover, false alarms for auditory word recognition memory were related to cortical thickness within the left inferior frontal gyrus and left temporal pole, while false alarms for visual object recognition memory was related to cortical thickness within the right-temporal pole. This pattern of results suggests that specific vulnerability in processing verbal stimuli can hinder episodic memory in PPA, and provides evidence for differential contributions of the left and right temporal poles in word and object recognition memory.

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Hippocampal subfield surface deformity in nonsemantic primary progressive aphasia

Cited by 16 publications

References 57 publications

Radiomic Features of Hippocampal Subregions in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Radiomic Features of Hippocampal Subregions in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment

Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases

Selective verbal recognition memory impairments are associated with atrophy of the language network in non-semantic variants of primary progressive aphasia

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