Neuroimaging of the developing brain

Horn, John D. Van; Pelphrey, Kevin A.

doi:10.1007/s11682-015-9365-9

Cited by 77 publications

(65 citation statements)

References 23 publications

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“…These images were taken from a database comprising healthy subjects, and patients with various brain disorders and presumably have good coverage of shape variability. The manual segmentations in these libraries followed the protocols defined for caudates (Kim, et al,; 2015; Looi et al, 2008) and hippocampi (Joo et al, 2014). For each structure, we calculated regionally the image similarity between an individual MRI to segment and each template image using a patch-based approach.…”

Section: Methodsmentioning

confidence: 99%

Egocentric and allocentric visuospatial working memory in premotor Huntington's disease: A double dissociation with caudate and hippocampal volumes

et al. 2017

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Our brains represent spatial information in egocentric (self-based) or allocentric (landmark-based) coordinates. Rodent studies have demonstrated a critical role for the caudate in egocentric navigation and the hippocampus in allocentric navigation. We administered tests of egocentric and allocentric working memory to individuals with premotor Huntington’s disease (pmHD), which is associated with early caudate nucleus atrophy, and controls. Each test had 80 trials during which subjects were asked to remember 2 locations over 1-sec delays. The only difference between these otherwise identical tests was that locations could only be coded in self-based or landmark-based coordinates. We applied a multiatlas-based segmentation algorithm and computed point-wise Jacobian determinants to measure regional variations in caudate and hippocampal volumes from 3 T MRI. As predicted, the pmHD patients were significantly more impaired on egocentric working memory. Only egocentric accuracy correlated with caudate volumes, specifically the dorsolateral caudate head, right more than left, a region that receives dense efferents from dorsolateral prefrontal cortex. In contrast, only allocentric accuracy correlated with hippocampal volumes, specifically intermediate and posterior regions that connect strongly with parahippocampal and posterior parietal cortices. These results indicate that the distinction between egocentric and allocentric navigation applies to working memory. The dorsolateral caudate is important for egocentric working memory, which can explain the disproportionate impairment in pmHD. Allocentric working memory, in contrast, relies on the hippocampus and is relatively spared in pmHD.

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Section: Methodsmentioning

confidence: 99%

Egocentric and allocentric visuospatial working memory in premotor Huntington's disease: A double dissociation with caudate and hippocampal volumes

et al. 2017

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“…Dictionary learning and sparse representation techniques have been successfully applied for brain fMRI time series analysis and functional brain network identification (e.g., Oikonomou et al, 2012; Abolghasemi et al, 2013; Zhao S et al, 2015; Zhang S et al, 2015; Jiang et al, 2014; Jiang et al, 2015a; Jiang et al, 2015b; Lv et al, 2015a; Lv et al, 2015b; Lv et al, 2015c; Makkie et al, 2015). In this work, the computational framework of identification of connectome-scale ICNs in each individual subject via dictionary learning and sparse representation is illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

Connectome-scale functional intrinsic connectivity networks in macaques

et al. 2017

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There have been extensive studies of intrinsic connectivity networks (ICNs) in the human brains using resting state fMRI in the literature. However, the functional organization of ICNs in macaque brains has been less explored so far, despite growing interests in the field. In this work, we propose a computational framework to identify connectome-scale group-wise consistent ICNs in macaques via sparse representation of whole-brain resting state fMRI data. Experimental results demonstrate that 70 group-wise consistent ICNs are successfully identified in macaque brains via the proposed framework. These 70 ICNs are interpreted based on two publicly available parcellation maps of macaque brains and our work significantly expand currently known macaque ICNs already reported in the literature. In general, this set of connectome-scale group-wise consistent ICNs can potentially benefit a variety of studies in the neuroscience and brain mapping fields, and they provide a foundation to better understand brain evolution in the future.

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“…Recently, resting-state functional Magnetic Resonance Imaging (RS-fMRI), which uses the blood-oxygenation-level-dependent (BOLD) signal as neurophysiological index, has been successfully applied to early diagnosis of AD/eMCI before the clinical symptoms (Smith, et al, 2011; Sporns, 2011; Wee, et al, 2013; Wee, et al, 2015; Wee, et al, 2012a; Wee, et al, 2012b). The BOLD signal is sensitive to spontaneous and intrinsic neural activity within the brain, thus can be used as an efficient and noninvasive way for investigating neurological disorders at a whole-brain level.…”

Section: Introductionmentioning

confidence: 99%

High‐order resting‐state functional connectivity network for MCI classification

Chen

Zhang

Gao

et al. 2016

Human Brain Mapping

216

227

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Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis.

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Neuroimaging of the developing brain

Cited by 77 publications

References 23 publications

Egocentric and allocentric visuospatial working memory in premotor Huntington's disease: A double dissociation with caudate and hippocampal volumes

Egocentric and allocentric visuospatial working memory in premotor Huntington's disease: A double dissociation with caudate and hippocampal volumes

Connectome-scale functional intrinsic connectivity networks in macaques

High‐order resting‐state functional connectivity network for MCI classification

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