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Sandhitsu R. Das

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YNICL Journal 2025 Journal Article

Posterior hippocampal sparing in Lewy body disorders with Alzheimer’s copathology: An in vivo MRI study

  • Jesse S. Cohen
  • Jeffrey Phillips
  • Sandhitsu R. Das
  • Christopher A. Olm
  • Hamsanandini Radhakrishnan
  • Emma Rhodes
  • Katheryn A.Q. Cousins
  • Sharon X. Xie

BACKGROUND: Lewy body disorders (LBD), encompassing Parkinson disease (PD), PD dementia (PDD), and dementia with Lewy bodies (DLB), are characterized by alpha-synuclein pathology but often are accompanied by Alzheimer's disease (AD) neuropathological change (ADNC). The medial temporal lobe (MTL) is a primary locus of tau accumulation and associated neurodegeneration in AD. However, it is unclear the extent to which AD copathology in LBD (LBD/AD+) contributes to MTL-specific patterns of degeneration. We employ a MTL subregional segmentation strategy of T1-weighted (T1w) MRI in biomarker-supported or autopsy-confirmed LBD and LBD/AD+ to investigate the anatomic consequences of co-occurring LBD/AD+ pathology on neurodegeneration. METHODS: < 185.7 pg/mL n = 126 (75.4 %)). The T1 Automated Segmentation of Hippocampal Subfields (ASHS) pipeline was used to compute volume and thickness measurements of MTL subregions in LBD/AD- and LBD/AD+. Linear regression tested the association of AD copathology and subregion volume/thickness, covarying for age and sex, and intracranial volume for volume measurements. Secondary analyses correlated MTL subregional volume/thickness with cognition and neuropathology. RESULTS: LBD/AD+ had decreased volume/thickness compared to LBD/AD- in all MTL subregions except posterior hippocampus. The greatest effect sizes were seen in Brodmann Area 35 (BA35) (Cohen's d = 0.62, p = 0.002, β = 0.107 ± 0.034), and entorhinal cortex (ERC) (Cohen's d = 0.56, p = 0.006, β = 0.088 ± 0.031). Smaller differences were seen in the parahippocampal cortex (PHC) (Cohen's d = 0.5, p = 0.012, β = 0.082 ± 0.033), BA36 (Cohen's d = 0.47, p = 0.021, β = 0.090 ± 0.039) and anterior hippocampus (Cohen's d = 0.45, p = 0.029, β = 111.790 ± 50.595). Verbal memory scores positively correlated with volume/thickness in anterior and posterior hippocampus, BA35, ERC and PHC, while visuospatial memory positively correlated only in BA35. In the subset of participants with autopsy, lower ERC volume was associated with a higher tau load in ERC (adjusted odds ratio 0.013, 95 % CI [0.0002, 0.841], uncorrected p = 0.041). CONCLUSIONS: Relative to LBD/AD-, LBD/AD+ has greater T1w MRI evidence of atrophy in multiple MTL subregions. Atrophy in MTL subregions associates with memory performance and tau pathological load. The observed pattern of atrophy largely follows expectation from AD Braak stages, except for posterior hippocampus. Longitudinal studies are needed to validate the hypothesized spread of neurodegeneration.

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YNIMG Journal 2022 Journal Article

A framework For brain atlases: Lessons from seizure dynamics

  • Andrew Y. Revell
  • Alexander B. Silva
  • T. Campbell Arnold
  • Joel M. Stein
  • Sandhitsu R. Das
  • Russell T. Shinohara
  • Dani S. Bassett
  • Brian Litt

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain's function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

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YNICL Journal 2022 Journal Article

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

  • T. Campbell Arnold
  • Ramya Muthukrishnan
  • Akash R. Pattnaik
  • Nishant Sinha
  • Adam Gibson
  • Hannah Gonzalez
  • Sandhitsu R. Das
  • Brian Litt

Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0. 84 ± 0. 08 and 0. 74 ± 0. 22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0. 84–0. 86) as reported in the literature. Median 95 % Hausdorff distances were 3. 6 mm and 4. 0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0. 94, p < 0. 0001) and held-out test subjects (r = 0. 87, p < 0. 0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0. 5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0. 90, p < 0. 0001, mean absolute error = 6. 3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.

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YNICL Journal 2022 Journal Article

Ex vivo MRI and histopathology detect novel iron-rich cortical inflammation in frontotemporal lobar degeneration with tau versus TDP-43 pathology

  • M. Dylan Tisdall
  • Daniel T. Ohm
  • Rebecca Lobrovich
  • Sandhitsu R. Das
  • Gabor Mizsei
  • Karthik Prabhakaran
  • Ranjit Ittyerah
  • Sydney Lim

Frontotemporal lobar degeneration (FTLD) is a heterogeneous spectrum of age-associated neurodegenerative diseases that include two main pathologic categories of tau (FTLD-Tau) and TDP-43 (FTLD-TDP) proteinopathies. These distinct proteinopathies are often clinically indistinguishable during life, posing a major obstacle for diagnosis and emerging therapeutic trials tailored to disease-specific mechanisms. Moreover, MRI-derived measures have had limited success to date discriminating between FTLD-Tau or FTLD-TDP. T2*-weighted (T2*w) ex vivo MRI has previously been shown to be sensitive to non-heme iron in healthy intracortical lamination and myelin, and to pathological iron deposits in amyloid-beta plaques and activated microglia in Alzheimer’s disease neuropathologic change (ADNC). However, an integrated, ex vivo MRI and histopathology approach is understudied in FTLD. We apply joint, whole-hemisphere ex vivo MRI at 7 T and histopathology to the study autopsy-confirmed FTLD-Tau (n = 4) and FTLD-TDP (n = 3), relative to ADNC disease-control brains with antemortem clinical symptoms of frontotemporal dementia (n = 2), and an age-matched healthy control. We detect distinct laminar patterns of novel iron-laden glial pathology in both FTLD-Tau and FTLD-TDP brains. We find iron-positive ameboid and hypertrophic microglia and astrocytes largely in deeper GM and adjacent WM in FTLD-Tau. In contrast, FTLD-TDP presents prominent superficial cortical layer iron reactivity in astrocytic processes enveloping small blood vessels with limited involvement of adjacent WM, as well as more diffuse distribution of punctate iron-rich dystrophic microglial processes across all GM lamina. This integrated MRI/histopathology approach reveals ex vivo MRI features that are consistent with these pathological observations distinguishing FTLD-Tau and FTLD-TDP subtypes, including prominent irregular hypointense signal in deeper cortex in FTLD-Tau whereas FTLD-TDP showed upper cortical layer hypointense bands and diffuse cortical speckling. Moreover, differences in adjacent WM degeneration and iron-rich gliosis on histology between FTLD-Tau and FTLD-TDP were also readily apparent on MRI as hyperintense signal and irregular areas of hypointensity, respectively that were more prominent in FTLD-Tau compared to FTLD-TDP. These unique histopathological and radiographic features were distinct from healthy control and ADNC brains, suggesting that iron-sensitive T2*w MRI, adapted to in vivo application at sufficient resolution, may eventually offer an opportunity to improve antemortem diagnosis of FTLD proteinopathies using tissue-validated methods.

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YNIMG Journal 2021 Journal Article

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease

  • Mengjin Dong
  • Long Xie
  • Sandhitsu R. Das
  • Jiancong Wang
  • Laura E.M. Wisse
  • Robin deFlores
  • David A. Wolk
  • Paul A. Yushkevich

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD) and are used in clinical trials to track therapeutic efficacy of disease-modifying treatments. However, longitudinal MRI change measures based on deformable registration can be confounded by MRI artifacts, resulting in over-estimation or underestimation of hippocampal atrophy. For example, the deformation-based-morphometry method ALOHA (Das et al., 2012) finds an increase in hippocampal volume in a substantial proportion of longitudinal scan pairs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, unexpected, given that the hippocampal gray matter is lost with age and disease progression. We propose an alternative approach to quantify disease progression in the hippocampal region: to train a deep learning network (called DeepAtrophy) to infer temporal information from longitudinal scan pairs. The underlying assumption is that by learning to derive time-related information from scan pairs, the network implicitly learns to detect progressive changes that are related to aging and disease progression. Our network is trained using two categorical loss functions: one that measures the network's ability to correctly order two scans from the same subject, input in arbitrary order; and another that measures the ability to correctly infer the ratio of inter-scan intervals between two pairs of same-subject input scans. When applied to longitudinal MRI scan pairs from subjects unseen during training, DeepAtrophy achieves greater accuracy in scan temporal ordering and interscan interval inference tasks than ALOHA (88.5% vs. 75.5% and 81.1% vs. 75.0%, respectively). A scalar measure of time-related change in a subject level derived from DeepAtrophy is then examined as a biomarker of disease progression in the context of AD clinical trials. We find that this measure performs on par with ALOHA in discriminating groups of individuals at different stages of the AD continuum. Overall, our results suggest that using deep learning to infer temporal information from longitudinal MRI of the hippocampal region has good potential as a biomarker of disease progression, and hints that combining this approach with conventional deformation-based morphometry algorithms may lead to improved biomarkers in the future.

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YNICL Journal 2021 Journal Article

Oh brother, where art tau? Amyloid, neurodegeneration, and cognitive decline without elevated tau

  • Lauren E. McCollum
  • Sandhitsu R. Das
  • Long Xie
  • Robin de Flores
  • Jieqiong Wang
  • Sharon X. Xie
  • Laura E.M. Wisse
  • Paul A. Yushkevich

Mild cognitive impairment (MCI) can be an early manifestation of Alzheimer's disease (AD) pathology, other pathologic entities [e.g., cerebrovascular disease, Lewy body disease, LATE (limbic-predominant age-related TDP-43 encephalopathy)], or mixed pathologies, with concomitant AD- and non-AD pathology being particularly common, albeit difficult to identify, in living MCI patients. The National Institute on Aging and Alzheimer's Association (NIA-AA) A/T/(N) [β-Amyloid/Tau/(Neurodegeneration)] AD research framework, which classifies research participants according to three binary biomarkers [β-amyloid (A+/A-), tau (T+/T-), and neurodegeneration (N+/N-)], provides an indirect means of identifying such cases. Individuals with A+T-(N+) MCI are thought to have both AD pathologic change, given the presence of β-amyloid, and non-AD pathophysiology, given neurodegeneration without tau, because in typical AD it is tau accumulation that is most tightly linked to neuronal injury and cognitive decline. Thus, in A+T-(N+) MCI (hereafter referred to as "mismatch MCI" for the tau-neurodegeneration mismatch), non-AD pathology is hypothesized to drive neurodegeneration and symptoms, because β-amyloid, in the absence of tau, likely reflects a preclinical stage of AD. We compared a group of individuals with mismatch MCI to groups with A+T+(N+) MCI (or "prodromal AD") and A-T-(N+) MCI (or "neurodegeneration-only MCI") on cross-sectional and longitudinal cognition and neuroimaging characteristics. β-amyloid and tau status were determined by CSF assays, while neurodegeneration status was based on hippocampal volume on MRI. Overall, mismatch MCI was less "AD-like" than prodromal AD and generally, with some exceptions, more closely resembled the neurodegeneration-only group. At baseline, mismatch MCI had less episodic memory loss compared to prodromal AD. Longitudinally, mismatch MCI declined more slowly than prodromal AD across all included cognitive domains, while mismatch MCI and neurodegeneration-only MCI declined at comparable rates. Prodromal AD had smaller baseline posterior hippocampal volume than mismatch MCI, and whole brain analyses demonstrated cortical thinning that was widespread in prodromal AD but largely restricted to the medial temporal lobes (MTLs) for the mismatch and neurodegeneration-only MCI groups. Longitudinally, mismatch MCI had slower rates of volume loss than prodromal AD throughout the MTLs. Differences in cross-sectional and longitudinal cognitive and neuroimaging measures between mismatch MCI and prodromal AD may reflect disparate underlying pathologic processes, with the mismatch group potentially being driven by non-AD pathologies on a background of largely preclinical AD. These findings suggest that β-amyloid status alone in MCI may not reveal the underlying driver of symptoms with important implications for enrollment in clinical trials and prognosis.

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YNICL Journal 2019 Journal Article

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients

  • Preya Shah
  • John M. Bernabei
  • Lohith G. Kini
  • Arian Ashourvan
  • Jacqueline Boccanfuso
  • Ryan Archer
  • Kelly Oechsel
  • Sandhitsu R. Das

Patients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment. We analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. Using image coregistration and segmentation, we identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We further associate these metrics with post-surgical outcomes. Greater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good-outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model. This study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically-relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

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YNICL Journal 2018 Journal Article

Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy

  • Peter N. Hadar
  • Lohith G. Kini
  • Carlos Coto
  • Virginie Piskin
  • Lauren E. Callans
  • Stephanie H. Chen
  • Joel M. Stein
  • Sandhitsu R. Das

OBJECTIVE: To provide a multi-atlas framework for automated hippocampus segmentation in temporal lobe epilepsy (TLE) and clinically validate the results with respect to surgical lateralization and post-surgical outcome. METHODS: We retrospectively identified 47 TLE patients who underwent surgical resection and 12 healthy controls. T1-weighted 3 T MRI scans were acquired for all subjects, and patients were identified by a neuroradiologist with regards to lateralization and degree of hippocampal sclerosis (HS). Automated segmentation was implemented through the Joint Label Fusion/Corrective Learning (JLF/CL) method. Gold standard lateralization was determined from the surgically resected side in Engel I (seizure-free) patients at the two-year timepoint. ROC curves were used to identify appropriate thresholds for hippocampal asymmetry ratios, which were then used to analyze JLF/CL lateralization. RESULTS: The optimal template atlas based on subject images with varying appearances, from normal-appearing to severe HS, was demonstrated to be composed entirely of normal-appearing subjects, with good agreement between automated and manual segmentations. In applying this atlas to 26 surgically resected seizure-free patients at a two-year timepoint, JLF/CL lateralized seizure onset 92% of the time. In comparison, neuroradiology reads lateralized 65% of patients, but correctly lateralized seizure onset in these patients 100% of the time. When compared to lateralized neuroradiology reads, JLF/CL was in agreement and correctly lateralized all 17 patients. When compared to nonlateralized radiology reads, JLF/CL correctly lateralized 78% of the nine patients. SIGNIFICANCE: While a neuroradiologist's interpretation of MR imaging is a key, albeit imperfect, diagnostic tool for seizure localization in medically-refractory TLE patients, automated hippocampal segmentation may provide more efficient and accurate epileptic foci localization. These promising findings demonstrate the clinical utility of automated segmentation in the TLE MR imaging pipeline prior to surgical resection, and suggest that further investigation into JLF/CL-assisted MRI reading could improve clinical outcomes. Our JLF/CL software is publicly available at https://www.nitrc.org/projects/ashs/.

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YNIMG Journal 2017 Journal Article

Multi-template analysis of human perirhinal cortex in brain MRI: Explicitly accounting for anatomical variability

  • Long Xie
  • John B. Pluta
  • Sandhitsu R. Das
  • Laura E.M. Wisse
  • Hongzhi Wang
  • Lauren Mancuso
  • Dasha Kliot
  • Brian B. Avants

Rational The human perirhinal cortex (PRC) plays critical roles in episodic and semantic memory and visual perception. The PRC consists of Brodmann areas 35 and 36 (BA35, BA36). In Alzheimer's disease (AD), BA35 is the first cortical site affected by neurofibrillary tangle pathology, which is closely linked to neural injury in AD. Large anatomical variability, manifested in the form of different cortical folding and branching patterns, makes it difficult to segment the PRC in MRI scans. Pathology studies have found that in ~97% of specimens, the PRC falls into one of three discrete anatomical variants. However, current methods for PRC segmentation and morphometry in MRI are based on single-template approaches, which may not be able to accurately model these discrete variants Methods A multi-template analysis pipeline that explicitly accounts for anatomical variability is used to automatically label the PRC and measure its thickness in T2-weighted MRI scans. The pipeline uses multi-atlas segmentation to automatically label medial temporal lobe cortices including entorhinal cortex, PRC and the parahippocampal cortex. Pairwise registration between label maps and clustering based on residual dissimilarity after registration are used to construct separate templates for the anatomical variants of the PRC. An optimal path of deformations linking these templates is used to establish correspondences between all the subjects. Experimental evaluation focuses on the ability of single-template and multi-template analyses to detect differences in the thickness of medial temporal lobe cortices between patients with amnestic mild cognitive impairment (aMCI, n=41) and age-matched controls (n=44). Results The proposed technique is able to generate templates that recover the three dominant discrete variants of PRC and establish more meaningful correspondences between subjects than a single-template approach. The largest reduction in thickness associated with aMCI, in absolute terms, was found in left BA35 using both regional and summary thickness measures. Further, statistical maps of regional thickness difference between aMCI and controls revealed different patterns for the three anatomical variants.

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YNIMG Journal 2017 Journal Article

Similar patterns of neural activity predict memory function during encoding and retrieval

  • James E. Kragel
  • Youssef Ezzyat
  • Michael R. Sperling
  • Richard Gorniak
  • Gregory A. Worrell
  • Brent M. Berry
  • Cory Inman
  • Jui-Jui Lin

Neural networks that span the medial temporal lobe (MTL), prefrontal cortex, and posterior cortical regions are essential to episodic memory function in humans. Encoding and retrieval are supported by the engagement of both distinct neural pathways across the cortex and common structures within the medial temporal lobes. However, the degree to which memory performance can be determined by neural processing that is common to encoding and retrieval remains to be determined. To identify neural signatures of successful memory function, we administered a delayed free-recall task to 187 neurosurgical patients implanted with subdural or intraparenchymal depth electrodes. We developed multivariate classifiers to identify patterns of spectral power across the brain that independently predicted successful episodic encoding and retrieval. During encoding and retrieval, patterns of increased high frequency activity in prefrontal, MTL, and inferior parietal cortices, accompanied by widespread decreases in low frequency power across the brain predicted successful memory function. Using a cross-decoding approach, we demonstrate the ability to predict memory function across distinct phases of the free-recall task. Furthermore, we demonstrate that classifiers that combine information from both encoding and retrieval states can outperform task-independent models. These findings suggest that the engagement of a core memory network during either encoding or retrieval shapes the ability to remember the past, despite distinct neural interactions that facilitate encoding and retrieval.

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YNICL Journal 2016 Journal Article

A brain stress test: Cerebral perfusion during memory encoding in mild cognitive impairment

  • Long Xie
  • Sudipto Dolui
  • Sandhitsu R. Das
  • Grace E. Stockbower
  • Molly Daffner
  • Hengyi Rao
  • Paul A. Yushkevich
  • John A. Detre

Arterial spin labeled perfusion magnetic resonance imaging (ASL MRI) provides non-invasive quantification of cerebral blood flow, which can be used as a biomarker of brain function due to the tight coupling between cerebral blood flow (CBF) and brain metabolism. A growing body of literature suggests that regional CBF is altered in neurodegenerative diseases. Here we examined ASL MRI CBF in subjects with amnestic mild cognitive impairment (n = 65) and cognitively normal healthy controls (n = 62), both at rest and during performance of a memory-encoding task. As compared to rest, task-enhanced ASL MRI improved group discrimination, which supports the notion that physiologic measures during a cognitive challenge, or "stress test", may increase the ability to detect subtle functional changes in early disease stages. Further, logistic regression analysis demonstrated that ASL MRI and concomitantly acquired structural MRI provide complementary information of disease status. The current findings support the potential utility of task-enhanced ASL MRI as a biomarker in early Alzheimer's disease.

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YNIMG Journal 2015 Journal Article

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

  • David M. Cash
  • Chris Frost
  • Leonardo O. Iheme
  • Devrim Ünay
  • Melek Kandemir
  • Jurgen Fripp
  • Olivier Salvado
  • Pierrick Bourgeat

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: −1. 4% to −2. 2% (AD) and −0. 35% to −0. 67% (control), for ventricles: 4. 6% to 10. 2% (AD) and 1. 2% to 3. 4% (control), and for hippocampi: −1. 5% to −7. 0% (AD) and −0. 4% to −1. 4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

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YNIMG Journal 2014 Journal Article

Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements

  • Nicholas J. Tustison
  • Philip A. Cook
  • Arno Klein
  • Gang Song
  • Sandhitsu R. Das
  • Jeffrey T. Duda
  • Benjamin M. Kandel
  • Niels van Strien

Many studies of the human brain have explored the relationship between cortical thickness and cognition, phenotype, or disease. Due to the subjectivity and time requirements in manual measurement of cortical thickness, scientists have relied on robust software tools for automation which facilitate the testing and refinement of neuroscientific hypotheses. The most widely used tool for cortical thickness studies is the publicly available, surface-based FreeSurfer package. Critical to the adoption of such tools is a demonstration of their reproducibility, validity, and the documentation of specific implementations that are robust across large, diverse imaging datasets. To this end, we have developed the automated, volume-based Advanced Normalization Tools (ANTs) cortical thickness pipeline comprising well-vetted components such as SyGN (multivariate template construction), SyN (image registration), N4 (bias correction), Atropos (n-tissue segmentation), and DiReCT (cortical thickness estimation). In this work, we have conducted the largest evaluation of automated cortical thickness measures in publicly available data, comparing FreeSurfer and ANTs measures computed on 1205 images from four open data sets (IXI, MMRR, NKI, and OASIS), with parcellation based on the recently proposed Desikan–Killiany–Tourville (DKT) cortical labeling protocol. We found good scan–rescan repeatability with both FreeSurfer and ANTs measures. Given that such assessments of precision do not necessarily reflect accuracy or an ability to make statistical inferences, we further tested the neurobiological validity of these approaches by evaluating thickness-based prediction of age and gender. ANTs is shown to have a higher predictive performance than FreeSurfer for both of these measures. In promotion of open science, we make all of our scripts, data, and results publicly available which complements the use of open image data sets and the open source availability of the proposed ANTs cortical thickness pipeline.

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YNIMG Journal 2014 Journal Article

Subject-specific functional parcellation via Prior Based Eigenanatomy

  • Paramveer S. Dhillon
  • David A. Wolk
  • Sandhitsu R. Das
  • Lyle H. Ungar
  • James C. Gee
  • Brian B. Avants

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ 1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.

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YNICL Journal 2013 Journal Article

Arterial spin labeled MRI in prodromal Alzheimer's disease: A multi-site study

  • Ze Wang
  • Sandhitsu R. Das
  • Sharon X. Xie
  • Steven E. Arnold
  • John A. Detre
  • David A. Wolk

We examined differences in cerebral blood flow (CBF) measured by Arterial Spin Labeled perfusion MRI (ASL MRI) across the continuum from cognitively normal (CN) older adults to mild Alzheimer's Disease (AD) using data from the multi-site Alzheimer's Disease Neuroimaging Initiative (ADNI). Measures of CBF, in a predetermined set of regions (meta-ROI), and hippocampal volume were compared between CN (n = 47), patients with early and late Mild Cognitive Impairment [EMCI (n = 32), LMCI (n = 35)], and AD (n = 15). Associations between these measures and disease severity, assessed by Clinical Dementia Rating scale sum of boxes (CDR SB), were also assessed. Mean meta-ROI CBF was associated with group status and significant hypoperfusion was observed in LMCI and AD relative to CN. Hippocampal volume was associated with group status, but only AD patients had significantly smaller volumes than the CN. When examining the relationship between these measures and disease severity, both were significantly associated with CDR SB and appeared to provide independent prediction of status. In light of the tight link between CBF and metabolism, ASL MRI represents a promising functional biomarker for early diagnosis and disease tracking in AD and this study is the first to demonstrate the feasibility in a multi-site context in this population. Combining functional and structural measures, which can be acquired in the same scanning session, appears to provide additional information about disease severity relative to either measure alone.

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YNIMG Journal 2012 Journal Article

Measuring longitudinal change in the hippocampal formation from in vivo high-resolution T2-weighted MRI

  • Sandhitsu R. Das
  • Brian B. Avants
  • John Pluta
  • Hongzhi Wang
  • Jung W. Suh
  • Michael W. Weiner
  • Susanne G. Mueller
  • Paul A. Yushkevich

The hippocampal formation (HF) is a brain structure of great interest because of its central role in learning and memory, and its associated vulnerability to several neurological disorders. In vivo oblique coronal T2-weighted MRI with high in-plane resolution (∼0. 5mm×0. 5mm), thick slices (∼2. 0mm), and a field of view tailored to imaging the hippocampal formation (denoted HF-MRI in this paper) has been advanced as a useful imaging modality for detailed hippocampal morphometry. Cross-sectional analysis of volume measurements derived from HF-MRI has shown the modality's promise to yield sensitive imaging-based biomarker for neurological disorders such as Alzheimer's disease. However, the utility of this modality for making measurements of longitudinal change has not yet been demonstrated. In this paper, using an unbiased deformation-based morphometry (DBM) pipeline, we examine the suitability of HF-MRI for estimating longitudinal change by comparing atrophy rates measured in the whole hippocampus from this modality with those measured from more common isotropic (∼1mm3) T1-weighted MRI in the same set of individuals, in a cohort of healthy controls and patients with cognitive impairment. While measurements obtained from HF-MRI were largely consistent with those obtained from T1-MRI, HF-MRI yielded slightly larger group effect of greater atrophy rates in patients than in controls. The estimated minimum sample size required for detecting a 25% change in patients' atrophy rate in the hippocampus compared to the control group with a statistical power β =0. 8 was N =269. For T1-MRI, the equivalent sample size was N =325. Using a dataset of test–retest scans, we show that the measurements were free of additive bias. We also demonstrate that these results were not a confound of certain methodological choices made in the DBM pipeline to address the challenges of making longitudinal measurements from HF-MRI, using a region of interest (ROI) around the HF to globally align serial images, followed by slice-by-slice deformable registration to measure local volume change. Additionally, we present a preliminary study of atrophy rate measurements within hippocampal subfields using HF-MRI. Cross-sectional differences in atrophy rates were detected in several subfields.

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YNIMG Journal 2011 Journal Article

A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation

  • Hongzhi Wang
  • Sandhitsu R. Das
  • Jung Wook Suh
  • Murat Altinay
  • John Pluta
  • Caryne Craige
  • Brian Avants
  • Paul A. Yushkevich

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i. e. , occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer (Fischl et al. , 2002); hippocampus segmentation using multi-atlas label fusion (Artaechevarria et al. , 2009); brain extraction using BET (Smith, 2002); and brain tissue segmentation using FAST (Zhang et al. , 2001). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0. 908 for normal controls and 0. 893 for patients with mild cognitive impairment. Average Dice overlaps of 0. 964, 0. 905 and 0. 951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.

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YNIMG Journal 2011 Journal Article

Heterogeneity of functional activation during memory encoding across hippocampal subfields in temporal lobe epilepsy

  • Sandhitsu R. Das
  • Dawn Mechanic-Hamilton
  • John Pluta
  • Marc Korczykowski
  • John A. Detre
  • Paul A. Yushkevich

Pathology studies have shown that the anatomical subregions of the hippocampal formation are differentially affected in various neurological disorders, including temporal lobe epilepsy (TLE). Analysis of structure and function within these subregions using magnetic resonance imaging (MRI) has the potential to generate insights on disease associations as well as normative brain function. In this study, an atlas-based normalization method (Yushkevich, P. A. , Avants, B. B. , Pluta, J. , Das, S. , Minkoff, D. , Mechanic-Hamilton, D. , Glynn, S. , Pickup, S. , Liu, W. , Gee, J. C. , Grossman, M. , Detre, J. A. , 2009. A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9. 4 T. NeuroImage 44 (2), 385–398) was used to label hippocampal subregions, making it possible to examine subfield-level functional activation during an episodic memory task in two different cohorts of healthy controls and subjects diagnosed with intractable unilateral TLE. We report, for the first time, functional activation patterns within hippocampal subfields in TLE. We detected group differences in subfield activation between patients and controls as well as inter-hemispheric activation asymmetry within subfields in patients, with dentate gyrus (DG) and the anterior hippocampus region showing the greatest effects. DG was also found to be more active than CA1 in controls, but not in patients’ epileptogenic side. These preliminary results will encourage further research on the utility of subfield-based biomarkers in TLE.

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YNIMG Journal 2010 Journal Article

Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: An illustration in ADNI 3 T MRI data

  • Paul A. Yushkevich
  • Brian B. Avants
  • Sandhitsu R. Das
  • John Pluta
  • Murat Altinay
  • Caryne Craige

Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i. e. , an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.

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YNIMG Journal 2010 Journal Article

Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI

  • Paul A. Yushkevich
  • Hongzhi Wang
  • John Pluta
  • Sandhitsu R. Das
  • Caryne Craige
  • Brian B. Avants
  • Michael W. Weiner
  • Susanne Mueller

We present and evaluate a new method for automatically labeling the subfields of the hippocampal formation in focal 0. 4×0. 5×2. 0mm3 resolution T2-weighted magnetic resonance images that can be acquired in the routine clinical setting with under 5min scan time. The method combines multi-atlas segmentation, similarity-weighted voting, and a novel learning-based bias correction technique to achieve excellent agreement with manual segmentation. Initial partitioning of MRI slices into hippocampal ’head’, ’body’ and ’tail’ slices is the only input required from the user, necessitated by the nature of the underlying segmentation protocol. Dice overlap between manual and automatic segmentation is above 0. 87 for the larger subfields, CA1 and dentate gyrus, and is competitive with the best results for whole-hippocampus segmentation in the literature. Intraclass correlation of volume measurements in CA1 and dentate gyrus is above 0. 89. Overlap in smaller hippocampal subfields is lower in magnitude (0. 54 for CA2, 0. 62 for CA3, 0. 77 for subiculum and 0. 79 for entorhinal cortex) but comparable to overlap between manual segmentations by trained human raters. These results support the feasibility of subfield-specific hippocampal morphometry in clinical studies of memory and neurodegenerative disease.

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YNIMG Journal 2009 Journal Article

Registration based cortical thickness measurement

  • Sandhitsu R. Das
  • Brian B. Avants
  • Murray Grossman
  • James C. Gee

Cortical thickness is an important biomarker for image-based studies of the brain. A diffeomorphic registration based cortical thickness (DiReCT) measure is introduced where a continuous one-to-one correspondence between the gray matter–white matter interface and the estimated gray matter–cerebrospinal fluid interface is given by a diffeomorphic mapping in the image space. Thickness is then defined in terms of a distance measure between the interfaces of this sheet like structure. This technique also provides a natural way to compute continuous estimates of thickness within buried sulci by preventing opposing gray matter banks from intersecting. In addition, the proposed method incorporates neuroanatomical constraints on thickness values as part of the mapping process. Evaluation of this method is presented on synthetic images. As an application to brain images, a longitudinal study of thickness change in frontotemporal dementia (FTD) spectrum disorder is reported.

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v2026.09.13