Sandhitsu (Sandy) Das

My research broadly focuses on studying pathology and normative brain function at a macroscopic level through the use of non-invasive imaging. My current work aims to develop and validate imaging technologies to explore brain structure and function at a finer spatial resolution than is typically achieved. Toward this end, we utilize state-of-the-art acquisition and analysis techniques based on high-resolution MR imaging. Recently, we have ventured into imaging at a high magnetic field of 7 Tesla.

The specific class of imaging problems that serves as a testbed for these technologies in our work revolves around detailed studies of the human medial temporal lobe, including the hippocampal formation and extra-hippocampal cortices. I’m part of the Hippocampus Gang, led by Paul Yushkevich, that has been developing a lot of the enabling technologies for this work. I’m currently co-directing the following projects:

  • Development of image acquisition and analysis techniques for high-resolution imaging of medial temporal lobe at 7 Tesla (Collaborators: David Wolk and Jongho Lee). This work is currently funded by a pilot grant from University of Pennsylvania’s Institute for Translational Medicine ( TAPITMAT-TBIC #10037893 ). 
  • High resolution imaging of medial temporal lobe in temporal lobe epilepsy patients (Collaborator: Kathryn Davis). This work is supported by a pilot grant from University of Pennsylvania’s Center for Biomedical Image Computing and Analytics.    

In addition to this, I’m involved in several ongoing projects in the laboratory:

  •  Using resting-state BOLD fMRI to study brain networks that have dissociable connectivity with subregions of the medial temporal lobe (Collaborator: David Wolk). In particular, we are looking at evidence of involvement in anterior vs. posterior MTL networks, as well as networks within the MTL, in prodromal Alzheimer’s Disease.
  • MTL subregional morphometry using high-resolution structural MRI now available as part of the ADNI2 (Alzheimer’s Disease Neuroimaging Initiative) study. We are part of the ADNI subfield imaging group that is testing several automated methods to make measurements of MTL subregions, including hippocampal subfields. This work is supported by Alzheimer’s Association.
  • Exploring the role of hippocampus and extra-hippocampal MTL cortex in mediating working memory (Collaborator: David Wolk). This project uses cortical cortical thickness as an imaging marker and extensive behavioral testing in older healthy controls and amnesic MCI patients to tease out the brain-behavior relationships as they relate to object memory, spatial memory and contextual binding of the two during working memory tasks.

Selected publications:

  • [DOI] Z. Wang, S. R. Das, S. X. Xie, S. Arnold, J. A. Detre, D. A. Wolk, and A. D. Initiative, “Arterial spin labeled MRI in prodromal Alzheimer’s disease: A multi-site study.,” Neuroimage Clin, vol. 2, pp. 630-6, 2013.
    [Bibtex]
    @ARTICLE{Wang2013NC,
    author = {Wang, Ze and Das, Sandhitsu R. and Xie, Sharon X. and Arnold, Steven
    E. and Detre, John A. and Wolk, David A. and Alzheimer's Disease
    Neuroimaging Initiative},
    title = {{A}rterial spin labeled {MRI} in prodromal {A}lzheimer's disease:
    {A} multi-site study.},
    journal = {{N}euroimage {C}lin},
    year = {2013},
    volume = {2},
    pages = {630-6},
    abstract = {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},
    address = {Netherlands},
    doi = {10.1016/j.nicl.2013.04.014},
    issn = {2213-1582},
    owner = {srdas},
    pii = {S2213-1582(13)00053-3},
    pmcid = {PMC3777751},
    pubmedid = {24179814},
    timestamp = {2014.02.19},
    us_nlm_id = {101597070},
    uuid = {C6FEFB83-AE05-43E3-87AA-652845568627},
    web_data_source = {PubMed}
    }
  • [DOI] S. R. Das, J. Pluta, L. Mancuso, D. Kliot, S. Orozco, B. C. Dickerson, P. A. Yushkevich, and D. A. Wolk, “Increased functional connectivity within medial temporal lobe in mild cognitive impairment.,” Hippocampus, vol. 23, iss. 1, pp. 1-6, 2013.
    [Bibtex]
    @ARTICLE{Das2013H,
    author = {Das, Sandhitsu R. and Pluta, John and Mancuso, Lauren and Kliot,
    Dasha and Orozco, Sylvia and Dickerson, Bradford C. and Yushkevich,
    Paul A. and Wolk, David A.},
    title = {{I}ncreased functional connectivity within medial temporal lobe in
    mild cognitive impairment.},
    journal = {{H}ippocampus},
    year = {2013},
    volume = {23},
    pages = {1-6},
    number = {1},
    month = {1},
    abstract = {Pathology at preclinical and prodromal stages of Alzheimer's disease
    (AD) may manifest itself as measurable functional change in neuronal
    networks earlier than detectable structural change. Functional connectivity
    as measured using resting-state functional magnetic resonance imaging
    has emerged as a useful tool for studying disease effects on baseline
    states of neuronal networks. In this study, we use high resolution
    MRI to label subregions within the medial temporal lobe (MTL), a
    site of early pathology in AD, and report an increase in functional
    connectivity in amnestic mild cognitive impairment between entorhinal
    cortex and subregions of the MTL, with the strongest effect in the
    anterior hippocampus. However, our data also replicated the effects
    of decreased connectivity of the MTL to other nodes of the default
    mode network reported by other researchers. This dissociation of
    changes in functional connectivity within the MTL versus the MTL's
    connection with other neocortical structures can help enrich the
    characterization of early stages of disease progression in AD},
    address = {United States},
    citation_identifier = {Das 2013},
    doi = {10.1002/hipo.22051},
    endnote_reference_number = {157},
    issn = {1098-1063},
    keywords = {Disease Progression;Humans;Middle Aged;Neural Pathways;Female;Entorhinal
    Cortex;Hippocampus;Cohort Studies;Male;Aged;Magnetic Resonance Imaging;Aged,
    80 and over;Temporal Lobe;research support, n.i.h., extramural;Mild
    Cognitive Impairment},
    mid = {NIHMS452636},
    organization = {Penn Image Computing and Science Laboratory (PICSL), Department of
    Radiology, University of Pennsylvania, Philadelphia, Pennsylvania,
    USA. sudas\@seas.upenn.edu},
    owner = {srdas},
    pmcid = {PMC3642853},
    pubmedid = {22815064},
    timestamp = {2014.02.19},
    us_nlm_id = {9108167},
    uuid = {17492B62-593F-41CB-A94C-DE69BB0F2D7B},
    web_data_source = {PubMed}
    }
  • [DOI] S. R. Das, B. B. Avants, J. Pluta, H. Wang, J. W. Suh, M. W. Weiner, S. Mueller, and P. A. Yushkevich, “Measuring longitudinal change in the hippocampal formation from in vivo high-resolution T2-weighted MRI.,” Neuroimage, 2012.
    [Bibtex]
    @ARTICLE{Das2012N,
    author = {Das, Sandhitsu R. and Avants, Brian B. and Pluta, John and Wang,
    Hongzhi and Suh, Jung Wook and Weiner, Michael W. and Mueller, Susanne
    G. and Yushkevich, Paul A.},
    title = {{M}easuring longitudinal change in the hippocampal formation from
    in vivo high-resolution {T}2-weighted {MRI}.},
    journal = {{N}euroimage},
    year = {2012},
    month = {1},
    abstract = {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$\times$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 (1mm(3))
    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 $\beta$=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.},
    doi = {10.1016/j.neuroimage.2012.01.098},
    issn = {1095-9572},
    organization = {Penn Image Computing and Science Laboratory (PICSL), Department of
    Radiology, University of Pennsylvania, Philadelphia, PA, USA.},
    owner = {srdas},
    pii = {S1053-8119(12)00115-2},
    primary_contributor_role = {Author},
    publicationstatus = {Published},
    pubmedid = {22306801},
    timestamp = {2014.02.19},
    us_nlm_id = {9215515},
    uuid = {D77422CD-5A46-4525-9886-3D83793F237C},
    web_data_source = {PubMed}
    }
  • [DOI] J. Pluta, P. Yushkevich, S. Das, and D. Wolk, “In vivo Analysis of Hippocampal Subfield Atrophy in Mild Cognitive Impairment via Semi-Automatic Segmentation of T2-Weighted MRI.,” J Alzheimers Dis, vol. 31, iss. 1, pp. 85-99, 2012.
    [Bibtex]
    @ARTICLE{Pluta2012JAD,
    author = {Pluta, John and Yushkevich, Paul and Das, Sandhitsu and Wolk, David},
    title = {{I}n vivo {A}nalysis of {H}ippocampal {S}ubfield {A}trophy in {M}ild
    {C}ognitive {I}mpairment via {S}emi-{A}utomatic {S}egmentation of
    {T}2-{W}eighted {MRI}.},
    journal = {{J} {A}lzheimers {D}is},
    year = {2012},
    volume = {31},
    pages = {85-99},
    number = {1},
    month = {4},
    abstract = {The measurement of hippocampal volumes using MRI is a useful in-vivo
    biomarker for detection and monitoring of early Alzheimer's disease
    (AD), including during the amnestic mild cognitive impairment (a-MCI)
    stage. The pathology underlying AD has regionally selective effects
    within the hippocampus. As such, we predict that hippocampal subfields
    are more sensitive in discriminating prodromal AD (i.e., a-MCI) from
    cognitively normal controls than whole hippocampal volumes, and attempt
    to demonstrate this using a semi-automatic method that can accurately
    segment hippocampal subfields. High-resolution coronal-oblique T2-weighted
    images of the hippocampal formation were acquired in 45 subjects
    (28 controls and 17 a-MCI (mean age: 69.5 9.2; 70.2 7.6)). CA1, CA2,
    CA3, and CA4/DG subfields, along with head and tail regions, were
    segmented using an automatic algorithm. CA1 and CA4/DG segmentations
    were manually edited. Whole hippocampal volumes were obtained from
    the subjects' T1-weighted anatomical images. Automatic segmentation
    produced significant group differences in the following subfields:
    CA1 (left: p = 0.001, right: p = 0.038), CA4/DG (left: p = 0.002,
    right: p = 0.043), head (left: p = 0.018, right: p = 0.002), and
    tail (left: p = 0.019). After manual correction, differences were
    increased in CA1 (left: p < 0.001, right: p = 0.002), and reduced
    in CA4/DG (left: p = 0.029, right: p = 0.221). Whole hippocampal
    volumes significantly differed bilaterally (left: p = 0.028, right:
    p = 0.009). This pattern of atrophy in a-MCI is consistent with the
    topography of AD pathology observed in postmortem studies, and corrected
    left CA1 provided stronger discrimination than whole hippocampal
    volume (p = 0.03). These results suggest that semi-automatic segmentation
    of hippocampal subfields is efficient and may provide additional
    sensitivity beyond whole hippocampal volumes},
    address = {Netherlands},
    citation_identifier = {Pluta 2012},
    doi = {10.3233/JAD-2012-111931},
    endnote_reference_number = {95},
    issn = {1875-8908},
    keywords = {Humans;Area Under Curve;Functional Laterality;Female;Hippocampus;Atrophy;ROC
    Curve;Male;Magnetic Resonance Imaging;Case-Control Studies;research
    support, n.i.h., extramural;Automatic Data Processing;Chi-Square
    Distribution;video-audio media;Mild Cognitive Impairment},
    mid = {NIHMS366415},
    organization = {Penn Image Computing and Science Laboratory, Department of Radiology,
    University of Pennsylvania, PA, USA Center for Functional Neuroimaging,
    Departments of Neurology and Radiology, University of Pennsylvania,
    Hospital of the University of Pennsylvania, PA, USA.},
    owner = {srdas},
    pii = {74701307X52M342M},
    pmcid = {PMC3391337},
    pubmedid = {22504319},
    sentelink = {file://localhost/Users/srdas/Documents/Sente/My%20Bibliographies/Pluta/J%20Alzheimers%20Dis/2012/Pluta%20J%20Alzheimers%20Dis%202012%20891DCBE5-4580-4CDB-918.pdf,Sente,},
    timestamp = {2014.02.19},
    us_nlm_id = {9814863},
    uuid = {263D90AB-999E-44E6-BFFB-32B71598720A},
    web_data_source = {PubMed}
    }
  • [DOI] S. R. Das, D. Mechanic-Hamilton, J. Pluta, M. Korczykowski, J. A. Detre, and P. A. Yushkevich, “Heterogeneity of functional activation during memory encoding across hippocampal subfields in temporal lobe epilepsy.,” Neuroimage, vol. 58, iss. 4, pp. 1121-30, 2011.
    [Bibtex]
    @ARTICLE{Das2011N,
    author = {Das, Sandhitsu R. and Mechanic-Hamilton, Dawn and Pluta, John and
    Korczykowski, Marc and Detre, John A. and Yushkevich, Paul A.},
    title = {{H}eterogeneity of functional activation during memory encoding across
    hippocampal subfields in temporal lobe epilepsy.},
    journal = {{N}euroimage},
    year = {2011},
    volume = {58},
    pages = {1121-30},
    number = {4},
    month = {7},
    abstract = {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 et al., Neuroimage 44(2), 2009)
    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.},
    address = {United States},
    citation_identifier = {Das 2011},
    doi = {10.1016/j.neuroimage.2011.06.085},
    endnote_reference_number = {15},
    issn = {1095-9572},
    keywords = {Cadaver;Image Processing, Computer-Assisted;Humans;Memory;Algorithms;Atlases
    as Topic;Hippocampus;Cohort Studies;Magnetic Resonance Imaging;Epilepsy,
    Temporal Lobe;Linear Models;CA1 Region, Hippocampal;research support,
    n.i.h., extramural},
    mid = {NIHMS310882},
    organization = {Penn Image Computing and Science Laboratory (PICSL), Department of
    Radiology, University of Pennsylvania, PA, USA.},
    owner = {srdas},
    pii = {S1053-8119(11)00750-6},
    pmcid = {PMC3285462},
    publicationstatus = {Published},
    pubmedid = {21763431},
    sentelink = {file://localhost/Users/srdas/Documents/Sente/My%20Bibliographies/Das/Neuroimage/2011/Das%20Neuroimage%202011%2024135177-B3E2-4710-8088-11DCC7.pdf,Sente,PDF
    Download},
    timestamp = {2014.02.19},
    us_nlm_id = {9215515},
    uuid = {7B3370F8-8058-4ADB-BDDE-D05F2C71D635},
    web_data_source = {PubMed}
    }
  • [DOI] H. Wang, S. R. Das, J. W. Suh, M. Altinay, J. Pluta, C. Craige, B. Avants, and P. A. Yushkevich, “A Learning-Based Wrapper Method to Correct Systematic Errors in Automatic Image Segmentation: Consistently Improved Performance in Hippocampus, Cortex and Brain Segmentation.,” Neuroimage, vol. 55, iss. 3, pp. 968-85, 2011.
    [Bibtex]
    @ARTICLE{Wang2011N,
    author = {Wang, Hongzhi and Das, Sandhitsu R. and Suh, Jung Wook and Altinay,
    Murat and Pluta, John and Craige, Caryne and Avants, Brian and Yushkevich,
    Paul A.},
    title = {{A} {L}earning-{B}ased {W}rapper {M}ethod to {C}orrect {S}ystematic
    {E}rrors in {A}utomatic {I}mage {S}egmentation: {C}onsistently {I}mproved
    {P}erformance in {H}ippocampus, {C}ortex and {B}rain {S}egmentation.},
    journal = {{N}euroimage},
    year = {2011},
    volume = {55},
    pages = {968-85},
    number = {3},
    month = {1},
    abstract = {We propose a simple but generally applicable approach to improving
    the accuracy of automatic image segmentation algorithms relative
    to manual segmentations. The ap- proach is based on the hypothesis
    that a large fraction of the errors produced by auto- matic 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 seg- mentation 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 aver- age 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.},
    citation_identifier = {Wang 2011},
    doi = {10.1016/j.neuroimage.2011.01.006},
    endnote_reference_number = {96},
    issn = {1095-9572},
    keywords = {research support, non-u.s. gov't;Image Processing, Computer-Assisted;Humans;Middle
    Aged;Algorithms;Atlases as Topic;Brain;Databases, Factual;Female;Hippocampus;Male;Aged;Image
    Enhancement;Alzheimer Disease;Cerebral Cortex;Software;research support,
    n.i.h., extramural;Artificial Intelligence},
    mid = {NIHMS265549},
    organization = {Penn Image Computing and Science Laboratory, Departments of Radiology,
    University of Pennsylvania, Philadelphia, PA, USA.},
    owner = {srdas},
    pii = {S1053-8119(11)00024-3},
    pmcid = {PMC3049832},
    primary_contributor_role = {Author},
    publicationstatus = {Published},
    pubmedid = {21237273},
    sentelink = {file://localhost/Users/srdas/Documents/Sente/My%20Bibliographies/Wang/Neuroimage/2011/Wang%20Neuroimage%202011%20B9172645-1992-437C-9BA4-806A0.pdf,Sente,PDF
    Download},
    timestamp = {2014.02.19},
    us_nlm_id = {9215515},
    uuid = {FA1A3150-8FFA-4DC6-9B2A-5A009FDBB67A},
    web_data_source = {PubMed}
    }
  • [DOI] P. A. Yushkevich, H. Wang, J. Pluta, S. Das, C. Craige, B. B. Avants, M. W. Weiner, and S. Mueller, “Nearly Automatic Segmentation of Hippocampal Subfields in In Vivo Focal T2-Weighted MRI.,” Neuroimage, vol. 53, iss. 4, pp. 1208-24, 2010.
    [Bibtex]
    @ARTICLE{Yushkevich2010N,
    author = {Yushkevich, Paul A. and Wang, Hongzhi and Pluta, John and Das, Sandhitsu
    R. and Craige, Caryne and Avants, Brian B. and Weiner, Michael W.
    and Mueller, Susanne},
    title = {{N}early {A}utomatic {S}egmentation of {H}ippocampal {S}ubfields
    in {I}n {V}ivo {F}ocal {T}2-{W}eighted {MRI}.},
    journal = {{N}euroimage},
    year = {2010},
    volume = {53},
    pages = {1208-24},
    number = {4},
    month = {6},
    abstract = {We present and evaluate a new method for automatically labeling the
    subfields of the hippocampal formation in focal 0.4x0.5x2.0mm(3)
    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.},
    citation_identifier = {Yushkevich 2010},
    doi = {10.1016/j.neuroimage.2010.06.040},
    endnote_reference_number = {8},
    issn = {1095-9572},
    keywords = {research support, non-u.s. gov't;Image Interpretation, Computer-Assisted;Humans;Middle
    Aged;Algorithms;Female;Hippocampus;Male;Aged;Magnetic Resonance Imaging;Aged,
    80 and over;Adult;research support, n.i.h., extramural;Brain Mapping},
    mid = {NIHMS220223},
    organization = {Penn Image Computing and Science Laboratory, Department of Radiology,
    University of Pennsylvania, Philadelphia, USA.},
    owner = {srdas},
    pii = {S1053-8119(10)00884-0},
    pmcid = {PMC2939190},
    primary_contributor_role = {Author},
    publicationstatus = {Published},
    pubmedid = {20600984},
    sentelink = {file://localhost/Users/srdas/Documents/Sente/My%20Bibliographies/Yushkevich/Neuroimage/2010/Yushkevich%20Neuroimage%202010%201966171D-279E-495D-A091.pdf,Manual
    Link,Local file},
    timestamp = {2014.02.19},
    us_nlm_id = {9215515},
    uuid = {4A2CD65A-FAA7-4969-8C62-4ECCBF0D6EB4},
    web_data_source = {PubMed}
    }
  • [DOI] S. R. Das, D. Mechanic-Hamilton, M. Korczykowski, J. Pluta, S. Glynn, B. B. Avants, J. A. Detre, and P. A. Yushkevich, “Structure specific analysis of the hippocampus in temporal lobe epilepsy.,” Hippocampus, vol. 19, iss. 6, pp. 517-25, 2009.
    [Bibtex]
    @ARTICLE{Das2009H,
    author = {Das, Sandhitsu R. and Mechanic-Hamilton, Dawn and Korczykowski, Marc
    and Pluta, John and Glynn, Simon and Avants, Brian B. and Detre,
    John A. and Yushkevich, Paul A.},
    title = {{S}tructure specific analysis of the hippocampus in temporal lobe
    epilepsy.},
    journal = {{H}ippocampus},
    year = {2009},
    volume = {19},
    pages = {517-25},
    number = {6},
    month = {6},
    abstract = {The hippocampus is a major structure of interest affected by temporal
    lobe epilepsy (TLE). Region of interest (ROI)-based analysis has
    traditionally been used to study hippocampal involvement in TLE,
    although spatial variation of structural and functional pathology
    have been known to exist within the ROI. In this article, structure-specific
    analysis (Yushkevich et al. (2007) Neuroimage 35:1516-1530) is applied
    to the study of both structure and function in TLE patients. This
    methodology takes into account information about the spatial correspondence
    of voxels within ROIs on left and right sides of the same subject
    as well as between subjects. Hippocampal thickness is studied as
    a measure of structural integrity, and functional activation in a
    functional magnetic resonance imaging (fMRI) experiment in which
    subjects performed a memory encoding task is studied as a measure
    of functional integrity. Pronounced disease-related decrease in thickness
    is found in posterior and anterior hippocampus. A region in the body
    also shows increased thickness in patients' healthy hippocampi compared
    with controls. Functional activation in diseased hippocampi is reduced
    in the body region compared to controls, whereas a region in the
    tail showing greater right-lateralized activation in controls also
    shows greater activation in healthy hippocampi compared with the
    diseased side in patients. Summary measurements generated by integrating
    quantities of interest over the entire hippocampus can also be used,
    as is done in conventional ROI analysis.},
    address = {United States},
    citation_identifier = {Das 2009},
    doi = {10.1002/hipo.20620},
    endnote_reference_number = {14},
    issn = {1098-1063},
    keywords = {Humans;Memory;Functional Laterality;Cluster Analysis;Hippocampus;Magnetic
    Resonance Imaging;Analysis of Variance;Epilepsy, Temporal Lobe;Signal
    Processing, Computer-Assisted;Organ Size;research support, n.i.h.,
    extramural;Neuropsychological Tests},
    mid = {NIHMS186984},
    organization = {Penn Image Computing and Science Laboratory, Department of Radiology,
    University of Pennsylvania, Philadelphia, Pennsylvania, USA. sudas\@seas.upenn.edu},
    owner = {srdas},
    pmcid = {PMC2893564},
    primary_contributor_role = {Author},
    publicationstatus = {Published},
    pubmedid = {19437496},
    sentelink = {file://localhost/Users/srdas/Documents/Sente/My%20Bibliographies/Das/Hippocampus/2009/Das%20Hippocampus%202009%20FBF0B4A4-652B-4780-A0F3-97E1D.pdf,Manual
    Link,Local file},
    timestamp = {2014.02.19},
    us_nlm_id = {9108167},
    uuid = {9917A4B6-5D46-484C-B560-E353AA0ED514},
    web_data_source = {PubMed}
    }
  • [DOI] S. R. Das, B. B. Avants, M. Grossman, and J. C. Gee, “Registration based cortical thickness measurement,” NeuroImage, vol. 45, iss. 3, pp. 867-879, 2009.
    [Bibtex]
    @ARTICLE{Das2009N,
    author = {Das, Sandhitsu R. and Avants, Brian B. and Grossman, Murray and Gee,
    James C.},
    title = {{R}egistration based cortical thickness measurement},
    journal = {{N}euro{I}mage},
    year = {2009},
    volume = {45},
    pages = {867 - 879},
    number = {3},
    abstract = {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.},
    citation_identifier = {Das 2009a},
    doi = {10.1016/j.neuroimage.2008.12.016},
    endnote_reference_number = {168},
    issn = {1053-8119},
    keywords = {Euclidean distance;Deformable models;Cortex;Diffeomorphism;FTD;Longitudinal;Thickness},
    owner = {srdas},
    pmcid = {PMC2836782},
    primary_contributor_role = {Author},
    publicationstatus = {Unknown},
    sentelink = {{http://www.sciencedirect.com/science/article/B6WNP-4V75YSB-7/2/dfe2140dc8af0b8a3455de103f2f2340},BibTeX,Web
    page},
    timestamp = {2014.02.19},
    uuid = {8ACC4DC3-D877-44D5-A7E3-652DE51DB589}
    }