258 research outputs found

    Distinct patterns of functional and effective connectivity between perirhinal cortex and other cortical regions in recognition memory and perceptual discrimination.

    Get PDF
    Traditionally, the medial temporal lobe (MTL) is thought to be dedicated to declarative memory. Recent evidence challenges this view, suggesting that perirhinal cortex (PrC), which interfaces the MTL with the ventral visual pathway, supports highly integrated object representations in recognition memory and perceptual discrimination. Even with comparable representational demands, perceptual and memory tasks differ in numerous task demands and the subjective experience they evoke. Here, we tested whether such differences are reflected in distinct patterns of connectivity between PrC and other cortical regions, including differential involvement of prefrontal control processes. We examined functional magnetic resonance imaging data for closely matched perceptual and recognition memory tasks for faces that engaged right PrC equivalently. Multivariate seed analyses revealed distinct patterns of interactions: Right ventrolateral prefrontal and posterior cingulate cortices exhibited stronger functional connectivity with PrC in recognition memory; fusiform regions were part of the pattern that displayed stronger functional connectivity with PrC in perceptual discrimination. Structural equation modeling revealed distinct patterns of effective connectivity that allowed us to constrain interpretation of these findings. Overall, they demonstrate that, even when MTL structures show similar involvement in recognition memory and perceptual discrimination, differential neural mechanisms are reflected in the interplay between the MTL and other cortical regions

    Prospective memory impairments in Alzheimer's Disease and behavioral variant frontotemporal dementia: Clinical and neural correlates

    Get PDF
    BACKGROUND: Prospective memory (PM) refers to a future-oriented form of memory in which the individual must remember to execute an intended action either at a future point in time (Time-based) or in response to a specific event (Event-based). Lapses in PM are commonly exhibited in neurodegenerative disorders including Alzheimer's disease (AD) and frontotemporal dementia (FTD), however, the neurocognitive mechanisms driving these deficits remain unknown. OBJECTIVE: To investigate the clinical and neural correlates of Time- and Event-based PM disruption in AD and the behavioral-variant FTD (bvFTD). METHODS: Twelve AD, 12 bvFTD, and 12 healthy older Control participants completed a modified version of the Cambridge Prospective Memory test, which examines Time- and Event-based aspects of PM. All participants completed a standard neuropsychological assessment and underwent whole-brain structural MRI. RESULTS: AD and bvFTD patients displayed striking impairments across Time- and Event-based PM relative to Controls, however, Time-based PM was disproportionately affected in the AD group. Episodic memory dysfunction and hippocampal atrophy was found to correlate strongly with PM integrity in both patient groups, however, dissociable neural substrates were also evident for PM performance across dementia syndromes. CONCLUSION: Our study reveals the multifaceted nature of PM dysfunction in neurodegenerative disorders, and suggests common and dissociable neurocognitive mechanisms, which subtend these deficits in each patient group. Future studies of PM disturbance in dementia syndromes will be crucial for the development of successful interventions to improve functional independence in the patient's daily life

    Two years later – Revisiting autobiographical memory representations in vmPFC and hippocampus

    Get PDF
    A long-standing question in memory neuroscience concerns how and where autobiographical memories of personal experiences are represented in the brain. In a previous high resolution multivoxel pattern analysis fMRI study, we examined two week old (recent) and ten year old (remote) autobiographical memories (Bonnici et al., 2012, J. Neurosci. 32:16982–16991). We found that remote memories were particularly well represented in ventromedial prefrontal cortex (vmPFC) compared to recent memories. Moreover, while both types of memory were represented within anterior and posterior hippocampus, remote memories were more easily distinguished in the posterior portion. These findings suggested that a change of some kind had occurred between two weeks and ten years in terms of where autobiographical memories were represented in the brain. In order to examine this further, here participants from the original study returned two years later and recalled the memories again. We found that there was no difference in the detectability of memory representations within vmPFC for the now 2 year old and 12 year old memories, and this was also the case for the posterior hippocampus. Direct comparison of the two week old memories (original study) with themselves two years later (present study) confirmed that their representation within vmPFC had become more evident. Overall, this within-subjects longitudinal fMRI study extends our understanding of autobiographical memory representations by allowing us to narrow the window within which their consolidation is likely to occur. We conclude that after a memory is initially encoded, its representation within vmPFC has stablised by, at most, two years later

    Representations of recent and remote autobiographical memories in hippocampal subfields.

    Get PDF
    The hippocampus has long been implicated in supporting autobiographical memories, but little is known about how they are instantiated in hippocampal subfields. Using high resolution functional MRI combined with multi-voxel pattern analysis we found it was possible to detect representations of specific autobiographical memories in individual hippocampal subfields. Moreover, while subfields in the anterior hippocampus contained information about both recent (two weeks old) and remote (ten years old) autobiographical memories, posterior CA3 and DG only contained information about the remote memories. Thus, the hippocampal subfields are differentially involved in the representation of recent and remote autobiographical memories during vivid recall. © 2013 Wiley Periodicals, Inc

    The hippocampus and entorhinal cortex encode the path and Euclidean distances to goals during navigation

    Get PDF
    BACKGROUND Despite decades of research on spatial memory, we know surprisingly little about how the brain guides navigation to goals. While some models argue that vectors are represented for navigational guidance, other models postulate that the future path is computed. Although the hippocampal formation has been implicated in processing spatial goal information, it remains unclear whether this region processes path- or vector-related information. RESULTS We report neuroimaging data collected from subjects navigating London's Soho district; these data reveal that both the path distance and the Euclidean distance to the goal are encoded by the medial temporal lobe during navigation. While activity in the posterior hippocampus was sensitive to the distance along the path, activity in the entorhinal cortex was correlated with the Euclidean distance component of a vector to the goal. During travel periods, posterior hippocampal activity increased as the path to the goal became longer, but at decision points, activity in this region increased as the path to the goal became closer and more direct. Importantly, sensitivity to the distance was abolished in these brain areas when travel was guided by external cues. CONCLUSIONS The results indicate that the hippocampal formation contains representations of both the Euclidean distance and the path distance to goals during navigation. These findings argue that the hippocampal formation houses a flexible guidance system that changes how it represents distance to the goal depending on the fluctuating demands of navigation

    Differential effects of emotional cues on components of prospective memory: an ERP study

    Get PDF
    So far, little is known about the neurocognitive mechanisms associated with emotion effects on prospective memory (PM) performance. Thus, this study aimed at disentangling possible mechanisms for the effects of emotional valence of PM cues on the distinct phases composing PM by investigating event-related potentials (ERPs). Participants were engaged in an ongoing N-back task while being required to perform a PM task. The emotional valence of both the ongoing pictures and the PM cues was manipulated (pleasant, neutral, unpleasant). ERPs were recorded during the PM phases, such as encoding, maintenance, and retrieval of the intention. A recognition task including PM cues and ongoing stimuli was also performed at the end of the sessions. ERP results suggest that emotional PM cues not only trigger an automatic, bottom-up, capture of attention, but also boost a greater allocation of top-down processes. These processes seem to be recruited to hold attention toward the emotional stimuli and to retrieve the intention from memory, likely because of the motivational significance of the emotional stimuli. Moreover, pleasant PM cues seemed to modulate especially the prospective component, as revealed by changes in the amplitude of the ERP correlates of strategic monitoring as a function of the relevance of the valence for the PM task. Unpleasant pictures seemed to modulate especially the retrospective component, as revealed by the largest old/new effect being elicited by unpleasant PM pictures in the recognition task

    Dopamine and memory dedifferentiation in aging.

    Get PDF
    The dedifferentiation theory of aging proposes that a reduction in the specificity of neural representations causes declines in complex cognition as people get older, and may reflect a reduction in dopaminergic signaling. The present pharmacological fMRI study investigated episodic memory-related dedifferentiation in young and older adults, and its relation to dopaminergic function, using a randomized placebo-controlled double-blind crossover design with the agonist Bromocriptine (1.25mg) and the antagonist Sulpiride (400mg). We used multi-voxel pattern analysis to measure memory specificity: the degree to which distributed patterns of activity distinguishing two different task contexts during an encoding phase are reinstated during memory retrieval. As predicted, memory specificity was reduced in older adults in prefrontal cortex and in hippocampus, consistent with an impact of neural dedifferentiation on episodic memory representations. There was also a linear age-dependent dopaminergic modulation of memory specificity in hippocampus reflecting a relative boost to memory specificity on Bromocriptine in older adults whose memory was poorer at baseline, and a relative boost on Sulpiride in older better performers, compared to the young. This differed from generalized effects of both agents on task specificity in the encoding phase. The results demonstrate a link between aging, dopaminergic function and dedifferentiation in the hippocampus.This research was funded mainly by a Fellowship to AMM from Research into Ageing, UK, and by an RCUK Academic Fellowship at the University of Edinburgh. Some of the research was conducted by Hunar Abdulrahman as part of a dissertation for the MSc in Neurosciences at the University of Edinburgh. The research was also supported by a Human Brain Project grant from the National Institute of Mental Health and the National Institute of Biomedical Imaging & Bioengineering. PCF was supported by a Wellcome Trust Senior Fellowship in Clinical Science, and by the Bernard Wolfe Health Neuroscience Fund. ETB is a part-time (50%) employee and shareholder of GSK. AMM is a member of the University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross-council Lifelong Health and Wellbeing Initiative, Grant number G0700704/84698.This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1016/j.neuroimage.2015.03.03

    Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease

    Full text link
    This is the peer reviewed version of the following article: Xie, L, Wisse, LEM, Pluta, J, et al. Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. Hum Brain Mapp. 2019; 40: 3431 3451, which has been published in final form at https://doi.org/10.1002/hbm.24607. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology-and thus are promising biomarkers for Alzheimer's disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.Northern California Institute for Research and Education; Foundation for the National Institutes of Health; Canadian Institutes of Health Research; Transition Therapeutics; Takeda Pharmaceutical Company; Servier; Piramal Imaging; Pfizer Inc.; Novartis Pharmaceuticals Corporation; Neurotrack Technologies; NeuroRx Research; Meso Scale Diagnostics, LLC.; Lundbeck and Merck & Co., Inc.; Lumosity; Johnson & Johnson Pharmaceutical Research & Development LLC.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; IXICO Ltd.; GE Healthcare; Fujirebio; Genentech, Inc.; F. Hoffmann-La Roche Ltd.; EuroImmun; Eli Lilly and Company; Elan Pharmaceuticals, Inc.; Cogstate and Eisai Inc.; CereSpir, Inc.; Bristol-Myers Squibb Company; Biogen; BioClinica, Inc.; Araclon Biotech; Alzheimer's Drug Discovery Foundation; Alzheimer's Association; AbbVie; National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Department of Defense ADNI, Grant/Award Number: W81XWH-12-2-0012; Alzheimer's Disease Neuroimaging Initiative, Grant/Award Number: U01 AG024904; Spain Ministry of Economy, Industry and Competitiveness, Grant/Award Number: DPI2017-87743-R; Foundation Philippe Chatrier; BrightFocus Foundation; National Institutes of Health, Grant/Award Numbers: R01-AG055005, R01-EB017255, P30-AG010124, R01-AG040271, R01-AG056014Xie, L.; Wisse, LEM.; Pluta, J.; De Flores, R.; Piskin, V.; Manjón Herrera, JV.; Wang, H.... (2019). Automated segmentation of medial temporal lobe subregions on in vivo T1-weighted MRI in early stages of Alzheimer's disease. Human Brain Mapping. 40(12):3431-3451. https://doi.org/10.1002/hbm.24607S343134514012Apostolova, L. G., Green, A. E., Babakchanian, S., Hwang, K. S., Chou, Y.-Y., Toga, A. W., & Thompson, P. M. (2012). Hippocampal Atrophy and Ventricular Enlargement in Normal Aging, Mild Cognitive Impairment (MCI), and Alzheimer Disease. Alzheimer Disease & Associated Disorders, 26(1), 17-27. doi:10.1097/wad.0b013e3182163b62Augustinack, J. C., Huber, K. E., Stevens, A. A., Roy, M., Frosch, M. P., van der Kouwe, A. J. W., … Fischl, B. (2013). Predicting the location of human perirhinal cortex, Brodmann’s area 35, from MRI. NeuroImage, 64, 32-42. doi:10.1016/j.neuroimage.2012.08.071AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41. doi:10.1016/j.media.2007.06.004Bender, A. R., Keresztes, A., Bodammer, N. C., Shing, Y. L., Werkle‐Bergner, M., Daugherty, A. M., … Raz, N. (2017). Optimization and validation of automated hippocampal subfield segmentation across the lifespan. Human Brain Mapping, 39(2), 916-931. doi:10.1002/hbm.23891Berron, D., Vieweg, P., Hochkeppler, A., Pluta, J. B., Ding, S.-L., Maass, A., … Wisse, L. E. M. (2017). A protocol for manual segmentation of medial temporal lobe subregions in 7 Tesla MRI. NeuroImage: Clinical, 15, 466-482. doi:10.1016/j.nicl.2017.05.022BOBINSKI, M., WEGIEL, J., TARNAWSKI, M., BOBINSKI, M., REISBERG, B., DE LEON, M. J., … WISNIEWSKI, H. M. (1997). Relationships between Regional Neuronal Loss and Neurofibrillary Changes in the Hippocampal Formation and Duration and Severity of Alzheimer Disease. Journal of Neuropathology and Experimental Neurology, 56(4), 414-420. doi:10.1097/00005072-199704000-00010Boccardi, M., Bocchetta, M., Apostolova, L. G., Barnes, J., Bartzokis, G., Corbetta, G., … Frisoni, G. B. (2015). Delphi definition of the EADC-ADNI Harmonized Protocol for hippocampal segmentation on magnetic resonance. Alzheimer’s & Dementia, 11(2), 126-138. doi:10.1016/j.jalz.2014.02.009Boccardi, M., Bocchetta, M., Morency, F. C., Collins, D. L., Nishikawa, M., Ganzola, R., … Frisoni, G. B. (2015). Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimer’s & Dementia, 11(2), 175-183. doi:10.1016/j.jalz.2014.12.002Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica, 82(4), 239-259. doi:10.1007/bf00308809Braak, H., & Braak, E. (1995). Staging of alzheimer’s disease-related neurofibrillary changes. Neurobiology of Aging, 16(3), 271-278. doi:10.1016/0197-4580(95)00021-6Chan, D., Fox, N. C., Scahill, R. I., Crum, W. R., Whitwell, J. L., Leschziner, G., … Rossor, M. N. (2001). Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Annals of Neurology, 49(4), 433-442. doi:10.1002/ana.92Chételat, G., Fouquet, M., Kalpouzos, G., Denghien, I., De la Sayette, V., Viader, F., … Desgranges, B. (2008). Three-dimensional surface mapping of hippocampal atrophy progression from MCI to AD and over normal aging as assessed using voxel-based morphometry. Neuropsychologia, 46(6), 1721-1731. doi:10.1016/j.neuropsychologia.2007.11.037Collins, D. L., & Pruessner, J. C. (2010). Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. NeuroImage, 52(4), 1355-1366. doi:10.1016/j.neuroimage.2010.04.193Coupé, P., Manjón, J. V., Fonov, V., Pruessner, J., Robles, M., & Collins, D. L. (2011). Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2), 940-954. doi:10.1016/j.neuroimage.2010.09.018Das, S. R., Mancuso, L., Olson, I. R., Arnold, S. E., & Wolk, D. A. (2015). Short-Term Memory Depends on Dissociable Medial Temporal Lobe Regions in Amnestic Mild Cognitive Impairment. Cerebral Cortex, 26(5), 2006-2017. doi:10.1093/cercor/bhv022Das, S. R., Pluta, J., Mancuso, L., Kliot, D., Yushkevich, P. A., & Wolk, D. A. (2015). Anterior and posterior MTL networks in aging and MCI. Neurobiology of Aging, 36, S141-S150.e1. doi:10.1016/j.neurobiolaging.2014.03.041Davies, R. R., Halliday, G. M., Xuereb, J. H., Kril, J. J., & Hodges, J. R. (2009). The neural basis of semantic memory: Evidence from semantic dementia. Neurobiology of Aging, 30(12), 2043-2052. doi:10.1016/j.neurobiolaging.2008.02.005De Flores, R., La Joie, R., & Chételat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29-50. doi:10.1016/j.neuroscience.2015.08.033De Flores, R., La Joie, R., Landeau, B., Perrotin, A., Mézenge, F., de La Sayette, V., … Chételat, G. (2014). Effects of age and Alzheimer’s disease on hippocampal subfields. Human Brain Mapping, 36(2), 463-474. doi:10.1002/hbm.22640De Vita, E., Thomas, D. L., Roberts, S., Parkes, H. G., Turner, R., Kinchesh, P., … Ordidge, R. J. (2003). High resolution MRI of the brain at 4.7 Tesla using fast spin echo imaging. The British Journal of Radiology, 76(909), 631-637. doi:10.1259/bjr/69317841Delli Pizzi, S., Franciotti, R., Bubbico, G., Thomas, A., Onofrj, M., & Bonanni, L. (2016). Atrophy of hippocampal subfields and adjacent extrahippocampal structures in dementia with Lewy bodies and Alzheimer’s disease. Neurobiology of Aging, 40, 103-109. doi:10.1016/j.neurobiolaging.2016.01.010Dice, L. R. (1945). Measures of the Amount of Ecologic Association Between Species. Ecology, 26(3), 297-302. doi:10.2307/1932409Dickerson, B. C., Goncharova, I., Sullivan, M. P., Forchetti, C., Wilson, R. S., Bennett, D. A., … deToledo-Morrell, L. (2001). MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease ☆ ☆This research was supported by grants P01 AG09466 and P30 AG10161 from the National Institute on Aging, National Institutes of Health. Neurobiology of Aging, 22(5), 747-754. doi:10.1016/s0197-4580(01)00271-8Ding, S.-L., & Van Hoesen, G. W. (2010). Borders, extent, and topography of human perirhinal cortex as revealed using multiple modern neuroanatomical and pathological markers. Human Brain Mapping, 31(9), 1359-1379. doi:10.1002/hbm.20940Ding, S.-L., Van Hoesen, G. W., Cassell, M. D., & Poremba, A. (2009). Parcellation of human temporal polar cortex: A combined analysis of multiple cytoarchitectonic, chemoarchitectonic, and pathological markers. The Journal of Comparative Neurology, 514(6), 595-623. doi:10.1002/cne.22053Ekstrom, A. D., Bazih, A. J., Suthana, N. A., Al-Hakim, R., Ogura, K., Zeineh, M., … Bookheimer, S. Y. (2009). Advances in high-resolution imaging and computational unfolding of the human hippocampus. NeuroImage, 47(1), 42-49. doi:10.1016/j.neuroimage.2009.03.017Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774-781. doi:10.1016/j.neuroimage.2012.01.021Fischl, B., Stevens, A. A., Rajendran, N., Yeo, B. T. T., Greve, D. N., Van Leemput, K., … Augustinack, J. C. (2009). Predicting the location of entorhinal cortex from MRI. NeuroImage, 47(1), 8-17. doi:10.1016/j.neuroimage.2009.04.033Frisoni, G. B., Jack, C. R., Bocchetta, M., Bauer, C., Frederiksen, K. S., Liu, Y., … Cavedo, E. (2015). The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: Evidence of validity. Alzheimer’s & Dementia, 11(2), 111-125. doi:10.1016/j.jalz.2014.05.1756Fukutani, Y., Kobayashi, K., Nakamura, I., Watanabe, K., Isaki, K., & Cairns, N. J. (1995). Neurons, intracellular and extracellular neurofibrillary tangles in subdivisions of the hippocampal cortex in normal ageing and Alzheimer’s disease. Neuroscience Letters, 200(1), 57-60. doi:10.1016/0304-3940(95)12083-gGlasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., … Jenkinson, M. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105-124. doi:10.1016/j.neuroimage.2013.04.127Greene, S. J., & Killiany, R. J. (2011). Hippocampal Subregions are Differentially Affected in the Progression to Alzheimer’s Disease. The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology, 295(1), 132-140. doi:10.1002/ar.21493Hu, S., Coupé, P., Pruessner, J. C., & Collins, D. L. (2012). Nonlocal regularization for active appearance model: Application to medial temporal lobe segmentation. Human Brain Mapping, 35(2), 377-395. doi:10.1002/hbm.22183Iglesias, J. E., Augustinack, J. C., Nguyen, K., Player, C. M., Player, A., Wright, M., … Van Leemput, K. (2015). A computational atlas of the hippocampal formation using ex vivo , ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage, 115, 117-137. doi:10.1016/j.neuroimage.2015.04.042Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., … Dubois, B. (2016). A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87(5), 539-547. doi:10.1212/wnl.0000000000002923Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., … Harvey, D. (2008). The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685-691. doi:10.1002/jmri.21049Kim, H., Caldairou, B., Bernasconi, A., & Bernasconi, N. (2018). Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling. Frontiers in Neuroinformatics, 12. doi:10.3389/fninf.2018.00039Kivisaari, S. L., Probst, A., & Taylor, K. I. (2013). The Perirhinal, Entorhinal, and Parahippocampal Cortices and Hippocampus: An Overview of Functional Anatomy and Protocol for Their Segmentation in MR Images. fMRI, 239-267. doi:10.1007/978-3-642-34342-1_19Krumm, S., Kivisaari, S. L., Probst, A., Monsch, A. U., Reinhardt, J., Ulmer, S., … Taylor, K. I. (2016). Cortical thinning of parahippocampal subregions in very early Alzheimer’s disease. Neurobiology of Aging, 38, 188-196. doi:10.1016/j.neurobiolaging.2015.11.001Landau, S. M., Mintun, M. A., Joshi, A. D., Koeppe, R. A., Petersen, R. C., … Aisen, P. S. (2012). Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Annals of Neurology, 72(4), 578-586. doi:10.1002/ana.23650Lehmann, M., Douiri, A., Kim, L. G., Modat, M., Chan, D., Ourselin, S., … Fox, N. C. (2010). Atrophy patterns in Alzheimer’s disease and semantic dementia: A comparison of FreeSurfer and manual volumetric measurements. NeuroImage, 49(3), 2264-2274. doi:10.1016/j.neuroimage.2009.10.056Leow, A. D., Klunder, A. D., Jack, C. R., Toga, A. W., Dale, A. M., Bernstein, M. A., … Thompson, P. M. (2006). Longitudinal stability of MRI for mapping brain change using tensor-based morphometry. NeuroImage, 31(2), 627-640. doi:10.1016/j.neuroimage.2005.12.013Leung, K. K., Barnes, J., Ridgway, G. R., Bartlett, J. W., Clarkson, M. J., Macdonald, K., … Ourselin, S. (2010). Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer’s disease. NeuroImage, 51(4), 1345-1359. doi:10.1016/j.neuroimage.2010.03.018Mah, L., Binns, M. A., & Steffens, D. C. (2015). Anxiety Symptoms in Amnestic Mild Cognitive Impairment Are Associated with Medial Temporal Atrophy and Predict Conversion to Alzheimer Disease. The American Journal of Geriatric Psychiatry, 23(5), 466-476. doi:10.1016/j.jagp.2014.10.005Malykhin, N. V., Bouchard, T. P., Camicioli, R., & Coupland, N. J. (2008). Aging hippocampus and amygdala. NeuroReport, 19(5), 543-547. doi:10.1097/wnr.0b013e3282f8b18cMalykhin, N. V., Bouchard, T. P., Ogilvie, C. J., Coupland, N. J., Seres, P., & Camicioli, R. (2007). Three-dimensional volumetric analysis and reconstruction of amygdala and hippocampal head, body and tail. Psychiatry Research: Neuroimaging, 155(2), 155-165. doi:10.1016/j.pscychresns.2006.11.011Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010Martin, S. B., Smith, C. D., Collins, H. R., Schmitt, F. A., & Gold, B. T. (2010). Evidence that volume of anterior medial temporal lobe is reduced in seniors destined for mild cognitive impairment. Neurobiology of Aging, 31(7), 1099-1106. doi:10.1016/j.neurobiolaging.2008.08.010Mishra, S., Gordon, B. A., Su, Y., Christensen, J., Friedrichsen, K., Jackson, K., … Benzinger, T. L. S. (2017). AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure. NeuroImage, 161, 171-178. doi:10.1016/j.neuroimage.2017.07.050Mufson, E. J., & Pandya, D. N. (1984). Some observations on the course and composition of the cingulum bundle in the rhesus monkey. The Journal of Comparative Neurology, 225(1), 31-43. doi:10.1002/cne.902250105Olsen, R. K., Palombo, D. J., Rabin, J. S., Levine, B., Ryan, J. D., & Rosenbaum, R. S. (2013). Volumetric analysis of medial temporal lobe subregions in developmental amnesia using high‐resolution magnetic resonance imaging. Hippocampus, 23(10), 855-860. doi:10.1002/hipo.22153Olsen, R. K., Yeung, L.-K., Noly-Gandon, A., D’Angelo, M. C., Kacollja, A., Smith, V. M., … Barense, M. D. (2017). Human anterolateral entorhinal cortex volumes are associated with cognitive decline in aging prior to clinical diagnosis. Neurobiology of Aging, 57, 195-205. doi:10.1016/j.neurobiolaging.2017.04.025Palmqvist, S., Schöll, M., Strandberg, O., Mattsson, N., Stomrud, E., Zetterberg, H., … Hansson, O. (2017). Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nature Communications, 8(1). doi:10.1038/s41467-017-01150-xPasquini, L., Scherr, M., Tahmasian, M., Myers, N. E., Ortner, M., Kurz, A., … Sorg, C. (2016). Increased Intrinsic Activity of Medial-Temporal Lobe Subregions is Associated with Decreased Cortical Thickness of Medial-Parietal Areas in Patients with Alzheimer’s Disease Dementia. Journal of Alzheimer’s Disease, 51(1), 313-326. doi:10.3233/jad-150823Petersen, R. C. (2004). Mild cognitive impairment as a diagnostic entity. Journal of Internal Medicine, 256(3), 183-194. doi:10.1111/j.1365-2796.2004.01388.xPetersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., … Jack, C. R. (2009). Mild Cognitive Impairment. Archives of Neurology, 66(12). doi:10.1001/archneurol.2009.266Preston, A. R., Bornstein, A. M., Hutchinson, J. B., Gaare, M. E., Glover, G. H., & Wagner, A. D. (2010). High-resolution fMRI of Content-sensitive Subsequent Memory Responses in Human Medial Temporal Lobe. Journal of Cognitive Neuroscience, 22(1), 156-173. doi:10.1162/jocn.2009.21195Qiu, A., Fennema-Notestine, C., Dale, A. M., & Miller, M. I. (2009). Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. NeuroImage, 45(3), 656-661. doi:10.1016/j.neuroimage.2009.01.013Wisse, L. E. M., Gerritsen, L., Zwanenburg, J. J. M., Kuijf, H. J., Luijten, P. R., Biessels, G. J., & Geerlings, M. I. (2012). Subfields of the hippocampal formation at 7T MRI: In vivo volumetric assessment. NeuroImage, 61(4), 1043-1049. doi:10.1016/j.neuroimage.2012.03.023Wisse, L. E. M., Biessels, G. J., & Geerlings, M. I. (2014). A Critical Appraisal of the Hippocampal Subfield Segmentation Package in FreeSurfer. Frontiers in Aging Neuroscience, 6. doi:10.3389/fnagi.2014.00261Witter, M., Van Hoesen, G., & Amaral, D. (1989). Topographical organization of the entorhinal projection to the dentate gyrus of the monkey. The Journal of Neuroscience, 9(1), 216-228. doi:10.1523/jneurosci.09-01-00216.1989Wolk, D. A., & Dickerson, B. C. (2011). Fractionating verbal episodic memory in Alzheimer’s disease. NeuroImage, 54(2), 1530-1539. doi:10.1016/j.neuroimage.2010.09.005Xie, L., Shinohara, R. T., Ittyerah, R., Kuijf, H. J., Pluta, J. B., Blom, K., … Wisse, L. E. M. (2018). Automated Multi-Atlas Segmentation of Hippocampal and Extrahippocampal Subregions in Alzheimer’s Disease at 3T and 7T: What Atlas Composition Works Best? Journal of Alzheimer’s Disease, 63(1), 217-225. doi:10.3233/jad-170932Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. NeuroImage, 31(3), 1116-1128. doi:10.1016/j.neuroimage.2006.01.015Zeineh, M. M., Engel, S. A., Thompson, P. M., & Bookheimer, S. Y. (2001). Unfolding the human hippocampus with high resolution structural and functional MRI. The Anatomical Record, 265(2), 111-120. doi:10.1002/ar.106
    corecore