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1.  Study protocol: the Whitehall II imaging sub-study 
BMC Psychiatry  2014;14:159.
Background
The Whitehall II (WHII) study of British civil servants provides a unique source of longitudinal data to investigate key factors hypothesized to affect brain health and cognitive ageing. This paper introduces the multi-modal magnetic resonance imaging (MRI) protocol and cognitive assessment designed to investigate brain health in a random sample of 800 members of the WHII study.
Methods/design
A total of 6035 civil servants participated in the WHII Phase 11 clinical examination in 2012–2013. A random sample of these participants was included in a sub-study comprising an MRI brain scan, a detailed clinical and cognitive assessment, and collection of blood and buccal mucosal samples for the characterisation of immune function and associated measures. Data collection for this sub-study started in 2012 and will be completed by 2016. The participants, for whom social and health records have been collected since 1985, were between 60–85 years of age at the time the MRI study started. Here, we describe the pre-specified clinical and cognitive assessment protocols, the state-of-the-art MRI sequences and latest pipelines for analyses of this sub-study.
Discussion
The integration of cutting-edge MRI techniques, clinical and cognitive tests in combination with retrospective data on social, behavioural and biological variables during the preceding 25 years from a well-established longitudinal epidemiological study (WHII cohort) will provide a unique opportunity to examine brain structure and function in relation to age-related diseases and the modifiable and non-modifiable factors affecting resilience against and vulnerability to adverse brain changes.
doi:10.1186/1471-244X-14-159
PMCID: PMC4048583  PMID: 24885374
Epidemiology; Magnetic resonance imaging; Diffusion tensor imaging; White matter; Functional MRI; Connectome; Resting state brain networks; Neuropsychology; Dementia; Affective disorders
2.  The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data 
Thompson, Paul M. | Stein, Jason L. | Medland, Sarah E. | Hibar, Derrek P. | Vasquez, Alejandro Arias | Renteria, Miguel E. | Toro, Roberto | Jahanshad, Neda | Schumann, Gunter | Franke, Barbara | Wright, Margaret J. | Martin, Nicholas G. | Agartz, Ingrid | Alda, Martin | Alhusaini, Saud | Almasy, Laura | Almeida, Jorge | Alpert, Kathryn | Andreasen, Nancy C. | Andreassen, Ole A. | Apostolova, Liana G. | Appel, Katja | Armstrong, Nicola J. | Aribisala, Benjamin | Bastin, Mark E. | Bauer, Michael | Bearden, Carrie E. | Bergmann, Ørjan | Binder, Elisabeth B. | Blangero, John | Bockholt, Henry J. | Bøen, Erlend | Bois, Catherine | Boomsma, Dorret I. | Booth, Tom | Bowman, Ian J. | Bralten, Janita | Brouwer, Rachel M. | Brunner, Han G. | Brohawn, David G. | Buckner, Randy L. | Buitelaar, Jan | Bulayeva, Kazima | Bustillo, Juan R. | Calhoun, Vince D. | Cannon, Dara M. | Cantor, Rita M. | Carless, Melanie A. | Caseras, Xavier | Cavalleri, Gianpiero L. | Chakravarty, M. Mallar | Chang, Kiki D. | Ching, Christopher R. K. | Christoforou, Andrea | Cichon, Sven | Clark, Vincent P. | Conrod, Patricia | Coppola, Giovanni | Crespo-Facorro, Benedicto | Curran, Joanne E. | Czisch, Michael | Deary, Ian J. | de Geus, Eco J. C. | den Braber, Anouk | Delvecchio, Giuseppe | Depondt, Chantal | de Haan, Lieuwe | de Zubicaray, Greig I. | Dima, Danai | Dimitrova, Rali | Djurovic, Srdjan | Dong, Hongwei | Donohoe, Gary | Duggirala, Ravindranath | Dyer, Thomas D. | Ehrlich, Stefan | Ekman, Carl Johan | Elvsåshagen, Torbjørn | Emsell, Louise | Erk, Susanne | Espeseth, Thomas | Fagerness, Jesen | Fears, Scott | Fedko, Iryna | Fernández, Guillén | Fisher, Simon E. | Foroud, Tatiana | Fox, Peter T. | Francks, Clyde | Frangou, Sophia | Frey, Eva Maria | Frodl, Thomas | Frouin, Vincent | Garavan, Hugh | Giddaluru, Sudheer | Glahn, David C. | Godlewska, Beata | Goldstein, Rita Z. | Gollub, Randy L. | Grabe, Hans J. | Grimm, Oliver | Gruber, Oliver | Guadalupe, Tulio | Gur, Raquel E. | Gur, Ruben C. | Göring, Harald H. H. | Hagenaars, Saskia | Hajek, Tomas | Hall, Geoffrey B. | Hall, Jeremy | Hardy, John | Hartman, Catharina A. | Hass, Johanna | Hatton, Sean N. | Haukvik, Unn K. | Hegenscheid, Katrin | Heinz, Andreas | Hickie, Ian B. | Ho, Beng-Choon | Hoehn, David | Hoekstra, Pieter J. | Hollinshead, Marisa | Holmes, Avram J. | Homuth, Georg | Hoogman, Martine | Hong, L. Elliot | Hosten, Norbert | Hottenga, Jouke-Jan | Hulshoff Pol, Hilleke E. | Hwang, Kristy S. | Jack, Clifford R. | Jenkinson, Mark | Johnston, Caroline | Jönsson, Erik G. | Kahn, René S. | Kasperaviciute, Dalia | Kelly, Sinead | Kim, Sungeun | Kochunov, Peter | Koenders, Laura | Krämer, Bernd | Kwok, John B. J. | Lagopoulos, Jim | Laje, Gonzalo | Landen, Mikael | Landman, Bennett A. | Lauriello, John | Lawrie, Stephen M. | Lee, Phil H. | Le Hellard, Stephanie | Lemaître, Herve | Leonardo, Cassandra D. | Li, Chiang-shan | Liberg, Benny | Liewald, David C. | Liu, Xinmin | Lopez, Lorna M. | Loth, Eva | Lourdusamy, Anbarasu | Luciano, Michelle | Macciardi, Fabio | Machielsen, Marise W. J. | MacQueen, Glenda M. | Malt, Ulrik F. | Mandl, René | Manoach, Dara S. | Martinot, Jean-Luc | Matarin, Mar | Mather, Karen A. | Mattheisen, Manuel | Mattingsdal, Morten | Meyer-Lindenberg, Andreas | McDonald, Colm | McIntosh, Andrew M. | McMahon, Francis J. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Milaneschi, Yuri | Mohnke, Sebastian | Montgomery, Grant W. | Morris, Derek W. | Moses, Eric K. | Mueller, Bryon A. | Muñoz Maniega, Susana | Mühleisen, Thomas W. | Müller-Myhsok, Bertram | Mwangi, Benson | Nauck, Matthias | Nho, Kwangsik | Nichols, Thomas E. | Nilsson, Lars-Göran | Nugent, Allison C. | Nyberg, Lars | Olvera, Rene L. | Oosterlaan, Jaap | Ophoff, Roel A. | Pandolfo, Massimo | Papalampropoulou-Tsiridou, Melina | Papmeyer, Martina | Paus, Tomas | Pausova, Zdenka | Pearlson, Godfrey D. | Penninx, Brenda W. | Peterson, Charles P. | Pfennig, Andrea | Phillips, Mary | Pike, G. Bruce | Poline, Jean-Baptiste | Potkin, Steven G. | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rietschel, Marcella | Rijpkema, Mark | Risacher, Shannon L. | Roffman, Joshua L. | Roiz-Santiañez, Roberto | Romanczuk-Seiferth, Nina | Rose, Emma J. | Royle, Natalie A. | Rujescu, Dan | Ryten, Mina | Sachdev, Perminder S. | Salami, Alireza | Satterthwaite, Theodore D. | Savitz, Jonathan | Saykin, Andrew J. | Scanlon, Cathy | Schmaal, Lianne | Schnack, Hugo G. | Schork, Andrew J. | Schulz, S. Charles | Schür, Remmelt | Seidman, Larry | Shen, Li | Shoemaker, Jody M. | Simmons, Andrew | Sisodiya, Sanjay M. | Smith, Colin | Smoller, Jordan W. | Soares, Jair C. | Sponheim, Scott R. | Sprooten, Emma | Starr, John M. | Steen, Vidar M. | Strakowski, Stephen | Strike, Lachlan | Sussmann, Jessika | Sämann, Philipp G. | Teumer, Alexander | Toga, Arthur W. | Tordesillas-Gutierrez, Diana | Trabzuni, Daniah | Trost, Sarah | Turner, Jessica | Van den Heuvel, Martijn | van der Wee, Nic J. | van Eijk, Kristel | van Erp, Theo G. M. | van Haren, Neeltje E. M. | van ‘t Ent, Dennis | van Tol, Marie-Jose | Valdés Hernández, Maria C. | Veltman, Dick J. | Versace, Amelia | Völzke, Henry | Walker, Robert | Walter, Henrik | Wang, Lei | Wardlaw, Joanna M. | Weale, Michael E. | Weiner, Michael W. | Wen, Wei | Westlye, Lars T. | Whalley, Heather C. | Whelan, Christopher D. | White, Tonya | Winkler, Anderson M. | Wittfeld, Katharina | Woldehawariat, Girma | Wolf, Christiane | Zilles, David | Zwiers, Marcel P. | Thalamuthu, Anbupalam | Schofield, Peter R. | Freimer, Nelson B. | Lawrence, Natalia S. | Drevets, Wayne
Brain Imaging and Behavior  2014;8:153-182.
The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
doi:10.1007/s11682-013-9269-5
PMCID: PMC4008818  PMID: 24399358
Genetics; MRI; GWAS; Consortium; Meta-analysis; Multi-site
3.  Distinction of seropositive NMO spectrum disorder and MS brain lesion distribution 
Neurology  2013;80(14):1330-1337.
Objective:
Neuromyelitis optica and its spectrum disorder (NMOSD) can present similarly to relapsing-remitting multiple sclerosis (RRMS). Using a quantitative lesion mapping approach, this research aimed to identify differences in MRI brain lesion distribution between aquaporin-4 antibody–positive NMOSD and RRMS, and to test their diagnostic potential.
Methods:
Clinical brain MRI sequences for 44 patients with aquaporin-4 antibody–positive NMOSD and 50 patients with RRMS were examined for the distribution and morphology of brain lesions. T2 lesion maps were created for each subject allowing the quantitative comparison of the 2 conditions with lesion probability and voxel-wise analysis.
Results:
Sixty-three percent of patients with NMOSD had brain lesions and of these 27% were diagnostic of multiple sclerosis. Patients with RRMS were significantly more likely to have lesions adjacent to the body of the lateral ventricle than patients with NMOSD. Direct comparison of the probability distributions and the morphologic attributes of the lesions in each group identified criteria of “at least 1 lesion adjacent to the body of the lateral ventricle and in the inferior temporal lobe; or the presence of a subcortical U-fiber lesion; or a Dawson's finger-type lesion,” which could distinguish patients with multiple sclerosis from those with NMOSD with 92% sensitivity, 96% specificity, 98% positive predictive value, and 86% negative predictive value.
Conclusion:
Careful inspection of the distribution and morphology of MRI brain lesions can distinguish RRMS and NMOSD.
doi:10.1212/WNL.0b013e3182887957
PMCID: PMC3656462  PMID: 23486868
4.  Whole-brain magnetic resonance spectroscopic imaging measures are related to disability in ALS 
Neurology  2013;80(7):610-615.
Objective:
To demonstrate the sensitivity of a recently developed whole-brain magnetic resonance spectroscopic imaging (MRSI) sequence to cerebral pathology and disability in amyotrophic lateral sclerosis (ALS), and compare with measures derived from diffusion tensor imaging.
Methods:
Whole-brain MRSI and diffusion tensor imaging were undertaken in 13 patients and 14 age-similar healthy controls. Mean N-acetylaspartate (NAA), fractional anisotropy, and mean diffusivity were extracted from the corticospinal tract, compared between groups, and then in relation to disability in the patient group.
Results:
Significant reductions in NAA were found along the course of the corticospinal tracts on whole-brain MRSI. There were also significant changes in fractional anisotropy (decreased) and mean diffusivity (increased) in the patient group, but only NAA showed a significant relationship with disability (r = 0.65, p = 0.01).
Conclusion:
Whole-brain MRSI has potential as a quantifiable neuroimaging marker of disability in ALS. It offers renewed hope for a neuroimaging outcome measure with the potential for harmonization across multiple sites in the context of a therapeutic trial.
doi:10.1212/WNL.0b013e318281ccec
PMCID: PMC3590062  PMID: 23325907
5.  A marginalized MAP approach and EM optimization for pair-wise registration 
We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.
PMCID: PMC3681203  PMID: 17633738
6.  Identification of common variants associated with human hippocampal and intracranial volumes 
Stein, Jason L | Medland, Sarah E | Vasquez, Alejandro Arias | Hibar, Derrek P | Senstad, Rudy E | Winkler, Anderson M | Toro, Roberto | Appel, Katja | Bartecek, Richard | Bergmann, Ørjan | Bernard, Manon | Brown, Andrew A | Cannon, Dara M | Chakravarty, M Mallar | Christoforou, Andrea | Domin, Martin | Grimm, Oliver | Hollinshead, Marisa | Holmes, Avram J | Homuth, Georg | Hottenga, Jouke-Jan | Langan, Camilla | Lopez, Lorna M | Hansell, Narelle K | Hwang, Kristy S | Kim, Sungeun | Laje, Gonzalo | Lee, Phil H | Liu, Xinmin | Loth, Eva | Lourdusamy, Anbarasu | Mattingsdal, Morten | Mohnke, Sebastian | Maniega, Susana Muñoz | Nho, Kwangsik | Nugent, Allison C | O’Brien, Carol | Papmeyer, Martina | Pütz, Benno | Ramasamy, Adaikalavan | Rasmussen, Jerod | Rijpkema, Mark | Risacher, Shannon L | Roddey, J Cooper | Rose, Emma J | Ryten, Mina | Shen, Li | Sprooten, Emma | Strengman, Eric | Teumer, Alexander | Trabzuni, Daniah | Turner, Jessica | van Eijk, Kristel | van Erp, Theo G M | van Tol, Marie-Jose | Wittfeld, Katharina | Wolf, Christiane | Woudstra, Saskia | Aleman, Andre | Alhusaini, Saud | Almasy, Laura | Binder, Elisabeth B | Brohawn, David G | Cantor, Rita M | Carless, Melanie A | Corvin, Aiden | Czisch, Michael | Curran, Joanne E | Davies, Gail | de Almeida, Marcio A A | Delanty, Norman | Depondt, Chantal | Duggirala, Ravi | Dyer, Thomas D | Erk, Susanne | Fagerness, Jesen | Fox, Peter T | Freimer, Nelson B | Gill, Michael | Göring, Harald H H | Hagler, Donald J | Hoehn, David | Holsboer, Florian | Hoogman, Martine | Hosten, Norbert | Jahanshad, Neda | Johnson, Matthew P | Kasperaviciute, Dalia | Kent, Jack W | Kochunov, Peter | Lancaster, Jack L | Lawrie, Stephen M | Liewald, David C | Mandl, René | Matarin, Mar | Mattheisen, Manuel | Meisenzahl, Eva | Melle, Ingrid | Moses, Eric K | Mühleisen, Thomas W | Nauck, Matthias | Nöthen, Markus M | Olvera, Rene L | Pandolfo, Massimo | Pike, G Bruce | Puls, Ralf | Reinvang, Ivar | Rentería, Miguel E | Rietschel, Marcella | Roffman, Joshua L | Royle, Natalie A | Rujescu, Dan | Savitz, Jonathan | Schnack, Hugo G | Schnell, Knut | Seiferth, Nina | Smith, Colin | Steen, Vidar M | Valdés Hernández, Maria C | Van den Heuvel, Martijn | van der Wee, Nic J | Van Haren, Neeltje E M | Veltman, Joris A | Völzke, Henry | Walker, Robert | Westlye, Lars T | Whelan, Christopher D | Agartz, Ingrid | Boomsma, Dorret I | Cavalleri, Gianpiero L | Dale, Anders M | Djurovic, Srdjan | Drevets, Wayne C | Hagoort, Peter | Hall, Jeremy | Heinz, Andreas | Jack, Clifford R | Foroud, Tatiana M | Le Hellard, Stephanie | Macciardi, Fabio | Montgomery, Grant W | Poline, Jean Baptiste | Porteous, David J | Sisodiya, Sanjay M | Starr, John M | Sussmann, Jessika | Toga, Arthur W | Veltman, Dick J | Walter, Henrik | Weiner, Michael W | Bis, Joshua C | Ikram, M Arfan | Smith, Albert V | Gudnason, Vilmundur | Tzourio, Christophe | Vernooij, Meike W | Launer, Lenore J | DeCarli, Charles | Seshadri, Sudha | Andreassen, Ole A | Apostolova, Liana G | Bastin, Mark E | Blangero, John | Brunner, Han G | Buckner, Randy L | Cichon, Sven | Coppola, Giovanni | de Zubicaray, Greig I | Deary, Ian J | Donohoe, Gary | de Geus, Eco J C | Espeseth, Thomas | Fernández, Guillén | Glahn, David C | Grabe, Hans J | Hardy, John | Hulshoff Pol, Hilleke E | Jenkinson, Mark | Kahn, René S | McDonald, Colm | McIntosh, Andrew M | McMahon, Francis J | McMahon, Katie L | Meyer-Lindenberg, Andreas | Morris, Derek W | Müller-Myhsok, Bertram | Nichols, Thomas E | Ophoff, Roel A | Paus, Tomas | Pausova, Zdenka | Penninx, Brenda W | Potkin, Steven G | Sämann, Philipp G | Saykin, Andrew J | Schumann, Gunter | Smoller, Jordan W | Wardlaw, Joanna M | Weale, Michael E | Martin, Nicholas G | Franke, Barbara | Wright, Margaret J | Thompson, Paul M
Nature genetics  2012;44(5):552-561.
Identifying genetic variants influencing human brain structures may reveal new biological mechanisms underlying cognition and neuropsychiatric illness. The volume of the hippocampus is a biomarker of incipient Alzheimer’s disease1,2 and is reduced in schizophrenia3, major depression4 and mesial temporal lobe epilepsy5. Whereas many brain imaging phenotypes are highly heritable6,7, identifying and replicating genetic influences has been difficult, as small effects and the high costs of magnetic resonance imaging (MRI) have led to underpowered studies. Here we report genome-wide association meta-analyses and replication for mean bilateral hippocampal, total brain and intracranial volumes from a large multinational consortium. The intergenic variant rs7294919 was associated with hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16) and the expression levels of the positional candidate gene TESC in brain tissue. Additionally, rs10784502, located within HMGA2, was associated with intracranial volume (12q14.3; N = 15,782; P = 1.12 × 10−12). We also identified a suggestive association with total brain volume at rs10494373 within DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
doi:10.1038/ng.2250
PMCID: PMC3635491  PMID: 22504417
7.  Physiological Noise in Brainstem fMRI 
The brainstem is directly involved in controlling blood pressure, respiration, sleep/wake cycles, pain modulation, motor, and cardiac output. As such it is of significant basic science and clinical interest. However, the brainstem’s location close to major arteries and adjacent pulsatile cerebrospinal fluid filled spaces, means that it is difficult to reliably record functional magnetic resonance imaging (fMRI) data from. These physiological sources of noise generate time varying signals in fMRI data, which if left uncorrected can obscure signals of interest. In this Methods Article we will provide a practical introduction to the techniques used to correct for the presence of physiological noise in time series fMRI data. Techniques based on independent measurement of the cardiac and respiratory cycles, such as retrospective image correction (RETROICOR, Glover et al., 2000), will be described and their application and limitations discussed. The impact of a physiological noise model, implemented in the framework of the general linear model, on resting fMRI data acquired at 3 and 7 T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimize the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modeling noise sources, and its effect on statistical inference via loss of degrees of freedom, and non-orthogonality of regressors, is given. Lastly, different strategies for assessing the benefit of different approaches to physiological noise modeling are presented.
doi:10.3389/fnhum.2013.00623
PMCID: PMC3790256  PMID: 24109446
brainstem; physiological noise; fMRI; imaging; 7 T
8.  A combined post-mortem magnetic resonance imaging and quantitative histological study of multiple sclerosis pathology 
Brain  2012;135(10):2938-2951.
Multiple sclerosis is a chronic inflammatory neurological condition characterized by focal and diffuse neurodegeneration and demyelination throughout the central nervous system. Factors influencing the progression of pathology are poorly understood. One hypothesis is that anatomical connectivity influences the spread of neurodegeneration. This predicts that measures of neurodegeneration will correlate most strongly between interconnected structures. However, such patterns have been difficult to quantify through post-mortem neuropathology or in vivo scanning alone. In this study, we used the complementary approaches of whole brain post-mortem magnetic resonance imaging and quantitative histology to assess patterns of multiple sclerosis pathology. Two thalamo-cortical projection systems were considered based on their distinct neuroanatomy and their documented involvement in multiple sclerosis: lateral geniculate nucleus to primary visual cortex and mediodorsal nucleus of the thalamus to prefrontal cortex. Within the anatomically distinct thalamo-cortical projection systems, magnetic resonance imaging derived cortical thickness was correlated significantly with both a measure of myelination in the connected tract and a measure of connected thalamic nucleus cell density. Such correlations did not exist between these markers of neurodegeneration across different thalamo-cortical systems. Magnetic resonance imaging lesion analysis depicted clearly demarcated subcortical lesions impinging on the white matter tracts of interest; however, quantitation of the extent of lesion-tract overlap failed to demonstrate any appreciable association with the severity of markers of diffuse pathology within each thalamo-cortical projection system. Diffusion-weighted magnetic resonance imaging metrics in both white matter tracts were correlated significantly with a histologically derived measure of tract myelination. These data demonstrate for the first time the relevance of functional anatomical connectivity to the spread of multiple sclerosis pathology in a ‘tract-specific’ pattern. Furthermore, the persisting relationship between metrics from post-mortem diffusion-weighted magnetic resonance imaging and histological measures from fixed tissue further validates the potential of imaging for future neuropathological studies.
doi:10.1093/brain/aws242
PMCID: PMC3470716  PMID: 23065787
multiple sclerosis; post-mortem imaging; diffusion imaging; white matter tracts; neurodegeneration
9.  A Bayesian model of shape and appearance for subcortical brain segmentation 
NeuroImage  2011;56(3):907-922.
Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer’s disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.
doi:10.1016/j.neuroimage.2011.02.046
PMCID: PMC3417233  PMID: 21352927
Segmentation; Classification; Bayesian; Subcortical structures; Shape model
10.  Target Identification for Stereotactic Thalamotomy Using Diffusion Tractography 
PLoS ONE  2012;7(1):e29969.
Background
Stereotactic targets for thalamotomy are usually derived from population-based coordinates. Individual anatomy is used only to scale the coordinates based on the location of some internal guide points. While on conventional MR imaging the thalamic nuclei are indistinguishable, recently it has become possible to identify individual thalamic nuclei using different connectivity profiles, as defined by MR diffusion tractography.
Methodology and Principal Findings
Here we investigated the inter-individual variation of the location of target nuclei for thalamotomy: the putative ventralis oralis posterior (Vop) and the ventral intermedius (Vim) nucleus as defined by probabilistic tractography. We showed that the mean inter-individual distance of the peak Vop location is 7.33 mm and 7.42 mm for Vim. The mean overlap between individual Vop nuclei was 40.2% and it was 31.8% for Vim nuclei. As a proof of concept, we also present a patient who underwent Vop thalamotomy for untreatable tremor caused by traumatic brain injury and another patient who underwent Vim thalamotomy for essential tremor. The probabilistic tractography indicated that the successful tremor control was achieved with lesions in the Vop and Vim respectively.
Conclusions
Our data call attention to the need for a better appreciation of the individual anatomy when planning stereotactic functional neurosurgery.
doi:10.1371/journal.pone.0029969
PMCID: PMC3251609  PMID: 22238685
11.  Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner 
Neuroimage  2011;57(1-4):167-181.
Diffusion imaging of post mortem brains has great potential both as a reference for brain specimens that undergo sectioning, and as a link between in vivo diffusion studies and “gold standard” histology/dissection. While there is a relatively mature literature on post mortem diffusion imaging of animals, human brains have proven more challenging due to their incompatibility with high-performance scanners. This study presents a method for post mortem diffusion imaging of whole, human brains using a clinical 3-Tesla scanner with a 3D segmented EPI spin-echo sequence. Results in eleven brains at 0.94 × 0.94 × 0.94 mm resolution are presented, and in a single brain at 0.73 × 0.73 × 0.73 mm resolution. Region-of-interest analysis of diffusion tensor parameters indicate that these properties are altered compared to in vivo (reduced diffusivity and anisotropy), with significant dependence on post mortem interval (time from death to fixation). Despite these alterations, diffusion tractography of several major tracts is successfully demonstrated at both resolutions. We also report novel findings of cortical anisotropy and partial volume effects.
Research highlights
► Acquisition and processing protocols for diffusion MRI of post-mortem human brains. ► Effect of post-mortem and scan intervals on diffusion indices. ► Tractography in post-mortem human brains. ► Radial diffusion anisotropy in cortical gray matter.
doi:10.1016/j.neuroimage.2011.03.070
PMCID: PMC3115068  PMID: 21473920
Diffusion tensor imaging; Tractography; Post mortem; Human; Brain
12.  Informatics and Data Mining Tools and Strategies for the Human Connectome Project 
The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: (1) storage of primary and processed data, (2) systematic processing and analysis of the data, (3) open-access data-sharing, and (4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda.
doi:10.3389/fninf.2011.00004
PMCID: PMC3127103  PMID: 21743807
connectomics; Human Connectome Project; XNAT; caret; resting state fMRI; diffusion imaging; network analysis; brain parcellation
13.  Atlas-based Improved Prediction of Magnetic Field Inhomogeneity for Distortion Correction of EPI data 
We describe a method for atlas-based segmentation of structural MRI for calculation of magnetic fieldmaps. CT data sets are used to construct a probabilistic atlas of the head and corresponding MR is used to train a classifier that segments soft tissue, air, and bone. Subject-specific fieldmaps are computed from the segmentations using a perturbation field model. Previous work has shown that distortion in echo-planar images can be corrected using predicted fieldmaps. We obtain results that agree well with acquired fieldmaps: 90% of voxel shifts from predicted fieldmaps show subvoxel disagreement with those computed from acquired fieldmaps. In addition, our fieldmap predictions show statistically significant improvement following inclusion of the atlas.
PMCID: PMC2895313  PMID: 20426203
14.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration 
NeuroImage  2009;46(3):786-802.
All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms (“SPM2-type” and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
doi:10.1016/j.neuroimage.2008.12.037
PMCID: PMC2747506  PMID: 19195496
15.  Fieldmap-Free Retrospective Registration and Distortion Correction for EPI-based Functional Imaging 
We describe a method for correcting the distortions present in echo planar images (EPI) and registering the EPI to structural MRI. A field map is predicted from an air/tissue segmentation of the MRI using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration. We obtain results that are similar to those obtained using fieldmaps, however neither fieldmaps, nor knowledge of shim coefficients is required.
PMCID: PMC2702772  PMID: 18982615
16.  Applying FSL to the FIAC Data: Model-Based and Model-Free Analysis of Voice and Sentence Repetition Priming 
Human brain mapping  2006;27(5):380-391.
This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM’05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing.
doi:10.1002/hbm.20246
PMCID: PMC2653076  PMID: 16565953
functional magnetic resonance imaging (FMRI); independent component analysis (ICA); linear modeling; Functional Image Analysis Contest (FIAC)

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