Os autores gostariam de agradecer o apoio da Fundação CAMH, graças a Michael e Sonja Koerner, a Família Kimel, eo Paul E. Garfinkel New Investigator Award Catalyst. Este projecto foi financiado pelo Fonds de Recherches Santé Québec, Institutos Canadenses de Pesquisa em Saúde (CIHR), as Ciências Naturais e Council of Canada, o Instituto do Cérebro Weston, sociedade do Alzheimer de Canadá Pesquisa de Engenharia, eo Micheal J. Fox Foundation para a pesquisa de Parkinson (MMC), bem como CIHR, a Fundação de Saúde Mental Ontario, NARSAD, e do Instituto Nacional de Saúde Mental (R01MH099167) (ANV). Os autores também gostaria de agradecer a Anusha Ravichandran para assistência a aquisição das imagens.

Os participantes do estudo O protocolo neste manuscrito foi desenvolvido para cinco imagens representativas de alta resolução coletadas de voluntários saudáveis ​​(3F, 2M; 29-57 anos, 37 avg.) Que estavam livres de doenças neurológicas e neuropsiquiátricas e os casos de traumatismo craniano grave. Todos os indivíduos foram recrutados no Centro de Dependência e Saúde Mental (CAMH). O estudo foi aprovado pelo Conselho de Ética em Pesquisa CAMH e foi realizado em conformidade com a Declaração de Helsinki. Todos os indivíduos forneceram escrito, o consentimento informado para aquisição de dados e de partilha. Para obter detalhes sobre a sequência de aquisição utilizado para recolher estas imagens, consulte Winterburn et al., 2013 e Park et al., 2014. 26,34 Imagens para todos os cinco indivíduos foram verificados quanto à qualidade e mantida. O hipocampo durou uma média de 118 cortes coronais nestas imagens. 1. Software Set-up <ol><li> Abra Display: </strong&gt; A partir do terminal usando o seguinte comando: Indicação image_name.mnc -label label_name.mnc. O programa será aberto 3 janelas: imagem 3-orientação da janela de visualização 3D, janela de visualização, e uma janela de navegação. O terminal também será usada para executar o programa. Ampliar a visão coronal, como as segmentações será realizada coronalmente. Zoom sobre o hipocampo. Selecione F (Segmentação) na janela de navegação. Selecione F (XY Radius: 0,1). A janela de terminal irá pedir para o usuário &quot;Enter tamanho xy escova:&quot;. Situado a 0,1. Isto irá definir o tamanho do seu pincel. O usuário pode agora começar a desenhar o hipocampo na imagem MR. </li></ol> 2. Whole Hippocampus manual Segmentação <ol><li> Set-up: Usando uma imagem ponderada em T1, vá até a fatia anterior-mais coronal do hipocampo. Para avançar fatias na direcção anterior, use a tecla &#39;+&#39;; use a tecla - para se mover na direção posterior &#39;&#39;. </li><li><strong&gt; Fatia A: Anterior-Most Slice: Usando o botão direito do mouse sobre o mouse, desenhar a borda mais externa da matéria cinzenta no hipocampo, onde se encontra com a substância branca do lobo temporal circundante e usar a substância branca de alta intensidade do alveus Para apoiar a borda superior, em que o hipocampo encontra a amígdala 12,22. Use a tecla E (Etiqueta de preenchimento) no menu de segmentação da janela de navegação para preencher a etiqueta dentro da fronteira. Continuar a aplicar essas fronteiras em toda a cabeça do hipocampo anterior. </li><li> Fatia B: Hippocampal Cabeça 1 (Figura 1B): <ol><li> Superior, inferior, lateral, medial fronteiras: Continue a desenhar as fronteiras como descrito no passo 2.2, utilizando a substância branca do lobo temporal e alveus como um guia. </li><li> Border supero-medial: Para isso, utilizando a vista axial, desenhe uma linha horizontal a partir da borda anterior do hipocampo lateral, 29, e incluir qualquer coisa abaixo dessa linha como hipocampo.NOTA: A fronteira supero-medial se torna mais ambíguo nessas fatias, onde a matéria cinzenta do hipocampo se mistura com a matéria cinzenta da amígdala. </li></ol></li><li> Fatia C: Hippocampal Head 2 com dentições: Dependendo do tema, as dentições do hipocampo pode ser visível por 3-4 fatias (normalmente, eles são mais visíveis em relação imagens T1-ponderadas em T2). Nestes fatias, continuar a utilizar a substância branca do alveus e lobo temporal para orientar fronteira segmentação 12,22. Para mais detalhes, siga os passos 2.5.1-2.5.2. </li><li> Fatia D: Cabeça Hippocampal 3: <ol><li> Superiores, inferiores, laterais, bordas mediais: Desenhar a borda inferior do hipocampo na substância branca do lobo temporal, a borda lateral no corno inferior do ventrículo lateral, a borda superior, seguindo a curva das dentições, no substância branca do alveus / fímbria, ea fronteira medial na regio hipointenson da cisterna ambiente 12,22. </li><li> Supero-medial e fronteiras ínfero-medial: Continuar a definir a fronteira supero-medial, como descrito no passo 2.3.2. Desenhe a parte inferior da borda medial onde o hipocampo dilui um pouco e se estende até a massa cinzenta levemente hiperintenso do córtex entorrinal 12,22. </li></ol></li><li> Fatia E: Hippocampal Head 4 com uncus: Continue a desenhar as bordas inferiores, laterais e superiores descritas nos passos 2.5.1-2.5.2. Incluir o unco (que está localizado medalha para o corpo principal do hipocampo e está rodeado por uma baixa intensidade-CSF) no hipocampo segmentação 12,2 2. </li><li> Fatia F: Hippocampal Corpo: Continue a desenhar as bordas inferiores, laterais, medial e superiores descritas nos passos 2.5.1-2.5.2. Desenhar a borda ínfero-medial no ponto onde o hipocampo dilui como que ele passa para o córtex entorrinal / para-hipocampal giro 12,22.Não inclua o CSF ​​baixa intensidade do sulco hipocampal vestigial na segmentação. </li><li> Fatia G: Cauda Hippocampal 1: comece segmentando fatias do tipo cauda do hipocampo quando a crus da fornix é primeiro visível. Excluir giro fascicular (uma estrutura de matéria cinzenta que se mistura com o hipocampo em partes da cauda do hipocampo) a partir da segmentação por extrapolação a forma do giro fascicular na cauda do hipocampo a partir de mais fatias anteriores 12,22. Esta extrapolação não é possível apenas por 2-3 fatias, após o que as duas estruturas não podem ser distinguidos com precisão; Neste ponto, toda a matéria cinzenta tratar visível nesta área como hipocampo. </li><li> Fatia H: Cauda Hippocampal 2: Segmento da substância cinzenta de baixa intensidade da cauda do hipocampo posterior da substância branca de alta intensidade circundante. </li><li> Fatia I: Posterior-Most Slice: Segmento pequena área remanescente de substância cinzenta no hipocampo dea substância branca torno do lóbulo temporal. </li></ol> 3. Hippocampal Subfield manual Segmentação <ol><li> Set-up: Usando uma imagem ponderada em T2, vá até a fatia anterior-mais coronal do hipocampo (como no passo 2.1). Para mudar a cor do pincel, selecione D (Set Pintura Lbl :) no menu segmentação na janela de navegação. O terminal de comando irá pedir: &quot;Entre etiqueta pintura atual:&quot;. Digite um número entre 1 e 255. Cada número corresponde a uma cor rótulo diferente. </li><li> Fatia A: Anterior-Most Slice: Desde divisões subcampo não são ainda visíveis na anterior-maior fatia, desenhe uma linha dividindo a matéria visível do hipocampo cinza ao longo do seu eixo visível mais longo (que não é necessariamente paralelo com qualquer dos eixos cardinais) em duas secções iguais para aproximar a verdadeira 12,22 anatomia. Rotular o superior dessas duas seções como CA1 ea seção inferior como subiculum por choosing uma etiqueta de cor diferente para cada subcampo 23,35. </li><li> Fatia B: Hippocampal Cabeça 1: Identifique a área de baixa intensidade no meio da formação do hipocampo como SR / SL / SM 13,37. Quando a curva ao longo da borda inferior do hipocampo se torna claro, usar esse marco como a borda lateral separando o subiculum do CA1 12,22. Continue a seguir o eixo mais longo do hipocampo para desenhar a borda CA1-subiculum na ponta supero-medial 37. </li><li> Fatia C: Cabeça Hippocampal 2 com dentições: <ol><li> SR / SL / SM, CA4 / DG, e subiculum: Rotular o SR / SL / SM, CA4 / DG, e subiculum como descrito para a fatia D (passo 3.5.1). </li><li> CA2 / CA3 e CA1: definir a fronteira entre CA1 e CA2 / CA3 como uma linha de ângulo de 45º se estende na direcção do supero-lateral a partir da extremidade mais súpero-lateral do SR / SL / SM 12,22. Estender a CA2 / CA3 medial ao longo da borda superior à calha entre a dentações 12,22. Rotular o resto da borda superior como CA1 12,22. </li></ol></li><li> Fatia D: Hippocampal Head 3 <ol><li> SR / SL / SM, CA4 / DG, e subiculum: Rotular a banda / SL / SM SR escuro primeira, que vai seguir a curva do CA1 37. Rotular qualquer matéria cinzenta de alta intensidade dentro do SR / SL / SM como CA4 / DG 12,22,23,35,37. Isto pode não ser uma região contínua, como na Figura 2C. Continuar a definir a fronteira subiculum-CA1 usando a curva no hipocampo inferiores 12,22. </li><li> CA2 / CA3 e CA1: Continuar a definir o CA1 e CA2 / CA3 fronteira como no passo 3.4.2. Estender a CA2 / CA3 medially a meio caminho ao longo da borda superior do hipocampo 12,22 e rotular a outra metade da borda superior como CA1 12,22. </li><li> Cabeça do hipocampo supero-medial: Neste fatia, dividir a cabeça do hipocampo supero-medial verticalmente pela metade. Rotular a metade medial como SR / SL / SM 12. Divida lateralmeia em meia novamente, desta vez na horizontal. Rotular a parte superior como CA4 / DG ea parte inferior como CA2 / CA3 12. </li></ol></li><li> Fatia E: Hippocampal Head 4 com uncus <ol><li> Cabeça lateral do hipocampo (subículo): na porção lateral destas fatias, definir a fronteira subículo-CA1 como uma linha vertical que se estende na direcção inferior da borda mais medial do CA4 / DG 12,22. </li><li> Cabeça lateral do hipocampo (CA1, CA2 / CA3, CA4 / DG, SR / SL / SM.): Definir a fronteira CA1-CA2 / CA3 do mesmo modo como no passo 3.4.2. Continue a rotular o SR / SL / SM como a região de baixa intensidade após a curva das regiões CA. Rotular o CA4 / DG como a cavidade centro dentro do SR / SL / SM, como no passo 3.5.1. </li><li> Cabeça do hipocampo Uncal (SR / SL / SM): Rotular o uncus do hipocampo durante cerca de 10 fatias como as transições de cabeça do hipocampo no corpo do hipocampo. No uncus, rotular a região de baixa intensidade no centro, como SR / SL / SM (quando este é difícil de ver, aproximar a anatomia segmentando uma linha de 2-3 voxels-se largamente acima do centro da uncus) 12. </li><li> Cabeça do hipocampo Uncal (CA2 / CA3, CA4 / DG): Desenhar uma linha na extremidade superior da secção SR / SL / SM junto infero-lateral / eixo supero-medial do uncus. Rotular toda a matéria cinzenta acima desta linha como CA2 / CA3 12. Rotular qualquer matéria cinzenta não marcado abaixo dessa linha (de cada lado da SR / SL / SM) como CA4 / DG 12. </li></ol></li><li> Fatia F: Hippocampal Corpo: Continuar a aplicar os limites descritos na etapa 3.6.1-3.6.2. </li><li> Fatia G: Cauda Hippocampal 1: Continuar a aplicar as regras descritas na etapa 3.6.1-3.6.2. A fronteira subículo-CA1 torna-se um ângulo de 45º linha que se estende na direcção infero-medial do bordo medial do CA4 / DG 12,22. </li><li> Fatia H: Cauda Hippocampal 2: Uma vez que o giro fasciculares já não pode ser distinguido do formatio hipocampoN, rotular toda a camada exterior como CA1, a área de baixa intensidade dentro desta como SR / SL / SM (como em fatias anteriores), e qualquer matéria cinzenta remanescente no meio como CA4 / DG 12,22. </li><li> Fatia I: Posterior-maioria Fatia: Uma vez que o escuro SR / SL / SM não é mais visível no centro da formação do hipocampo, rotular toda a estrutura como CA1 12,22. </li></ol> 4. Protocolo de Confiabilidade <ol><li> Resegmentar direito ou hipocampo deixou de cada sujeito, depois de esperar cerca de um mês de realizar a segmentação inicial. Segmento todos os subcampos ao longo de todo o comprimento ântero-posterior do hipocampo, tentando seguir as regras de protocolo de forma tão consistente quanto possível. </li><li> Calcule kappa da Dice entre os volumes originais e resegmented: <img alt="Equação 1" src="/files/ftp_upload/51861/51861eq1.jpg" /> onde k = kappa de Dados e A e B são volumes de etiqueta. </li></ol>

<ol>
	<li>Adler, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+of+the+human+hippocampus+in+3D+from+histology+and+high-resolution+ex-vivo+MRI.">Reconstruction of the human hippocampus in 3D from histology and high-resolution ex-vivo MRI.</a> IEEE Intl. Symp. on Biomed. Img. , 294-297 (2012).</li><li>Adler, D. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histology-derived+volumetric+annotation+of+the+human+hippocampal+subfields+in+postmortem+MRI.">Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI.</a> NeuroImage. 84 (1), 505-523 (2014).</li><li>Amaral, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+golgi+study+of+cell+types+in+the+hilar+region+of+the+hippocampus+in+the+rat.">A golgi study of cell types in the hilar region of the hippocampus in the rat.</a> J. Comp. Neurol. 182 (4 Pt 2), 851-914 (1978).</li><li>Blumberg, H. P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amygdala+and+Hippocampal+Volumes+in+Adolescents+and+Adults+With+Bipolar+Disorder.">Amygdala and Hippocampal Volumes in Adolescents and Adults With Bipolar Disorder.</a> Arch Gen Psychiatry. 60 (12), 1201-1208 (2003).</li><li>Braak, H., Braak, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuropathological+stageing+of+Alzheimer-related+changes.">Neuropathological stageing of Alzheimer-related changes.</a> Acta Neuropathol . 82 (4), 239-259 (1991).</li><li>Boccardi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Survey+of+protocols+for+the+manual+segmentation+of+the+hippocampus:+preparatory+steps+towards+a+joint+EADC-ADNI+harmonized+protocol.">Survey of protocols for the manual segmentation of the hippocampus: preparatory steps towards a joint EADC-ADNI harmonized protocol.</a> J. Alzheimer's Dis. 26 (3), 61-75 (2011).</li><li>Chakravarty, M. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performing+label-fusion-based+segmentation+using+multiple+automatically+generated+templates.">Performing label-fusion-based segmentation using multiple automatically generated templates.</a> Hum. Brain Mapp. 34 (10), 2635-2654 (2013).</li><li>Chakravarty, M. M., Bertrand, G., Hodge, C. P., Sadikot, A. F., Collins, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+creation+of+a+brain+atlas+for+image+guided+neurosurgery+using+serial+histological+data.">The creation of a brain atlas for image guided neurosurgery using serial histological data.</a> NeuroImage. 30 (2), 359-376 (2006).</li><li>Collins, D. L., Neelin, P., Peters, T. M., Evans, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+3D+intersubject+registration+of+MR+volumetric+data+in+standardized+Talairach+space.">Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space.</a> J. Comput. Assist. Tomogr. 18 (2), 192-205 (1994).</li><li>Heijer, F. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+and+diffusion+MRI+measures+of+the+hippocampus+and+memory+performance.">Structural and diffusion MRI measures of the hippocampus and memory performance.</a> NeuroImage. 63 (4), 1782-1789 (2012).</li><li>Duncan, K., Tompary, A., Davachi, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Associative+encoding+and+retrieval+are+predicted+by+functional+connectivity+in+distinct+hippocampal+area+ca1+pathways.">Associative encoding and retrieval are predicted by functional connectivity in distinct hippocampal area ca1 pathways.</a> The Journal of Neuroscience. 34 (34), 11188-11198 (2014).</li><li>Duvernoy, H. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The Human Hippocampus: Functional Anatomy Vascularization, and Serial Sections with MRI. , (2005).</li><li>Fatterpekar, G. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytoarchitecture+of+the+human+cerebral+cortex:+MR+microscopy+of+excised+specimens+at+9.4+Tesla.">Cytoarchitecture of the human cerebral cortex: MR microscopy of excised specimens at 9.4 Tesla.</a> Am. J. Neuroradiol. 23 (8), 1313-1321 (2002).</li><li>Frey, S., Pandya, D. N., Chakravarty, M. M., Bailey, L., Petrides, M., Collins, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+MRI+based+average+macaque+monkey+stereotaxic+atlas+and+space+(MNI+monkey+space).">An MRI based average macaque monkey stereotaxic atlas and space (MNI monkey space).</a> NeuroImage. 55 (4), 1435-1442 (2011).</li><li>Goubran, M., Crukley, C., de Ribaupierre, S., Peters, T. M., Khan, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+registration+of+ex-vivo.+MRI+to+sparsely+sectioned+histology+of+hippocampal+and+neocortical+temporal+lobe+specimens.">Image registration of ex-vivo. MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens.</a> NeuroImage. 83, 770-781 (2013).</li><li>Heckemann, R. A., Hajnal, J. V., Aljabar, P., Rueckert, D., Hammers, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automatic+anatomical+brain+MRI+segmentation+combining+label+propagation+and+decision+fusion.">Automatic anatomical brain MRI segmentation combining label propagation and decision fusion.</a> NeuroImage. 33 (1), 115-126 (2006).</li><li>Holmes, C. J., Hoge, R., Collins, L., Woods, R., Toga, A. W., Evans, A. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancement+of+MR+images+using+registration+for+signal+averaging.">Enhancement of MR images using registration for signal averaging.</a> J. Comput. Assist. Tomogr. 22 (2), 324-333 (1998).</li><li>Karnik-Henry, M. S., Wang, L., Barch, D. M., Harms, M. P., Campanella, C., Csernansky, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medial+temporal+lobe+structure+and+cognition+in+individuals+with+schizophrenia+and+in+their+non-psychotic+siblings.">Medial temporal lobe structure and cognition in individuals with schizophrenia and in their non-psychotic siblings.</a> Schizophrenia Research. 138 (2-3), 128-135 (2012).</li><li>Kim, J. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+3-D+extraction+and+evaluation+of+the+inner+and+outer+cortical+surfaces+using+a+Laplacian+map+and+partial+volume+effect+classification.">Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification.</a> NeuroImage. 27 (1), 210-221 (2005).</li><li>La Joie, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+effect+of+age+on+hippocampal+subfields+assessed+using+a+new+high-resolution+3T+MR+sequence.">Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence.</a> NeuroImage. 53 (2), 506-514 (2010).</li><li>Libby, L. A., Ekstrom, A. D., Ragland, J. D., Ranganath, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Differential+connectivity+of+perirhinal+and+parahippocampal+cortices+within+human+hippocampal+subregions+revealed+by+high-resolution+functional+imaging.">Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging.</a> The Journal of Neuroscience. 32 (19), 6550-6560 (2012).</li><li>Mai, J. K., Paxinos, G., Voss, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Atlas of the Human Brain. , (2008).</li><li>Mueller, S. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+of+hippocampal+subfields+and+age-related+changes+with+high+resolution+MRI+at+4T.">Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T.</a> Neurobiol Aging. 28 (5), 719-726 (2006).</li><li>Narr, K. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regional+specificity+of+hippocampal+volume+reductions+in+first-episode+schizophrenia.">Regional specificity of hippocampal volume reductions in first-episode schizophrenia.</a> NeuroImage. 21 (4), 1563-1575 (2004).</li><li>Olsen, R. K., Palombo, D. J., Rabin, J. S., Levine, B., Ryan, J. D., Rosenbaum, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Volumetric+Analysis+of+Medial+Temporal+Lobe+Subregions+in+Development+Amnesia+using+High-Resolution+Magnetic+Resonance+Imaging.">Volumetric Analysis of Medial Temporal Lobe Subregions in Development Amnesia using High-Resolution Magnetic Resonance Imaging.</a> Hippocampus. 23 (10), 855-860 (2013).</li><li>Park, M. T. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Derivation+of+high-resolution+MRI+atlases+of+the+human+cerebellum+at+3T+and+segmentation+using+multiple+automatically+generated+templates.">Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates.</a> NeuroImage. 95, 217-231 (2014).</li><li>Pipitone, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-atlas+Segmentation+of+the+Whole+Hippocampus+and+Subfields+Using+Multiple+Automatically+Generated+Templates.">Multi-atlas Segmentation of the Whole Hippocampus and Subfields Using Multiple Automatically Generated Templates.</a> NeuroImage. 101, 494-512 (2014).</li><li>Pluta, J., Yushkevich, P., Das, S., Wolk, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+vivo+analysis+of+hippocampal+subfield+atrophy+in+mild+cognitive+impairment+via+semi-automatic+segmentation+of+T2-weighted+MRI.Journal+of+Alzheimer's+Disease.">In vivo analysis of hippocampal subfield atrophy in mild cognitive impairment via semi-automatic segmentation of T2-weighted MRI.Journal of Alzheimer's Disease.</a> 31 (1), 85-99 (2012).</li><li>Pruessner, J. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Volumetry+of+hippocampus+and+amygdala+with+high-resolution+MRI+and+three-+dimensional+analysis+software:+minimizing+the+discrepancies+between+laboratories.">Volumetry of hippocampus and amygdala with high-resolution MRI and three- dimensional analysis software: minimizing the discrepancies between laboratories.</a> Cereb Cortex. 10 (4), 433-442 (2000).</li><li>Sabuncu, M. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+dynamics+of+cortical+and+hippocampal+atrophy+in+Alzheimer+disease.">The dynamics of cortical and hippocampal atrophy in Alzheimer disease.</a> Archives of Neurology. 68 (8), 1040-1048 (2011).</li><li>Scoville, W. B., Milner, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Loss+of+recent+memory+after+bilateral+hippocampal+lesions.">Loss of recent memory after bilateral hippocampal lesions.</a> J. Neuropsych. and Clin. Neurosci. 12 (1), 103-113 (1957).</li><li>Toga, A. W., Thompson, P. M., Mori, S., Amunts, K., Zilles, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+multimodal+atlases+of+the+human+brain.">Towards multimodal atlases of the human brain.</a> Nat. Rev. Neurosci. 7 (12), 952-966 (2006).</li><li>van Leemput, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+segmentation+of+hippocampal+subfields+from+ultra-high+resolution+in+vivo.">Automated segmentation of hippocampal subfields from ultra-high resolution in vivo.</a> MRI. Hippocampus. 19 (6), 549-557 (2009).</li><li>Winterburn, J. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+in+vivo+atlas+of+human+hippocampal+subfields+using+high-resolution+3+T+magnetic+resonance+imaging.">A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging.</a> NeuroImage. 74, 254-265 (2013).</li><li>Wisse, L. E. M., Gerritsen, L., Zwanenburg, J. J. M., Kuijf, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Subfields+of+the+hippocampal+formation+at+7+T+MRI:+in+vivo.+volumetric+assessment.">Subfields of the hippocampal formation at 7 T MRI: in vivo. volumetric assessment.</a> NeuroImage. 61 (4), 1043-1049 (2012).</li><li>Yelnik, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+three-dimensional,+histological+and+deformable+atlas+of+the+human+basal+ganglia.+I.+Atlas+construction+based+on+immunohistochemical+and+MRI+data.">A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data.</a> NeuroImage. 34 (2), 618-638 (2007).</li><li>Yushkevich, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+high-resolution+computational+atlas+of+the+human+hippocampus+from+postmortem+magnetic+resonance+imaging+at+9.4+T.">A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T.</a> NeuroImage. 44 (2), 385-398 (2009).</li><li>Yushkevich, P. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Comparison+of+21+Protocols+for+Labeling+Hippocampal+Subfields+and+Parahippocampal+Subregions+in+In+Vivo+MRI:+Towards+a+Harmonized+Segmentation+Protocol.">Quantitative Comparison of 21 Protocols for Labeling Hippocampal Subfields and Parahippocampal Subregions in In Vivo MRI: Towards a Harmonized Segmentation Protocol.</a> NeuroImage. , (2015).</li></ol>

The authors have no conflicts of interest to declare.

Segmentação subcampo hipocampal em imagens de RM está bem representado na literatura. No entanto, os protocolos existentes excluir partes do hipocampo 20,23,33,35, aplicam-se apenas a imagens fixas 37, ou exigir scanners ultra-altas de campo para a aquisição de imagens 35,37. Este manuscrito apresenta um protocolo de segmentação, que inclui cinco principais subdivisões (CA1, CA2 / CA3, CA4 / giro dentado, SR / SL / SM, subículo e do hipocampo) e se estende por toda a extensão a...

O hipocampo é uma estrutura do lobo temporal medial amplamente estudado que está associada com a memória episódica, navegação espacial, e outras funções cognitivas 10,31. O seu papel em desordens neurodegenerativas e neuropsiquiátricos tais como doença de Alzheimer, esquizofrenia, transtorno bipolar e é bem documentada 4,5,18,24,30. O objetivo deste artigo é para fornecer detalhes adicionais ao protocolo de segmentação manual publicado anteriormente para 34 subcampos hipocampo humano em imagens de alta resolução ressonância magnética (RM) adquiridas em 3T. Além disso, o componente de vídeo que acompanha este manuscrito irá fornecer mais assistência a pesquisadores que desejem implementar o protocolo sobre os seus próprios conjuntos de dados. O hipocampo pode ser dividido em subcampos com base nas diferenças observadas na citoarquitetônicos histologicamente preparados post mortem espécimes 12,22. Tais amostras post mortem definir o ground verdade para a identificação e estudo dos subcampos do hipocampo; No entanto preparações desta natureza exigem aptidões e equipamento especializado para a coloração, e são limitados pela disponibilidade de tecido fixo, especialmente em populações de doentes. imagiologia in vivo tem a vantagem de um grupo muito maior de indivíduos, e também apresenta a oportunidade para acompanhamento -se estudos e mudanças de observação nas populações. Embora tenha sido demonstrado que as intensidades de sinal em imagens de RM ex vivo T2-ponderado refletir densidade celular 13, ainda é difícil identificar fronteiras incontestáveis ​​entre subcampos usando apenas intensidades de sinal de RM. Como tal, foram desenvolvidas uma série de abordagens diferentes para a identificação de detalhes ao nível de histologia em imagens de RM. Alguns grupos têm feito esforços para reconstruir e digitalizar conjuntos de dados histológicos e, em seguida, usar essas reconstruções, juntamente com técnicas de registro de imagem para localizar hipocampo neuroanat subcampoomy on in vivo MR 1,2,8,9,14,15,17,32. Embora esta seja uma técnica eficaz para o mapeamento de uma versão da verdade chão histológico diretamente em imagens de RM, reconstruções dessa natureza são difíceis de concluir. Projetos como estes são limitados pela disponibilidade de amostras intactas medial do lobo temporal, as técnicas histológicas, a perda de dados durante o processamento histológico e as inconsistências morfológicas fundamentais entre cérebros vivo fixos e em. Outros grupos usaram scanners de alto campo (7T ou 9.4T), em um esforço para adquirir in vivo ou ex vivo imagens com uma pequena o suficiente (0,20-0,35 mm isotrópico) tamanho do voxel de visualizar espacialmente localizada diferenças de contraste de imagem que são usados ​​para inferir limites entre subcampos 35,37. Mesmo em 7T-9.4T e com tal um pequeno tamanho do voxel, as características citoarquitetônicos de subcampos hipocampo não são visíveis. Como tal, os protocolos de segmentação manual têm sido desenvolvidos que umpproximate os limites histológicos conhecidos sobre imagens de RM. Estes protocolos determinam limites de subcampos por interpretar diferenças de contraste de imagem locais e definição de regras geométricas (tais como linhas retas e ângulos) em relação a estruturas visíveis. Embora as imagens tiradas com uma intensidade de campo alta são capazes de oferecer uma visão detalhada sobre subcampos hipocampo, scanners de alto campo ainda não são comuns em ambientes clínicos ou de pesquisa, por isso protocolos 7T e 9.4T atualmente têm aplicabilidade limitada. Protocolos semelhantes foram desenvolvidas para imagens recolhidas no 3T e 4T scanners 11,20,21,23,24,25,28,33. Muitos destes protocolos são baseados em imagens com dimensões voxels voxel sub-1mm no plano coronal, mas têm grandes espessuras de corte (0,8-3 mm) 11,20,21,23,25,28,33 ou grandes distâncias inter-slice 20,28, ambos os quais resultam em um erro de medição significativo na estimativa dos volumes dos subcampos individuais. Além disso, muitos dos protocolos existentes 3Texcluir subcampos em todo ou parte da cabeça ou cauda do hipocampo 20,23,25,33 ou não fornecer segmentações detalhadas de subestruturas importantes (ou seja, combinar o DG com CA2 / CA3 ou não incluem os estratos radiatum / lacunosum / moleculare de CA) 11,20,21,23,24,25,28,33. Existe portanto uma necessidade na área para uma descrição detalhada de um protocolo que pode identificar com segurança subcampos relevantes ao longo da cabeça, do corpo e cauda do hipocampo que se baseia em um scanner comumente disponível em ambientes clínicos e de investigação. Esforços estão em andamento pela subcampos Grupo Hippocampal (www.hippocampalsubfields.com) harmonizar o processo de segmentação do hipocampo subcampo entre laboratórios, semelhante a um esforço de harmonização existente para a segmentação do hipocampo inteiro 6, e um papel inicial comparando 21 protocolos existentes foi publicado recentemente 38 . O trabalho deste grupo irá elucidar proce segmentação idealmentos. Este manuscrito fornece instruções escritas e de vídeo de execução de forma confiável o protocolo de segmentação subcampo hipocampal descrito anteriormente por Winterburn e colegas 34 em imagens de alta resolução 3T MR. O protocolo foi implementado em cinco imagens de controles saudáveis ​​para toda a hipocampo e cinco subcampos CA1 do hipocampo (, CA2 / CA3, CA4 / giro dentado, estratos radiatum / lacunosum / moleculare, e subiculum). Estas imagens segmentadas estão disponíveis ao público em linha (cobralab.ca/atlases/Hippocampus). O protocolo e as imagens segmentadas será útil para grupos que desejam estudar neuroanatomia do hipocampo detalhada em imagens de RM.

<table border="0">
	<tbody>
		<tr>
			<td>Discovery MR750 3T</td>
			<td>GE</td>
			<td>NA</td>
			<td>Or equivalent 3T scanner</td>
		</tr>
		<tr>
			<td>Minc Tool Kit</td>
			<td>McConnell Brain Imaging Center, Montreal Neurological Institute</td>
			<td>NA</td>
			<td>Open source: http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit</td>
		</tr>
	</tbody>
</table>

high-resolution in vivo manual segmentation protocol for human hippocampal subfields using 3t magnetic resonance imaging

O hipocampo humano tem sido muito estudada no contexto da memória e função normal do cérebro e seu papel em diferentes transtornos neuropsiquiátricos tem sido fortemente estudado. Embora muitos estudos de imagiologia tratar o hipocampo como uma única estrutura neuroanatômico unitária, isto é, de facto, constituído por vários sub-campos que têm uma geometria tridimensional complexa. Como tal, é sabido que estes subcampos executar funções especializadas e são diferencialmente afectada através do curso de diferentes estados de doença. A ressonância magnética (RM) pode ser usado como uma ferramenta poderosa para interrogar a morfologia do hipocampo e seus subcampos. Muitos grupos utilizam software avançado de imagem e hardware (&gt; 3T) para a imagem dos subcampos; no entanto, este tipo de tecnologia pode não estar prontamente disponível na maioria dos centros de pesquisa clínica e de imagem. Para endereçar esta necessidade, neste manuscrito fornece um protocolo passo-a-passo detalhado para segmentar o comprimento ântero-posterior completado hipocampo e seus subcampos: cornu Ammonis (CA) 1, CA2 / CA3, CA4 / giro denteado (DG), estratos radiatum / lacunosum / moleculare (SR / SL / SM), e subiculum. Este protocolo foi aplicado a cinco sujeitos (3F, 2M; 29-57 anos, Média 37.). Protocolo confiabilidade é avaliada por resegmenting direito ou hipocampo esquerdo de cada sujeito e de informática a sobreposição usando métrica kappa da Dice. Kappa de Dados (intervalo) significa entre os cinco temas são: toda hipocampo, 0,91 (0,90-0,92); CA1, 0,78 (0,77-0,79); CA2 / CA3, 0,64 (0,56-0,73); CA4 / giro denteado, 0,83 (0,81-0,85); estratos radiatum / lacunosum / moleculare, 0,71 (0,68-0,73); subiculum e 0,75 (0,72-0,78). O protocolo de segmentação aqui apresentado fornece outros laboratórios com um método confiável para estudar os hipocampo e subcampos do hipocampo in vivo utilizando ferramentas de RM comumente disponíveis.

. Os resultados do teste de fiabilidade protocolo estão resumidas na Tabela 2 para todo o hipocampo bilateral, significa sobreposição espacial como medido por kappa de dados é 0,91 e varia de 0,90 - 0,92. Os valores de kappa subcampo variar de 0,64 (CA2 / CA3) e 0,83 (CA4 / giro dentado). Os volumes médios para todos os sub-campos do hipocampo inteiro e são apresentados na Tabela 3. Os volumes para toda a gama a partir de hipocampo 2456.72-3325.02 mm 3....

Watch this Scientific Journal Video about High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging at JoVE.com

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

Alta resolução<em&gt; In Vivo</em&gt; Manual Protocolo Segmentação para hipocampo humano subcampos Utilizando Ressonância Magnética 3T

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

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High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

9.2K visualizações.  University of Toronto. The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum. 

Vídeo: High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral line-widths and allows high-resolution spectra to be obtained from extracts, intact cells, cell cultures, and more importantly intact tissue to investigate relationships between metabolites and cellular processes. In vivo HRMAS 1H-MRS studies have yet to be reported in the live fruit fly Drosophila melanogaster. Drosophila, as a simpler genetic organism, allows the multiple biological functions and various evolutionarily conserved signaling pathways to be examined at the whole organism level and it is a useful model for investigating genetics and physiology. To this end, we developed and implemented an in vivo HRMAS 1H-MRS method to investigate live Drosophila at 14.1 T. Here, we outline an HRMAS 1H-MRS protocol for the molecular characterization of Drosophila with a conventional MR spectrometer equipped with an HRMAS probe. This technique is a novel, in vivo, non-destructive Drosophila metabolite measurement approach, which enables the identification of disease biomarkers and thus may contribute to novel therapeutic development.

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

This technique enables the use of high-resolution magic angle spinning proton MR spectroscopy (HRMAS 1H-MRS) for molecular characterization of live Drosophila melanogaster with a conventional 14.1 tesla spectrometer equipped with an HRMAS probe.

Espectroscopia de Ressonância Magnética de viver Drosophila melanogaster Usando Angle Spinning Magia

High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral ...

Neurociência

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and evaluating mouse models of human disease. MRI is an excellent modality for investigating genetically altered animals. It is capable of whole brain coverage, can be used in vivo, and provides multiple contrast mechanisms for investigating different aspects of neuranatomy and physiology. The advent of high-field scanners along with the ability to scan multiple mice simultaneously allows for rapid phenotyping of novel mutations.
Effective mouse MRI studies require attention to many aspects of experiment design. In this article, we will describe general methods to acquire quality images for mouse phenotyping using a system that images mice concurrently in shielded transmit/receive radio frequency (RF) coils in a common magnet (Bock et al., 2003). We focus particularly on anatomical phenotyping, an important and accessible application that has shown a high potential for impact in many mouse models at our imaging centre. Before we can provide the detailed steps to acquire such images, there are important practical considerations for both in vivo brain imaging (Dazai et al., 2004) and ex vivo brain imaging (Spring et al., 2007) that should be noted. These are discussed below.

Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.

Múltiplas mouse-Imagem por Ressonância Magnética neuroanatômicos

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and ...

A Rapid Approach to High-Resolution Fluorescence Imaging in Semi-Thick Brain Slices

A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are characteristic to neurons in both normal and diseased brain. Towards this, a great deal of effort has been placed on building high-resolution neuroanatomical maps1-3. With the expansion of molecular genetics and advances in light microscopy has come the ability to query not only neuronal morphologies, but also the molecular and cellular makeup of individual neurons and their associated networks4. Major advances in the ability to mark and manipulate neurons through transgenic and gene targeting technologies in the rodent now allow investigators to 'program' neuronal subsets at will5-6. Arguably, one of the most influential contributions to contemporary neuroscience has been the discovery and cloning of genes encoding fluorescent proteins (FPs) in marine invertebrates7-8, alongside their subsequent engineering to yield an ever-expanding toolbox of vital reporters9. Exploiting cell type-specific promoter activity to drive targeted FP expression in discrete neuronal populations now affords neuroanatomical investigation with genetic precision.
Engineering FP expression in neurons has vastly improved our understanding of brain structure and function. However, imaging individual neurons and their associated networks in deep brain tissues, or in three dimensions, has remained a challenge. Due to high lipid content, nervous tissue is rather opaque and exhibits auto fluorescence. These inherent biophysical properties make it difficult to visualize and image fluorescently labelled neurons at high resolution using standard epifluorescent or confocal microscopy beyond depths of tens of microns. To circumvent this challenge investigators often employ serial thin-section imaging and reconstruction methods10, or 2-photon laser scanning microscopy11. Current drawbacks to these approaches are the associated labor-intensive tissue preparation, or cost-prohibitive instrumentation respectively.
Here, we present a relatively rapid and simple method to visualize fluorescently labelled cells in fixed semi-thick mouse brain slices by optical clearing and imaging. In the attached protocol we describe the methods of: 1) fixing brain tissue in situ via intracardial perfusion, 2) dissection and removal of whole brain, 3) stationary brain embedding in agarose, 4) precision semi-thick slice preparation using new vibratome instrumentation, 5) clearing brain tissue through a glycerol gradient, and 6) mounting on glass slides for light microscopy and z-stack reconstruction (Figure 1).
For preparing brain slices we implemented a relatively new piece of instrumentation called the 'Compresstome' VF-200 (http://www.precisionary.com/products_vf200.html). This instrument is a semi-automated microtome equipped with a motorized advance and blade vibration system with features similar in function to other vibratomes. Unlike other vibratomes, the tissue to be sliced is mounted in an agarose plug within a stainless steel cylinder. The tissue is extruded at desired thicknesses from the cylinder, and cut by the forward advancing vibrating blade. The agarose plug/cylinder system allows for reproducible tissue mounting, alignment, and precision cutting. In our hands, the 'Compresstome' yields high quality tissue slices for electrophysiology, immunohistochemistry, and direct fixed-tissue mounting and imaging. Combined with optical clearing, here we demonstrate the preparation of semi-thick fixed brain slices for high-resolution fluorescent imaging.

Here we describe a rapid and simple method to image fluorescently labeled cells in semi-thick brain slices. By fixing, slicing, and optically clearing brain tissue we describe how standard epifluorescent or confocal imaging can be used to visualize individual cells and neuronal networks within intact nervous tissue.

Uma abordagem rápida para alta resolução de imagem de fluorescência em fatias grossas Semi-Cérebro

A fundamental goal to both basic and clinical neuroscience is to better understand the identities, molecular makeup, and patterns of connectivity that are ...

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

De alta resolução funcionais Métodos ressonância magnética para Midbrain Humanos

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, hippocampus, and the associated parahippocampal regions (perirhinal, entorhinal,&#160;and parahippocampal proper). Unlike most other tracing protocols available, typically focusing on certain MTL areas (e.g., amygdala and/or hippocampus), the integrative perspective adopted by the present tracing guidelines allows for clear localization of all MTL subregions. By integrating information from a variety of sources, including extant tracing protocols separately targeting various MTL structures, histological reports, and brain atlases, and with the complement of illustrative visual materials, the present protocol provides an accurate, intuitive, and convenient guide for understanding the MTL anatomy. The need for such tracing guidelines is also emphasized by illustrating possible differences between automatic and manual segmentation protocols. This knowledge can be applied toward research involving not only structural MRI investigations but also structural-functional colocalization and fMRI signal extraction from anatomically defined ROIs, in healthy and clinical groups alike.

The present work provides a comprehensive set of guidelines for manually tracing the medial temporal lobe (MTL) structures. This protocol can be applied to research involving structural and/or combined structural-functional magnetic resonance imaging (MRI) investigations of the MTL, in both healthy and clinical groups.

Um protocolo abrangente para segmentação manual das estruturas do lobo temporal medial

The present paper describes a comprehensive protocol for manual tracing of the set of brain regions comprising the medial temporal lobe (MTL): amygdala, ...

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.

The neurochemistry of mammalian brain is changed in many neurological and systemic diseases. Characteristic profiles of cerebral metabolites can be efficiently obtained based on crude extracts of brain tissue. To this end, high-resolution NMR spectroscopy is employed, enabling detailed quantitative analysis of metabolite concentrations (metabolomics).

Metabolômica Análise do cérebro de um rato de alta resolução Espectroscopia de Ressonância Magnética Nuclear de Extratos de Tecidos

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics ...

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of structural MRI in vivo is ultimately limited by physiological noise, including pulsation, respiration and head movement. Although imaging hardware continues to improve, it is still difficult to resolve structures on the millimeter scale. For example, the primary visual sensory pathways synapse at the lateral geniculate nucleus (LGN), a visual relay and control nucleus in the thalamus that normally is organized into six interleaved monocular layers. Neuroimaging studies have not been able to reliably distinguish these layers due their small size that are less than 1 mm thick.
The resolving limit of structural MRI, in a postmortem brain was tested using multiple images averaged over a long duration (~24 h). The purpose was to test whether it was possible to resolve the individual layers of the LGN in the absence of physiological noise. A proton density (PD)1 weighted pulse sequence was used with varying resolution and other parameters to determine the minimum number of images necessary to be registered and averaged to reliably distinguish the LGN and other subcortical regions. The results were also compared to images acquired in living human brains. In vivo subjects were scanned in order to determine the additional effects of physiological noise on the minimum number of PD scans needed to differentiate subcortical structures, useful in clinical applications.

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

Here we present a protocol to determine the minimum number images that needed to be registered and averaged to resolve subcortical structures and test whether the individual layers of the LGN could be resolved in the absence of physiological noise.

De alta resolução de ressonância magnética estrutural do Subcórtex Humano<I&gt; In Vivo</I&gt; E Postmortem

The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of ...

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

Um protocolo de ressonância magnética para estimativa de tempo de início curso em situações de isquemia Cerebral permanente

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

A Standardized Pipeline for Examining Human Cerebellar Grey Matter Morphometry using Structural Magnetic Resonance Imaging

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond its historical association with motor control. Structural and functional neuroimaging studies have further refined understanding of the functional neuroanatomy of the cerebellum beyond its anatomical divisions, highlighting the need for the examination of individual cerebellar subunits in healthy variability and neurological diseases. This paper presents a standardized pipeline for examining cerebellum grey matter morphometry that combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation (Automatic Cerebellum Anatomical Parcellation using U-Net Locally Constrained Optimization; ACAPULCO) and voxel-based registration of the cerebellum (Spatially Unbiased Infra-tentorial Template; SUIT) for volumetric quantification. 
The pipeline has broad applicability to a range of neurological diseases and is fully automated, with manual intervention only required for quality control of the outputs. The pipeline is freely available, with substantial accompanying documentation, and can be run on Mac, Windows, and Linux operating systems. The pipeline is applied in a cohort of individuals with Friedreich ataxia (FRDA), and representative results, as well as recommendations on group-level inferential statistical analyses, are provided. This pipeline could facilitate reliability and reproducibility across the field, ultimately providing a powerful methodological approach for characterizing and tracking cerebellar structural changes in neurological diseases.

A standardized pipeline is presented for examining cerebellum grey matter morphometry. The pipeline combines high-resolution, state-of-the-art approaches for optimized and automated cerebellum parcellation and voxel-based registration of the cerebellum for volumetric quantification.

Um pipeline padronizado para examinar morfometria de matéria cinzenta cerebelar humana usando ressonância magnética estrutural

Multiple lines of research provide compelling evidence for a role of the cerebellum in a wide array of cognitive and affective functions, going far beyond ...

Uma configuração para visão humana, cognição e interação em cenários da vida real

A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies

Perception of others&#39; actions is crucial for survival, interaction, and communication. Despite decades of cognitive neuroscience research dedicated to understanding the perception of actions, we are still far away from developing a neurally inspired computer vision system that approaches human action perception. A major challenge is that actions in the real world consist of temporally unfolding events in space that happen &#34;here and now&#34; and are actable. In contrast, visual perception and cognitive neuroscience research to date have largely studied action perception through 2D displays (e.g., images or videos) that lack the presence of actors in space and time, hence these displays are limited in affording actability. Despite the growing body of knowledge in the field, these challenges must be overcome for a better understanding of the fundamental mechanisms of the perception of others&#39; actions in the real world. The aim of this study is to introduce a novel setup to conduct naturalistic laboratory experiments with live actors in scenarios that approximate real-world settings. The core element of the setup used in this study is a transparent organic light-emitting diode (OLED) screen through which participants can watch the live actions of a physically present actor while the timing of their presentation is precisely controlled. In this work, this setup was tested in a behavioral experiment. We believe that the setup will help researchers reveal fundamental and previously inaccessible cognitive and neural mechanisms of action perception and will be a foundation for future studies investigating social perception and cognition in naturalistic settings.

Autor em destaque: uma nova configuração para conduzir experimentos de laboratório naturalistas com atores humanos reais em cenários 

This study presents a naturalistic experimental setup that allows researchers to present real-time action stimuli, obtain response time and mouse tracking data while participants respond after each stimulus display, and change actors between experimental conditions with a unique system including a special transparent organic light-emitting diode (OLED) screen and light manipulation.

Uma configuração naturalista para apresentar pessoas reais e ações vivas em estudos de psicologia experimental e neurociência cognitiva

Perception of others&#39; actions is crucial for survival, interaction, and communication. Despite decades of cognitive neuroscience research dedicated to ...